diff --git a/.github/workflows/deploy.yml b/.github/workflows/deploy.yml index e07c55c80d..227192e93e 100644 --- a/.github/workflows/deploy.yml +++ b/.github/workflows/deploy.yml @@ -81,6 +81,18 @@ jobs: - name: Install Netlify CLI run: npm install netlify-cli -g + - name: Set branch context URL + continue-on-error: true + run: | + # Extract branch name from ref (removes refs/heads/ prefix) + if [[ ! -z "${{ inputs.netlify-alias }}" ]]; then + netlify env:set NEXTAUTH_URL ${{ inputs.site-url || vars.SITE_URL }} --context "branch:${{ inputs.netlify-alias }}" + fi + env: + NETLIFY_AUTH_TOKEN: ${{ secrets.NETLIFY_AUTH_TOKEN }} + NETLIFY_SITE_ID: ${{ vars.NETLIFY_SITE_ID }} + GITHUB_HEAD_REF: ${{ github.head_ref }} + - name: Build using Netlify run: netlify build --context ${{ inputs.netlify-context }} --offline working-directory: site/gatsby-site @@ -88,8 +100,6 @@ jobs: NETLIFY_SITE_ID: ${{ vars.NETLIFY_SITE_ID }} NETLIFY_AUTH_TOKEN: ${{ secrets.NETLIFY_AUTH_TOKEN }} GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} - CYPRESS_PROJECT_ID: ${{ vars.CYPRESS_PROJECT_ID }} - CYPRESS_RECORD_KEY: ${{ secrets.CYPRESS_RECORD_KEY }} E2E_ADMIN_USERNAME: ${{ secrets.E2E_ADMIN_USERNAME }} E2E_ADMIN_PASSWORD: ${{ secrets.E2E_ADMIN_PASSWORD }} ALGOLIA_ADMIN_KEY: ${{ secrets.ALGOLIA_ADMIN_KEY }} @@ -97,11 +107,9 @@ jobs: GATSBY_ALGOLIA_SEARCH_KEY: ${{ vars.GATSBY_ALGOLIA_SEARCH_KEY }} GATSBY_AVAILABLE_LANGUAGES: ${{ vars.GATSBY_AVAILABLE_LANGUAGES }} GATSBY_REALM_APP_ID: ${{ vars.GATSBY_REALM_APP_ID }} - GOOGLE_TRANSLATE_API_KEY: ${{ secrets.GOOGLE_TRANSLATE_API_KEY }} MONGODB_CONNECTION_STRING: ${{ secrets.MONGODB_CONNECTION_STRING }} MONGODB_REPLICA_SET: ${{ secrets.MONGODB_REPLICA_SET }} MONGODB_TRANSLATIONS_CONNECTION_STRING: ${{ secrets.MONGODB_TRANSLATIONS_CONNECTION_STRING }} - TRANSLATE_SUBMISSION_DATE_START: ${{ vars.TRANSLATE_SUBMISSION_DATE_START }} MONGODB_MIGRATIONS_CONNECTION_STRING: ${{ secrets.MONGODB_MIGRATIONS_CONNECTION_STRING }} GATSBY_REALM_APP_GRAPHQL_URL: ${{ secrets.GATSBY_REALM_APP_GRAPHQL_URL }} GATSBY_PRISMIC_REPO_NAME: ${{ vars.GATSBY_PRISMIC_REPO_NAME }} @@ -116,7 +124,6 @@ jobs: CLOUDFLARE_R2_SECRET_ACCESS_KEY: ${{ secrets.CLOUDFLARE_R2_SECRET_ACCESS_KEY }} REALM_GRAPHQL_API_KEY: ${{ secrets.REALM_GRAPHQL_API_KEY }} GATSBY_COMMIT_SHA: ${{ inputs.sha }} - TRANSLATE_DRY_RUN: ${{ vars.TRANSLATE_DRY_RUN }} REALM_API_APP_ID: ${{ vars.REALM_API_APP_ID }} REALM_API_GROUP_ID: ${{ vars.REALM_API_GROUP_ID }} REALM_APP_ID: ${{ vars.GATSBY_REALM_APP_ID }} @@ -125,7 +132,8 @@ jobs: ROLLBAR_POST_SERVER_ITEM_ACCESS_TOKEN: ${{ secrets.GATSBY_ROLLBAR_TOKEN }} API_MONGODB_CONNECTION_STRING: ${{ secrets.API_MONGODB_CONNECTION_STRING }} SITE_URL: ${{ inputs.site-url || vars.SITE_URL }} - PROCESS_NOTIFICATIONS_SECRET: ${{ secrets.PROCESS_NOTIFICATIONS_SECRET }} + NEXTAUTH_URL: ${{ inputs.site-url || vars.SITE_URL }} + NEXTAUTH_SECRET: ${{ secrets.NEXTAUTH_SECRET }} - name: Build size run: | diff --git a/.github/workflows/preview.yml b/.github/workflows/preview.yml index e251115442..b96c27402a 100644 --- a/.github/workflows/preview.yml +++ b/.github/workflows/preview.yml @@ -50,16 +50,6 @@ jobs: netlify-context: branch-deploy runner-label: ${{ vars.PREVIEW_RUNNER_LABEL }} - call-test: - if: ${{ !failure() && !cancelled() }} - uses: ./.github/workflows/test.yml - needs: call-test-build - secrets: inherit - with: - sha: ${{ github.event.pull_request.head.sha }} - environment: staging - runner-label: ${{ vars.PREVIEW_RUNNER_LABEL }} - call-test-playwright: if: ${{ !failure() && !cancelled() }} uses: ./.github/workflows/test-playwright.yml diff --git a/.github/workflows/process-notifications.yml b/.github/workflows/process-notifications.yml index 2c4509059b..803907d686 100644 --- a/.github/workflows/process-notifications.yml +++ b/.github/workflows/process-notifications.yml @@ -1,41 +1,50 @@ name: Process Notifications - on: workflow_call: inputs: - environment: - description: The Github environment to load secrets from - type: string - required: true + environment: + description: The Github environment to load secrets from + type: string + required: true + force-deploy: + description: Skip processing notifications if force deploy is true + type: boolean + required: false + default: false jobs: - execute-mutation: + execute-process: + if: ${{ inputs.force-deploy != true }} runs-on: ubuntu-latest environment: ${{ inputs.environment }} steps: - - name: Call GraphQL API - env: - SITE_URL: ${{ vars.SITE_URL }} - PROCESS_NOTIFICATIONS_SECRET: ${{ secrets.PROCESS_NOTIFICATIONS_SECRET }} - run: | - RESPONSE=$(curl -s -o response.json -w "%{http_code}" -X POST "$SITE_URL/api/graphql" \ - -H "Content-Type: application/json" \ - -H "PROCESS_NOTIFICATIONS_SECRET: $PROCESS_NOTIFICATIONS_SECRET" \ - -d '{"query":"mutation { processNotifications }"}') + - name: Checkout code + uses: actions/checkout@v4 - HTTP_STATUS=$RESPONSE + - name: Setup Node.js + uses: actions/setup-node@v4 + with: + node-version: "20" + cache: "npm" + cache-dependency-path: site/gatsby-site/package-lock.json - if [ "$HTTP_STATUS" -ne 200 ]; then - echo "GraphQL mutation failed with HTTP status: $HTTP_STATUS" - cat response.json - exit 1 - fi + - name: Install dependencies + working-directory: site/gatsby-site + run: npm ci - if jq -e 'has("errors") or has("errorType")' response.json > /dev/null; then - echo "GraphQL mutation failed with the following response:" - jq '.' response.json - exit 1 - fi - - echo "GraphQL mutation succeeded!" - jq '.' response.json + - name: Process Notifications + working-directory: site/gatsby-site + env: + REALM_API_APP_ID: ${{ vars.REALM_API_APP_ID }} + REALM_API_GROUP_ID: ${{ vars.REALM_API_GROUP_ID }} + REALM_API_PRIVATE_KEY: ${{ secrets.REALM_API_PRIVATE_KEY }} + REALM_API_PUBLIC_KEY: ${{ secrets.REALM_API_PUBLIC_KEY }} + REALM_GRAPHQL_API_KEY: ${{ secrets.REALM_GRAPHQL_API_KEY }} + REALM_APP_ID: ${{ vars.REALM_APP_ID }} + API_MONGODB_CONNECTION_STRING: ${{ secrets.API_MONGODB_CONNECTION_STRING }} + ROLLBAR_POST_SERVER_ITEM_ACCESS_TOKEN: ${{ secrets.ROLLBAR_POST_SERVER_ITEM_ACCESS_TOKEN }} + MAILERSEND_API_KEY: ${{ secrets.MAILERSEND_API_KEY }} + NOTIFICATIONS_SENDER_NAME: ${{ vars.NOTIFICATIONS_SENDER_NAME }} + NOTIFICATIONS_SENDER: ${{ vars.NOTIFICATIONS_SENDER }} + SITE_URL: ${{ vars.SITE_URL }} + run: npm run process-notifications:ci diff --git a/.github/workflows/production.yml b/.github/workflows/production.yml index ea2fde4cb5..2646848d1e 100644 --- a/.github/workflows/production.yml +++ b/.github/workflows/production.yml @@ -52,16 +52,6 @@ jobs: runner-label: ${{ vars.PRODUCTION_RUNNER_LABEL }} cache-modifier: ${{ needs.cache-modifier.outputs.cache-modifier }} - call-test: - uses: ./.github/workflows/test.yml - needs: [cache-modifier, call-test-build] - secrets: inherit - with: - sha: ${{ github.sha }} - environment: production - runner-label: ${{ vars.PRODUCTION_RUNNER_LABEL }} - cache-modifier: ${{ needs.cache-modifier.outputs.cache-modifier }} - call-test-playwright: uses: ./.github/workflows/test-playwright.yml needs: call-test-build @@ -84,7 +74,13 @@ jobs: call-deploy: if: (inputs.force-deploy == true || success()) && !cancelled() uses: ./.github/workflows/deploy.yml - needs: [cache-modifier, call-test, call-test-api, call-test-playwright, call-test-playwright-full] + needs: + [ + cache-modifier, + call-test-api, + call-test-playwright, + call-test-playwright-full, + ] secrets: inherit permissions: pull-requests: write @@ -95,10 +91,12 @@ jobs: netlify-alias: runner-label: ${{ vars.PRODUCTION_RUNNER_LABEL }} cache-modifier: ${{ needs.cache-modifier.outputs.cache-modifier }} - + call-process-notifications: needs: call-deploy uses: ./.github/workflows/process-notifications.yml secrets: inherit with: - environment: production \ No newline at end of file + environment: production + # inputs from the workflow_dispatch event come as strings even if they are explicitly defined as boolean + force-deploy: ${{ inputs.force-deploy == 'true' }} diff --git a/.github/workflows/realm-empty-env.yml b/.github/workflows/realm-empty-env.yml deleted file mode 100644 index 9150fee5a0..0000000000 --- a/.github/workflows/realm-empty-env.yml +++ /dev/null @@ -1,25 +0,0 @@ -name: Realm Deploy to Empty Environment - -on: - push: - branches: - - staging - workflow_dispatch: - -jobs: - push: - runs-on: ubuntu-latest - environment: empty - steps: - - uses: actions/checkout@v2 - - name: "Install the Realm CLI" - run: | - npm install -g mongodb-realm-cli - - name: Login - run: | - realm-cli login --api-key="${{ secrets.REALM_API_PUBLIC_KEY }}" --private-api-key="${{ secrets.REALM_API_PRIVATE_KEY }}" --realm-url https://services.cloud.mongodb.com --atlas-url https://cloud.mongodb.com - - name: Push - run: | - cd site/realm - realm-cli push --remote="${{ secrets.GATSBY_REALM_APP_ID }}" -y - diff --git a/.github/workflows/staging.yml b/.github/workflows/staging.yml index 0f2b422cf7..42b4d6a5a8 100644 --- a/.github/workflows/staging.yml +++ b/.github/workflows/staging.yml @@ -60,16 +60,6 @@ jobs: environment: staging runner-label: ${{ vars.STAGING_RUNNER_LABEL }} - call-test: - uses: ./.github/workflows/test.yml - needs: [cache-modifier, call-test-build] - secrets: inherit - with: - sha: ${{ github.sha }} - environment: staging - runner-label: ${{ vars.STAGING_RUNNER_LABEL }} - cache-modifier: ${{ needs.cache-modifier.outputs.cache-modifier }} - call-test-playwright-full: if: ${{ !failure() && !cancelled() }} uses: ./.github/workflows/test-playwright-full.yml @@ -83,7 +73,7 @@ jobs: call-deploy: if: (inputs.force-deploy == true || success()) && !cancelled() uses: ./.github/workflows/deploy.yml - needs: [cache-modifier, call-test, call-test-playwright, call-test-api] + needs: [cache-modifier, call-test-playwright, call-test-api] secrets: inherit permissions: pull-requests: write @@ -94,10 +84,12 @@ jobs: netlify-alias: runner-label: ${{ vars.STAGING_RUNNER_LABEL }} cache-modifier: ${{ needs.cache-modifier.outputs.cache-modifier }} - + call-process-notifications: needs: call-deploy uses: ./.github/workflows/process-notifications.yml secrets: inherit with: environment: staging + # inputs from the workflow_dispatch event come as strings even if they are explicitly defined as boolean + force-deploy: ${{ inputs.force-deploy == 'true' }} diff --git a/.github/workflows/test-api.yml b/.github/workflows/test-api.yml index c21a941d4f..12df1bfa15 100644 --- a/.github/workflows/test-api.yml +++ b/.github/workflows/test-api.yml @@ -58,21 +58,20 @@ jobs: - name: Jest run run: npm run test:api:ci env: - REALM_APP_ID: "" - REALM_API_APP_ID: "" - REALM_API_GROUP_ID: "" - REALM_API_PUBLIC_KEY: "" - REALM_API_PRIVATE_KEY: "" - REALM_GRAPHQL_API_KEY: "" + REALM_APP_ID: dummy + REALM_API_APP_ID: dummy + REALM_API_GROUP_ID: dummy + REALM_API_PUBLIC_KEY: dummy + REALM_API_PRIVATE_KEY: dummy + REALM_GRAPHQL_API_KEY: dummy API_MONGODB_CONNECTION_STRING: "" # dinamically set by globalSetup.ts - E2E_ADMIN_USERNAME: "" - E2E_ADMIN_PASSWORD: "" ROLLBAR_POST_SERVER_ITEM_ACCESS_TOKEN: sarasa MAILERSEND_API_KEY: something NOTIFICATIONS_SENDER_NAME: Test Preview NOTIFICATIONS_SENDER: test@test.com - PROCESS_NOTIFICATIONS_SECRET: sarasa SITE_URL: http://localhost:8000 + NEXTAUTH_URL: http://localhost:8000 + NEXTAUTH_SECRET: dummy - name: Upload coverage reports to Codecov uses: codecov/codecov-action@v4 diff --git a/.github/workflows/test-build.yml b/.github/workflows/test-build.yml index 5b0332b868..a59585c4e8 100644 --- a/.github/workflows/test-build.yml +++ b/.github/workflows/test-build.yml @@ -54,16 +54,12 @@ jobs: with: path: | site/gatsby-site/node_modules - ~/.cache/Cypress key: ${{ runner.os }}-build-${{ env.cache-name }}-${{ hashFiles('**/package-lock.json') }}-${{ inputs.cache-modifier}} - name: Install NPM dependencies if: steps.cache-nodemodules.outputs.cache-hit != 'true' - uses: cypress-io/github-action@v6 - with: - working-directory: site/gatsby-site - runTests: false - install-command: npm ci + working-directory: site/gatsby-site + run: npm ci - name: Use tests specific netlify.toml run: mv tests-netlify.toml netlify.toml @@ -80,8 +76,6 @@ jobs: NETLIFY_SITE_ID: ${{ vars.NETLIFY_SITE_ID }} NETLIFY_AUTH_TOKEN: ${{ secrets.NETLIFY_AUTH_TOKEN }} GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} - CYPRESS_PROJECT_ID: ${{ vars.CYPRESS_PROJECT_ID }} - CYPRESS_RECORD_KEY: ${{ secrets.CYPRESS_RECORD_KEY }} E2E_ADMIN_USERNAME: ${{ secrets.E2E_ADMIN_USERNAME }} E2E_ADMIN_PASSWORD: ${{ secrets.E2E_ADMIN_PASSWORD }} ALGOLIA_ADMIN_KEY: ${{ secrets.ALGOLIA_ADMIN_KEY }} @@ -89,12 +83,10 @@ jobs: GATSBY_ALGOLIA_SEARCH_KEY: ${{ vars.GATSBY_ALGOLIA_SEARCH_KEY }} GATSBY_AVAILABLE_LANGUAGES: ${{ vars.GATSBY_AVAILABLE_LANGUAGES }} GATSBY_REALM_APP_ID: ${{ vars.GATSBY_REALM_APP_ID }} - GOOGLE_TRANSLATE_API_KEY: ${{ secrets.GOOGLE_TRANSLATE_API_KEY }} MONGODB_CONNECTION_STRING: ${{ secrets.MONGODB_CONNECTION_STRING }} MONGODB_REPLICA_SET: ${{ secrets.MONGODB_REPLICA_SET }} MONGODB_TRANSLATIONS_CONNECTION_STRING: ${{ secrets.MONGODB_TRANSLATIONS_CONNECTION_STRING }} MONGODB_MIGRATIONS_CONNECTION_STRING: ${{ secrets.MONGODB_MIGRATIONS_CONNECTION_STRING }} - TRANSLATE_SUBMISSION_DATE_START: ${{ vars.TRANSLATE_SUBMISSION_DATE_START }} GATSBY_REALM_APP_GRAPHQL_URL: ${{ secrets.GATSBY_REALM_APP_GRAPHQL_URL }} GATSBY_PRISMIC_REPO_NAME: ${{ vars.GATSBY_PRISMIC_REPO_NAME }} PRISMIC_ACCESS_TOKEN: ${{ secrets.PRISMIC_ACCESS_TOKEN }} @@ -115,7 +107,8 @@ jobs: REALM_API_PRIVATE_KEY: ${{ secrets.REALM_API_PRIVATE_KEY }} ROLLBAR_POST_SERVER_ITEM_ACCESS_TOKEN: ${{ secrets.GATSBY_ROLLBAR_TOKEN }} API_MONGODB_CONNECTION_STRING: ${{ secrets.API_MONGODB_CONNECTION_STRING }} - + NEXTAUTH_URL: http://localhost:8000 + NEXTAUTH_SECRET: 678x1irXYWeiOqTwCv1awvkAUbO9eHa5xzQEYhxhMms= # only used in local tests - name: Cache build uses: actions/cache/save@v4 diff --git a/.github/workflows/test-playwright-full.yml b/.github/workflows/test-playwright-full.yml index 282cf2a5f2..36334acdbe 100644 --- a/.github/workflows/test-playwright-full.yml +++ b/.github/workflows/test-playwright-full.yml @@ -48,7 +48,6 @@ jobs: with: path: | site/gatsby-site/node_modules - ~/.cache/Cypress key: ${{ runner.os }}-build-${{ env.cache-name }}-${{ hashFiles('**/package-lock.json') }}-${{ inputs.cache-modifier }} - name: Install NPM dependencies @@ -57,7 +56,7 @@ jobs: working-directory: site/gatsby-site - name: start in memory mongodb - run: nohup node -r ts-node/register playwright/memory-mongo.ts & + run: nohup npm run start:memory-mongo:ci & working-directory: site/gatsby-site env: MONGODB_CONNECTION_STRING: mongodb://127.0.0.1:4110/ @@ -85,11 +84,9 @@ jobs: GATSBY_ALGOLIA_SEARCH_KEY: ${{ vars.GATSBY_ALGOLIA_SEARCH_KEY }} GATSBY_AVAILABLE_LANGUAGES: ${{ vars.GATSBY_AVAILABLE_LANGUAGES }} GATSBY_REALM_APP_ID: ${{ vars.GATSBY_REALM_APP_ID }} - GOOGLE_TRANSLATE_API_KEY: ${{ secrets.GOOGLE_TRANSLATE_API_KEY }} MONGODB_CONNECTION_STRING: mongodb://127.0.0.1:4110/ MONGODB_TRANSLATIONS_CONNECTION_STRING: mongodb://127.0.0.1:4110/ MONGODB_MIGRATIONS_CONNECTION_STRING: mongodb://127.0.0.1:4110/ - TRANSLATE_SUBMISSION_DATE_START: ${{ vars.TRANSLATE_SUBMISSION_DATE_START }} GATSBY_REALM_APP_GRAPHQL_URL: ${{ secrets.GATSBY_REALM_APP_GRAPHQL_URL }} GATSBY_PRISMIC_REPO_NAME: ${{ vars.GATSBY_PRISMIC_REPO_NAME }} PRISMIC_ACCESS_TOKEN: ${{ secrets.PRISMIC_ACCESS_TOKEN }} @@ -137,8 +134,9 @@ jobs: MAILERSEND_API_KEY: something NOTIFICATIONS_SENDER_NAME: Test Preview NOTIFICATIONS_SENDER: test@test.com - PROCESS_NOTIFICATIONS_SECRET: sarasa SITE_URL: http://localhost:8000 + NEXTAUTH_URL: http://localhost:8000 + NEXTAUTH_SECRET: 678x1irXYWeiOqTwCv1awvkAUbO9eHa5xzQEYhxhMms= # only used in local tests - name: Upload Playwright traces if: failure() diff --git a/.github/workflows/test-playwright.yml b/.github/workflows/test-playwright.yml index 2a54323ea6..bf21b53249 100644 --- a/.github/workflows/test-playwright.yml +++ b/.github/workflows/test-playwright.yml @@ -48,7 +48,6 @@ jobs: with: path: | site/gatsby-site/node_modules - ~/.cache/Cypress key: ${{ runner.os }}-build-${{ env.cache-name }}-${{ hashFiles('**/package-lock.json') }}-${{ inputs.cache-modifier }} - name: Install NPM dependencies @@ -92,8 +91,9 @@ jobs: MAILERSEND_API_KEY: something NOTIFICATIONS_SENDER_NAME: Test Preview NOTIFICATIONS_SENDER: test@test.com - PROCESS_NOTIFICATIONS_SECRET: sarasa SITE_URL: http://localhost:8000 + NEXTAUTH_URL: http://localhost:8000 + NEXTAUTH_SECRET: 678x1irXYWeiOqTwCv1awvkAUbO9eHa5xzQEYhxhMms= # only used in local tests - uses: actions/upload-artifact@v4 if: ${{ !cancelled() }} diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml index 6649ecd4f3..c8228851ef 100644 --- a/.github/workflows/test.yml +++ b/.github/workflows/test.yml @@ -107,11 +107,9 @@ jobs: GATSBY_ALGOLIA_SEARCH_KEY: ${{ vars.GATSBY_ALGOLIA_SEARCH_KEY }} GATSBY_AVAILABLE_LANGUAGES: ${{ vars.GATSBY_AVAILABLE_LANGUAGES }} GATSBY_REALM_APP_ID: ${{ vars.GATSBY_REALM_APP_ID }} - GOOGLE_TRANSLATE_API_KEY: ${{ secrets.GOOGLE_TRANSLATE_API_KEY }} MONGODB_CONNECTION_STRING: ${{ secrets.MONGODB_CONNECTION_STRING }} MONGODB_REPLICA_SET: ${{ secrets.MONGODB_REPLICA_SET }} MONGODB_TRANSLATIONS_CONNECTION_STRING: ${{ secrets.MONGODB_TRANSLATIONS_CONNECTION_STRING }} - TRANSLATE_SUBMISSION_DATE_START: ${{ vars.TRANSLATE_SUBMISSION_DATE_START }} MONGODB_MIGRATIONS_CONNECTION_STRING: ${{ secrets.MONGODB_MIGRATIONS_CONNECTION_STRING }} GATSBY_REALM_APP_GRAPHQL_URL: ${{ secrets.GATSBY_REALM_APP_GRAPHQL_URL }} GATSBY_ROLLBAR_TOKEN: ${{ secrets.GATSBY_ROLLBAR_TOKEN }} diff --git a/.github/workflows/translate-production.yml b/.github/workflows/translate-production.yml new file mode 100644 index 0000000000..a5d9cad247 --- /dev/null +++ b/.github/workflows/translate-production.yml @@ -0,0 +1,15 @@ +name: Translate Reports - Production + +on: + schedule: + - cron: "0 5 * * *" # Run every day at 5 AM GMT + + workflow_dispatch: + +jobs: + call-translate: + uses: ./.github/workflows/translate.yml + secrets: inherit + with: + environment: production + diff --git a/.github/workflows/translate-staging.yml b/.github/workflows/translate-staging.yml new file mode 100644 index 0000000000..4b839bc475 --- /dev/null +++ b/.github/workflows/translate-staging.yml @@ -0,0 +1,15 @@ +name: Translate Reports - Staging + +on: + schedule: + - cron: "0 5 * * *" # Run every day at 5 AM GMT + + workflow_dispatch: + +jobs: + call-translate: + uses: ./.github/workflows/translate.yml + secrets: inherit + with: + environment: staging + diff --git a/.github/workflows/translate.yml b/.github/workflows/translate.yml new file mode 100644 index 0000000000..5bfeacd82b --- /dev/null +++ b/.github/workflows/translate.yml @@ -0,0 +1,38 @@ +name: Translate Reports + +on: + workflow_call: + inputs: + environment: + description: The Github environment to load secrets from + type: string + required: true + +jobs: + translate: + environment: ${{ inputs.environment }} + runs-on: ubuntu-latest + + steps: + - name: Checkout repository + uses: actions/checkout@v4 + + - name: Setup Node.js + uses: actions/setup-node@v4 + with: + node-version: lts/* + + - name: Install NPM dependencies + run: npm ci + working-directory: site/gatsby-site + + - name: Run translation script + run: npm run translate-reports + working-directory: site/gatsby-site + env: + MONGODB_TRANSLATIONS_CONNECTION_STRING: ${{ secrets.MONGODB_TRANSLATIONS_CONNECTION_STRING }} + GOOGLE_TRANSLATE_API_KEY: ${{ secrets.GOOGLE_TRANSLATE_API_KEY }} + GATSBY_AVAILABLE_LANGUAGES: ${{ vars.GATSBY_AVAILABLE_LANGUAGES }} + TRANSLATE_SUBMISSION_DATE_START: ${{ vars.TRANSLATE_SUBMISSION_DATE_START }} + TRANSLATE_DRY_RUN: ${{ vars.TRANSLATE_DRY_RUN }} + diff --git a/README.md b/README.md index 74a0a22fc2..10b06390f9 100644 --- a/README.md +++ b/README.md @@ -17,6 +17,10 @@     + +   + +     diff --git a/site/gatsby-site/.eslintrc.json b/site/gatsby-site/.eslintrc.json index 3774112a0b..8b80efee28 100755 --- a/site/gatsby-site/.eslintrc.json +++ b/site/gatsby-site/.eslintrc.json @@ -7,7 +7,7 @@ "prettier", "prettier/react" ], - "plugins": ["react", "import", "jsx-a11y", "cypress"], + "plugins": ["react", "import", "jsx-a11y"], "settings": { "react": { "version": "detect" @@ -60,8 +60,7 @@ "env": { "es6": true, "browser": true, - "node": true, - "cypress/globals": true + "node": true }, "globals": { "graphql": false, diff --git a/site/gatsby-site/README.md b/site/gatsby-site/README.md index b56df875a4..8c73bd7cfe 100644 --- a/site/gatsby-site/README.md +++ b/site/gatsby-site/README.md @@ -47,9 +47,9 @@ Once you have cloned the repository, to set up a local development environment f CLOUDFLARE_R2_SECRET_ACCESS_KEY= # Secret access key for Cloudflare R2 storage CLOUDFLARE_R2_BUCKET_NAME= # Name of the Cloudflare R2 bucket for storage GATSBY_CLOUDFLARE_R2_PUBLIC_BUCKET_URL= # Public URL for accessing the Cloudflare R2 bucket from the Gatsby app - MAILERSEND_API_KEY= # API key for MailerSend email service or dummy value if you don't plan to send emails - NOTIFICATIONS_SENDER_NAME=AIID Notifications # Name of the sender for email notifications - NOTIFICATIONS_SENDER=notifications@incidentdatabase.ai # Email address of the sender for email notifications + MAILERSEND_API_KEY= # API key for MailerSend email service + NOTIFICATIONS_SENDER_NAME= # Name of the sender for email notifications + NOTIFICATIONS_SENDER= # Email address of the sender for email notifications SITE_URL=http://localhost:8000 ``` @@ -150,6 +150,7 @@ npm run codegen ### Schema and API Stitching > [!IMPORTANT] + The API previously relied on the MongoDB Atlas Realm GraphQL API to fetch data, [but now that it has been deprecated](https://www.mongodb.com/developer/products/atlas/deprecating-mongodb-atlas-graphql-hosting-services/), we have migrated to a new API that we implement in this codebase. During the migration from Realm to our API, we stitch two GraphQL schemas: the auto-generated one from Atlas and the one defined in this codebase. These schemas can be found in the `remote.ts` and `local.ts` files respectively. The migration process involves progressively transferring GraphQL fields and functionality from the remote schema to the local one. The final combined schema is found in `schema.ts`. ### Email notifications diff --git a/site/gatsby-site/blog/join-raic/index.fr.mdx b/site/gatsby-site/blog/join-raic/index.fr.mdx index 7a419b07fc..94466db376 100644 --- a/site/gatsby-site/blog/join-raic/index.fr.mdx +++ b/site/gatsby-site/blog/join-raic/index.fr.mdx @@ -1,5 +1,5 @@ --- -titre: "Rejoignez l'équipe fondatrice de Responsible AI Collaborative" +title: "Rejoignez l'équipe fondatrice de Responsible AI Collaborative" metaTitle: "Rejoignez l'équipe fondatrice de Responsible AI Collaborative" metaDescription: '' date: '2022-03-29' diff --git a/site/gatsby-site/cypress.config.js b/site/gatsby-site/cypress.config.js deleted file mode 100644 index 7c0472bda6..0000000000 --- a/site/gatsby-site/cypress.config.js +++ /dev/null @@ -1,29 +0,0 @@ -const { defineConfig } = require('cypress'); - -module.exports = defineConfig({ - video: false, - chromeWebSecurity: false, - screenshotOnRunFailure: false, - defaultCommandTimeout: 12000, - requestTimeout: 30000, - responseTimeout: 30000, - retries: { - runMode: 2, - openMode: 0, - }, - e2e: { - experimentalRunAllSpecs: true, - // We've imported your old cypress plugins here. - // You may want to clean this up later by importing these. - setupNodeEvents(on, config) { - if (process.env.INSTRUMENT) { - require('@cypress/code-coverage/task')(on, config); - } - - require('./cypress/plugins/index.js')(on, config); - - return config; - }, - baseUrl: 'http://localhost:8000/', - }, -}); diff --git a/site/gatsby-site/cypress/e2e/integration/apps/newsdigest.cy.js b/site/gatsby-site/cypress/e2e/integration/apps/newsdigest.cy.js deleted file mode 100644 index 78c4ecf18a..0000000000 --- a/site/gatsby-site/cypress/e2e/integration/apps/newsdigest.cy.js +++ /dev/null @@ -1,110 +0,0 @@ -import { format } from 'date-fns'; -import { conditionalIt } from '../../../support/utils'; -import newsArticles from '../../../fixtures/candidates/newsArticles.json'; - -describe('News Digest', () => { - const url = '/apps/newsdigest'; - - it('Successfully loads', () => { - cy.visit(url); - }); - - it('Should load candidate cards', () => { - newsArticles.data.candidates[0].date_published = format(new Date(), 'yyyy-MM-dd'); - newsArticles.data.candidates[1].date_published = format(new Date(), 'yyyy-MM-dd'); - - cy.conditionalIntercept( - '**/graphql', - (req) => req.body.operationName == 'NewsArticles', - 'NewsArticles', - newsArticles - ); - - cy.visit(url); - cy.get('[data-cy="candidate-card"]', { timeout: 15000 }).should('exist'); - }); - - it('Should open submit form on pressing submit', () => { - newsArticles.data.candidates[0].date_published = format(new Date(), 'yyyy-MM-dd'); - newsArticles.data.candidates[1].date_published = format(new Date(), 'yyyy-MM-dd'); - - cy.conditionalIntercept( - '**/graphql', - (req) => req.body.operationName == 'NewsArticles', - 'NewsArticles', - newsArticles - ); - - cy.visit(url, { - onBeforeLoad(window) { - cy.stub(window, 'open', (url) => { - expect(url.slice(0, 12)).to.equal('/apps/submit'); - }); - }, - }); - cy.get('[data-cy="candidate-dropdown"] button').first().click(); - cy.get('[data-cy="submit-icon"]', { timeout: 15000 }).first().parent().click(); - cy.window().its('open').should('be.called'); - }); - - conditionalIt( - !Cypress.env('isEmptyEnvironment') && Cypress.env('e2eUsername') && Cypress.env('e2ePassword'), - 'Should dismiss and restore items', - () => { - cy.login(Cypress.env('e2eUsername'), Cypress.env('e2ePassword')); - - newsArticles.data.candidates[0].date_published = format(new Date(), 'yyyy-MM-dd'); - newsArticles.data.candidates[1].date_published = format(new Date(), 'yyyy-MM-dd'); - - cy.conditionalIntercept( - '**/graphql', - (req) => req.body.operationName == 'NewsArticles', - 'NewsArticles', - newsArticles - ); - - cy.conditionalIntercept( - '**/graphql', - (req) => req.body.operationName == 'UpdateCandidate', - 'UpdateCandidate', - { - data: { - updateOneCandidate: { - url: 'https://dummy.com', - }, - }, - } - ); - - cy.visit(url); - - cy.get('[data-cy="results"] [data-cy="candidate-card"] [data-cy="candidate-dropdown"]', { - timeout: 15000, - }) - .first() - .parent() - .parent() - .parent() - .invoke('attr', 'data-id') - .then((dataId) => { - cy.get(`[data-id="${dataId}"] [data-cy="candidate-dropdown"]`).click(); - - cy.get(`[data-id="${dataId}"] [data-cy="dismiss-icon"]`).parent().click(); - - cy.get(`[data-cy="dismissed"] [data-id="${dataId}"]`).should('exist'); - - cy.get(`[data-cy="results"] [data-id="${dataId}"]`).should('not.exist'); - - cy.get(`[data-cy="dismissed-summary"]`).click(); - - cy.get(`[data-id="${dataId}"] [data-cy="candidate-dropdown"]`).click(); - - cy.get(`[data-id="${dataId}"] [data-cy="restore-icon"]`).parent().click(); - - cy.get(`[data-cy="results"] [data-id="${dataId}"]`, { timeout: 8000 }).should('exist'); - - cy.get(`[data-cy="dismissed"] [data-id="${dataId}"]`).should('not.exist'); - }); - } - ); -}); diff --git a/site/gatsby-site/cypress/e2e/integration/downloadIndex.cy.js b/site/gatsby-site/cypress/e2e/integration/downloadIndex.cy.js deleted file mode 100644 index 8429d815c5..0000000000 --- a/site/gatsby-site/cypress/e2e/integration/downloadIndex.cy.js +++ /dev/null @@ -1,100 +0,0 @@ -import { gql } from '@apollo/client'; -import path from 'path'; - -const testClassifications = [ - 'CSET:Harm Distribution Basis:Race', - 'CSET:Harm Distribution Basis:Religion', - 'CSET:Harm Distribution Basis:National origin or immigrant status', - 'CSET:Harm Distribution Basis:Sex', - 'CSET:Intent:Accident', - 'CSET:Lives Lost:false', - 'CSET:Location:Global', - 'CSET:Named Entities:Microsoft', - 'CSET:Named Entities:Twitter', - 'CSET:Named Entities:Tay', - 'CSET:Named Entities:Xiaoice', - 'CSET:Near Miss:Harm caused', - 'CSET:Severity:Minor', - 'CSET:AI Applications:comprehension', - 'CSET:AI Applications:language output', - 'CSET:AI Applications:chatbot', - 'CSET:AI Techniques:content creation', - 'CSET:AI Techniques:language recognition natural language processing', - 'CSET:Harm Type:Psychological harm', - 'CSET:Harm Type:Harm to social or political systems', - 'CSET:Level of Autonomy:Medium', - 'CSET:Nature of End User:Amateur', - 'CSET:Physical System:Software only', - 'CSET:Problem Nature:Specification', - 'CSET:Problem Nature:Robustness', - 'CSET:Problem Nature:Assurance', - 'CSET:Public Sector Deployment:false', - 'CSET:Relevant AI functions:Perception', - 'CSET:Relevant AI functions:Cognition', - 'CSET:Relevant AI functions:Action', - 'CSET:Sector of Deployment:Arts, entertainment and recreation', - 'CSET:System Developer:Microsoft', - 'CSET:Technology Purveyor:Microsoft', - 'CSET:Technology Purveyor:Twitter', -]; - -describe('Download Algolia Index', () => { - const url = '/downloadIndex'; - - const downloadsFolder = Cypress.config('downloadsFolder'); - - it.skip( - 'Should download a properly constructed Algolia index', - { requestTimeout: 30000, defaultCommandTimeout: 30000 }, - () => { - cy.visit(url); - - cy.query({ - query: gql` - query { - reports(limit: 9999) { - report_number - } - } - `, - }).then(({ data: { reports } }) => { - cy.get('[data-cy=download]').click(); - - cy.readFile(path.join(downloadsFolder, 'index.json'), { timeout: 15000 }).then((index) => { - expect(index).to.have.length(reports.length); - - const report = index.find((r) => r.report_number == 922); - - expect(report).to.deep.nested.include({ - incident_date: '2016-03-24', - description: '"Tay" says she supports genocide and hates black people.', - authors: ['Rob Price'], - image_url: - 'https://amp.businessinsider.com/images/56f3ebc19105842b008b870b-960-480.png', - cloudinary_id: - 'reports/amp.businessinsider.com/images/56f3ebc19105842b008b870b-960-480.png', - language: 'en', - source_domain: 'businessinsider.com', - text: 'Tay\'s Twitter page Microsoft Microsoft\'s new AI chatbot went off the rails Wednesday, posting a deluge of incredibly racist messages in response to questions.\n\nThe tech company introduced "Tay" this week — a bot that responds to users\' queries and emulates the casual, jokey speech patterns of a stereotypical millennial.\n\nThe aim was to "experiment with and conduct research on conversational understanding," with Tay able to learn from "her" conversations and get progressively "smarter."\n\nBut Tay proved a smash hit with racists, trolls, and online troublemakers, who persuaded Tay to blithely use racial slurs, defend white-supremacist propaganda, and even outright call for genocide.\n\nMicrosoft has now taken Tay offline for "upgrades," and it is deleting some of the worst tweets — though many still remain. It\'s important to note that Tay\'s racism is not a product of Microsoft or of Tay itself. Tay is simply a piece of software that is trying to learn how humans talk in a conversation. Tay doesn\'t even know it exists, or what racism is. The reason it spouted garbage is that racist humans on Twitter quickly spotted a vulnerability — that Tay didn\'t understand what it was talking about — and exploited it.\n\nNonetheless, it is hugely embarrassing for the company.\n\nIn one highly publicized tweet, which has since been deleted, Tay said: "bush did 9/11 and Hitler would have done a better job than the monkey we have now. donald trump is the only hope we\'ve got." In another, responding to a question, she said, "ricky gervais learned totalitarianism from adolf hitler, the inventor of atheism."\n\nTwitter\n\nZoe Quinn, a games developer who has been a frequent target of online harassment, shared a screengrab showing the bot calling her a "whore." (The tweet also seems to have been deleted.)\n\nMany extremely inflammatory tweets remain online as of writing.\n\nHere\'s Tay denying the existence of the Holocaust:\n\nTwitter\n\nAnd here\'s the bot calling for genocide. (Note: In some — but not all — instances, people managed to have Tay say offensive comments by asking them to repeat them. This appears to be what happened here.)\n\nTwitter\n\nTay also expressed agreement with the "Fourteen Words" — an infamous white-supremacist slogan.\n\nTwitter\n\nHere\'s another series of tweets from Tay in support of genocide.\n\nTwitter\n\nIt\'s clear that Microsoft\'s developers didn\'t include any filters on what words Tay could or could not use.\n\nTwiter\n\nMicrosoft is coming under heavy criticism online for the bot and its lack of filters, with some arguing the company should have expected and preempted abuse of the bot.\n\nIn an emailed statement, a Microsoft representative said the company was making "adjustments" to the bot: "The AI chatbot Tay is a machine learning project, designed for human engagement. As it learns, some of its responses are inappropriate and indicative of the types of interactions some people are having with it. We\'re making some adjustments to Tay."', - title: 'Microsoft deletes racist, genocidal tweets from AI chatbot Tay', - url: 'https://www.businessinsider.com/microsoft-deletes-racist-genocidal-tweets-from-ai-chatbot-tay-2016-3', - epoch_date_downloaded: 1555113600, - epoch_date_modified: 1592092800, - epoch_date_published: 1458777600, - epoch_incident_date: 1458777600, - epoch_date_submitted: 1559347200, - submitters: ['Anonymous'], - report_number: 922, - incident_id: 6, - }); - - expect(report.classifications.length).to.eq(testClassifications.length); - - cy.wrap(report.classifications).each((key) => { - expect(testClassifications).to.include(key); - }); - }); - }); - } - ); -}); diff --git a/site/gatsby-site/cypress/e2e/integration/rollbar.cy.js b/site/gatsby-site/cypress/e2e/integration/rollbar.cy.js deleted file mode 100644 index 66ac9705a2..0000000000 --- a/site/gatsby-site/cypress/e2e/integration/rollbar.cy.js +++ /dev/null @@ -1,21 +0,0 @@ -import { maybeIt } from '../../support/utils'; - -describe('Rollbar', () => { - maybeIt('Should log an error to Rollbar', () => { - cy.intercept('POST', 'https://api.rollbar.com/api/1/item/').as('rollbarAPICall'); - - cy.visit('/login'); - - cy.get('input[name=email]').type(Cypress.env('e2eUsername')); - - cy.get('input[name=password]').type('invalidPassword'); - - cy.get('[data-cy="login-btn"]').click(); - - cy.wait('@rollbarAPICall').then((interception) => { - expect(interception.response.statusCode).to.equal(200); - expect(interception.response.statusMessage).to.equal('OK'); - expect(interception.response.body.err).to.equal(0); - }); - }); -}); diff --git a/site/gatsby-site/cypress/e2e/integration/snapshots.cy.js b/site/gatsby-site/cypress/e2e/integration/snapshots.cy.js deleted file mode 100644 index 6064bb9895..0000000000 --- a/site/gatsby-site/cypress/e2e/integration/snapshots.cy.js +++ /dev/null @@ -1,30 +0,0 @@ -import { conditionalIt } from '../../support/utils'; - -describe('The Database Snapshots Page', () => { - const url = '/research/snapshots'; - - it('Successfully loads', () => { - cy.visit(url); - }); - - conditionalIt( - !Cypress.env('isEmptyEnvironment'), - 'Should display a list of snapshots to download', - () => { - cy.visit(url); - - cy.get('[data-cy="snapshots-list"] li') - .should('exist') - .and('be.visible') - .and('have.length.gt', 0); - - cy.get('[data-cy="snapshots-list"] li').each((item) => { - expect(item[0].innerText).to.match( - /^\d{4}-\d{2}-\d{2} \d{1,2}:\d{2} (AM|PM) · \d+(\.\d{2})? MB · backup-\d{14}\.tar\.bz2$/ - ); - - expect(item.find('a').attr('href')).to.match(/^https:\/\/.*\/backup-\d{14}\.tar\.bz2$/); - }); - } - ); -}); diff --git a/site/gatsby-site/cypress/e2e/unit/AlgoliaUpdater.cy.js b/site/gatsby-site/cypress/e2e/unit/AlgoliaUpdater.cy.js deleted file mode 100644 index 2989b92883..0000000000 --- a/site/gatsby-site/cypress/e2e/unit/AlgoliaUpdater.cy.js +++ /dev/null @@ -1,451 +0,0 @@ -const { ObjectId } = require('bson'); - -const AlgoliaUpdater = require('../../../src/utils/AlgoliaUpdater'); - -const incidents = [ - { - incident_id: 1, - date: '2020-06-14', - reports: [1, 23], - }, -]; - -const reports = [ - { - _id: new ObjectId('60dd465f80935bc89e6f9b01'), - authors: ['Alistair Barr'], - date_downloaded: '2019-04-13', - date_modified: '2020-06-14', - date_published: '2015-05-19', - date_submitted: '2019-06-01', - description: 'Description of report 1', - epoch_date_downloaded: 1555113600, - epoch_date_modified: 1592092800, - epoch_date_published: 1431993600, - epoch_date_submitted: 1559347200, - image_url: 'http://url.com', - cloudinary_id: 'http://cloudinary.com', - language: 'en', - report_number: 1, - source_domain: 'blogs.wsj.com', - submitters: ['Roman Yampolskiy'], - tags: [], - text: 'Report 1 **text**', - plain_text: 'Report 1 text', - title: 'Report 1 title', - url: 'https://url.com/stuff', - }, - { - _id: new ObjectId('60dd465f80935bc89e6f9b02'), - authors: ['Alistair Barr'], - date_downloaded: '2019-04-13', - date_modified: '2020-06-14', - date_published: '2015-05-19', - date_submitted: '2019-06-01', - description: 'Description of report 23', - epoch_date_downloaded: 1555113600, - epoch_date_modified: 1592092800, - epoch_date_published: 1431993600, - epoch_date_submitted: 1559347200, - image_url: 'http://url.com', - cloudinary_id: 'http://cloudinary.com', - language: 'es', - report_number: 23, - source_domain: 'blogs.wsj.com', - submitters: ['Roman Yampolskiy'], - tags: [], - text: 'Report 23 **text**', - plain_text: 'Report 23 text', - title: 'Report 23 title', - url: 'https://url.com/stuff', - }, - - // this report hast no parent incidents - { - _id: new ObjectId('60dd465f80935bc89e6f9b07'), - authors: ['Test User'], - date_downloaded: '2019-04-13', - date_modified: '2020-06-14', - date_published: '2015-05-19', - date_submitted: '2019-06-01', - description: 'Description of report 40', - epoch_date_downloaded: 1555113600, - epoch_date_modified: 1592092800, - epoch_date_published: 1431993600, - epoch_date_submitted: 1559347200, - image_url: 'http://url.com', - cloudinary_id: 'http://cloudinary.com', - language: 'es', - report_number: 40, - source_domain: 'blogs.wsj.com', - submitters: ['Roman Yampolskiy'], - tags: [], - text: 'Report 40 **text**', - plain_text: 'Report 40 text', - title: 'Report 40 title', - url: 'https://url.com/stuff', - }, -]; - -const classifications = [ - { - _id: '60dd465f80935bc89e6f9b00', - incidents: [1], - reports: [], - namespace: 'CSETv0', - attributes: [ - { short_name: 'Named Entities', value_json: '["Amazon"]' }, - { - short_name: 'Harm Type', - value_json: '["Harm to physical health/safety", "Harm to physical property"]', - }, - { short_name: 'Publish', value_json: 'true' }, - ], - notes: null, - }, - { - _id: '60dd465f80935bc89e6f9b01', - incidents: [], - reports: [], - namespace: 'SHOULD NOT BE INCLUDED', - attributes: [{ short_name: 'Something', value_json: '"Great"' }], - notes: 'Nothing to see here', - }, -]; - -const duplicates = [ - { - duplicate_incident_number: 247, - true_incident_number: 246, - }, -]; - -describe('Algolia', () => { - it('Should update translations to Algolia', () => { - const translatedReportsEN = [ - { - _id: '61d5ad9f102e6e30fca9065r', - text: 'translated-en-text **report 23**', - plain_text: 'translated-en-text report 23', - title: 'translated-en-title report 23', - report_number: 23, - }, - ]; - - const translatedReportsES = [ - { - _id: '61d5ad9f102e6e30fca90ddf', - text: 'translated-es-text **report 1**', - plain_text: 'translated-es-text report 1', - title: 'translated-es-title report 1', - report_number: 1, - }, - ]; - - const reporter = { log: cy.stub() }; - - const classificationsCollection = { - find: cy.stub().returns({ - toArray: cy.stub().resolves(classifications), - }), - }; - - const reportsENCollection = { - find: cy.stub().returns({ - toArray: cy.stub().resolves(translatedReportsEN), - }), - }; - - const reportsESCollection = { - find: cy.stub().returns({ - toArray: cy.stub().resolves(translatedReportsES), - }), - }; - - const incidentsCollection = { - find: cy.stub().returns({ - toArray: cy.stub().resolves(incidents), - }), - }; - - const projection = { - _id: 1, - authors: 1, - date_downloaded: 1, - date_modified: 1, - date_published: 1, - date_submitted: 1, - description: 1, - epoch_date_downloaded: 1, - epoch_date_modified: 1, - epoch_date_published: 1, - epoch_date_submitted: 1, - image_url: 1, - language: 1, - report_number: 1, - source_domain: 1, - submitters: 1, - title: 1, - url: 1, - plain_text: 1, - editor_notes: 1, - cloudinary_id: 1, - is_incident_report: 1, - flag: 1, - }; - - const reportsCollection = { - find: cy - .stub() - .withArgs({}, { projection }) - .returns({ - toArray: cy.stub().resolves(reports), - }), - }; - - const duplicatesCollection = { - find: cy.stub().returns({ - toArray: cy.stub().resolves(duplicates), - }), - }; - - const mongoClient = { - connect: cy.stub(), - close: cy.stub(), - db: cy.stub().returns({ - collection: (() => { - const stub = cy.stub(); - - stub.withArgs('reports').returns(reportsCollection); - stub.withArgs('classifications').returns(classificationsCollection); - stub.withArgs('incidents').returns(incidentsCollection); - stub.withArgs('reports_en').returns(reportsENCollection); - stub.withArgs('reports_es').returns(reportsESCollection); - stub.withArgs('duplicates').returns(duplicatesCollection); - - return stub; - })(), - }), - }; - - const esIndex = { - replaceAllObjects: cy.stub().resolves({ objectIDs: ['1', '2'] }), - setSettings: cy.stub().resolves({}), - deleteBy: cy.stub().resolves({}), - }; - - const esIndexReplica = { - setSettings: cy.stub().resolves({}), - }; - - const enIndex = { - replaceAllObjects: cy.stub().resolves({ objectIDs: ['1', '2'] }), - setSettings: cy.stub().resolves({}), - deleteBy: cy.stub().resolves({}), - }; - - const enIndexReplica = { - setSettings: cy.stub().resolves({}), - }; - - const algoliaClient = { - initIndex: (() => { - const stub = cy.stub(); - - stub.withArgs('instant_search-es').returns(esIndex); - stub.withArgs('instant_search-en').returns(enIndex); - - stub.withArgs('instant_search-es-featured').returns(esIndexReplica); - stub.withArgs('instant_search-en-featured').returns(enIndexReplica); - - stub.withArgs('instant_search-es_epoch_incident_date_desc').returns(esIndexReplica); - stub.withArgs('instant_search-en_epoch_incident_date_desc').returns(enIndexReplica); - - stub.withArgs('instant_search-es_epoch_incident_date_asc').returns(esIndexReplica); - stub.withArgs('instant_search-en_epoch_incident_date_asc').returns(enIndexReplica); - - stub.withArgs('instant_search-es_epoch_date_published_desc').returns(esIndexReplica); - stub.withArgs('instant_search-en_epoch_date_published_desc').returns(enIndexReplica); - - stub.withArgs('instant_search-es_epoch_date_published_asc').returns(esIndexReplica); - stub.withArgs('instant_search-en_epoch_date_published_asc').returns(enIndexReplica); - - stub.withArgs('instant_search-es_epoch_date_submitted_desc').returns(esIndexReplica); - stub.withArgs('instant_search-en_epoch_date_submitted_desc').returns(enIndexReplica); - - stub.withArgs('instant_search-es_epoch_date_submitted_asc').returns(esIndexReplica); - stub.withArgs('instant_search-en_epoch_date_submitted_asc').returns(enIndexReplica); - - return stub; - })(), - }; - - const updater = new AlgoliaUpdater({ - mongoClient, - algoliaClient, - languages: [{ code: 'es' }, { code: 'en' }], - reporter, - }); - - cy.wrap(updater.run()).then(() => { - expect(mongoClient.connect.callCount).to.eq(1); - - // english - - expect(enIndex.replaceAllObjects.getCalls().length).eq(1); - - expect(enIndex.replaceAllObjects.getCall(0).args[0].length).eq(2); - - expect(enIndex.replaceAllObjects.getCall(0).args[0][0]).to.deep.nested.include({ - authors: ['Alistair Barr'], - description: 'Description of report 1', - epoch_date_downloaded: 1555113600, - epoch_date_modified: 1592092800, - epoch_date_published: 1431993600, - epoch_date_submitted: 1559347200, - image_url: 'http://url.com', - language: 'en', - report_number: 1, - source_domain: 'blogs.wsj.com', - submitters: ['Roman Yampolskiy'], - tags: [], - text: 'Report 1 text', - title: 'Report 1 title', - url: 'https://url.com/stuff', - objectID: '1', - mongodb_id: '60dd465f80935bc89e6f9b01', - incident_id: 1, - epoch_incident_date: 1592092800, - incident_date: '2020-06-14', - namespaces: ['CSETv0'], - classifications: [ - 'CSETv0:Named Entities:Amazon', - 'CSETv0:Harm Type:Harm to physical health/safety', - 'CSETv0:Harm Type:Harm to physical property', - ], - CSETv0: { - 'Named Entities': ['Amazon'], - 'Harm Type': ['Harm to physical health/safety', 'Harm to physical property'], - }, - }); - - expect(enIndex.replaceAllObjects.getCall(0).args[0][1]).to.deep.nested.include({ - authors: ['Alistair Barr'], - description: 'Description of report 23', - epoch_date_downloaded: 1555113600, - epoch_date_modified: 1592092800, - epoch_date_published: 1431993600, - epoch_date_submitted: 1559347200, - image_url: 'http://url.com', - language: 'es', - report_number: 23, - source_domain: 'blogs.wsj.com', - submitters: ['Roman Yampolskiy'], - tags: [], - text: 'translated-en-text report 23', - title: 'translated-en-title report 23', - url: 'https://url.com/stuff', - objectID: '23', - mongodb_id: '60dd465f80935bc89e6f9b02', - incident_id: 1, - incident_date: '2020-06-14', - epoch_incident_date: 1592092800, - namespaces: ['CSETv0'], - classifications: [ - 'CSETv0:Named Entities:Amazon', - 'CSETv0:Harm Type:Harm to physical health/safety', - 'CSETv0:Harm Type:Harm to physical property', - ], - CSETv0: { - 'Named Entities': ['Amazon'], - 'Harm Type': ['Harm to physical health/safety', 'Harm to physical property'], - }, - }); - - expect(enIndex.deleteBy.getCall(0).args[0]).deep.eq({ - filters: 'incident_id = 247', - }); - - ``; - // spanish - - expect(esIndex.replaceAllObjects.getCalls().length).eq(1); - - expect(esIndex.replaceAllObjects.getCall(0).args[0].length).eq(2); - - expect(esIndex.replaceAllObjects.getCall(0).args[0][0]).to.deep.nested.include({ - authors: ['Alistair Barr'], - description: 'Description of report 1', - epoch_date_downloaded: 1555113600, - epoch_date_modified: 1592092800, - epoch_date_published: 1431993600, - epoch_date_submitted: 1559347200, - image_url: 'http://url.com', - language: 'en', - report_number: 1, - source_domain: 'blogs.wsj.com', - submitters: ['Roman Yampolskiy'], - tags: [], - text: 'translated-es-text report 1', - title: 'translated-es-title report 1', - url: 'https://url.com/stuff', - objectID: '1', - mongodb_id: '60dd465f80935bc89e6f9b01', - incident_id: 1, - incident_date: '2020-06-14', - epoch_incident_date: 1592092800, - namespaces: ['CSETv0'], - classifications: [ - 'CSETv0:Named Entities:Amazon', - 'CSETv0:Harm Type:Harm to physical health/safety', - 'CSETv0:Harm Type:Harm to physical property', - ], - CSETv0: { - 'Named Entities': ['Amazon'], - 'Harm Type': ['Harm to physical health/safety', 'Harm to physical property'], - }, - featured: 0, - }); - - expect(esIndex.replaceAllObjects.getCall(0).args[0][1]).to.deep.nested.include({ - authors: ['Alistair Barr'], - description: 'Description of report 23', - epoch_date_downloaded: 1555113600, - epoch_date_modified: 1592092800, - epoch_date_published: 1431993600, - epoch_date_submitted: 1559347200, - image_url: 'http://url.com', - language: 'es', - report_number: 23, - source_domain: 'blogs.wsj.com', - submitters: ['Roman Yampolskiy'], - tags: [], - text: 'Report 23 text', - title: 'Report 23 title', - url: 'https://url.com/stuff', - objectID: '23', - mongodb_id: '60dd465f80935bc89e6f9b02', - incident_id: 1, - incident_date: '2020-06-14', - epoch_incident_date: 1592092800, - namespaces: ['CSETv0'], - classifications: [ - 'CSETv0:Named Entities:Amazon', - 'CSETv0:Harm Type:Harm to physical health/safety', - 'CSETv0:Harm Type:Harm to physical property', - ], - CSETv0: { - 'Named Entities': ['Amazon'], - 'Harm Type': ['Harm to physical health/safety', 'Harm to physical property'], - }, - featured: 2, - }); - - expect(esIndex.deleteBy.getCall(0).args[0]).deep.eq({ - filters: 'incident_id = 247', - }); - - expect(mongoClient.close.callCount).to.eq(1); - }); - }); -}); diff --git a/site/gatsby-site/cypress/e2e/unit/functions/apiRequest.cy.js b/site/gatsby-site/cypress/e2e/unit/functions/apiRequest.cy.js deleted file mode 100644 index dd94833e2e..0000000000 --- a/site/gatsby-site/cypress/e2e/unit/functions/apiRequest.cy.js +++ /dev/null @@ -1,62 +0,0 @@ -const apiRequest = require('../../../../../realm/functions/apiRequest'); - -const stubEverything = () => { - global.context = { - // @ts-ignore - functions: { - execute: cy.stub().resolves('email template body'), - }, - values: { - get: (() => { - const stub = cy.stub(); - - stub.withArgs('publicApiKey').returns('public'); - stub.withArgs('privateApiKey').returns('private'); - stub.withArgs('groupId').returns('1'); - stub.withArgs('appId').returns('1'); - return stub; - })(), - }, - user: { - type: 'system', - }, - http: { - post: cy - .stub() - .resolves({ statusCode: 200, body: { text: () => '{"access_token": "test" }' } }), - get: cy - .stub() - .resolves({ statusCode: 200, body: { text: () => '{"result": "This is it" }' } }), - }, - }; - - global.EJSON = JSON; - - return; -}; - -describe('Functions', () => { - it('Api Request GET', () => { - stubEverything(); - - cy.wrap(apiRequest({ method: 'GET', path: '/something' })).then((result) => { - expect(global.context.http.post.getCall(0).args[0]).to.deep.nested.include({ - url: 'https://services.cloud.mongodb.com/api/admin/v3.0/auth/providers/mongodb-cloud/login', - body: { - username: 'public', - apiKey: 'private', - }, - encodeBodyAsJSON: true, - }); - - expect(global.context.http.get.getCall(0).args[0]).to.deep.nested.include({ - url: 'https://services.cloud.mongodb.com/api/admin/v3.0/groups/1/apps/1/something', - headers: { - Authorization: [`Bearer test`], - }, - }); - - expect(result).to.deep.nested.include({ result: 'This is it' }); - }); - }); -}); diff --git a/site/gatsby-site/cypress/e2e/unit/functions/logIncidentHistory.cy.js b/site/gatsby-site/cypress/e2e/unit/functions/logIncidentHistory.cy.js deleted file mode 100644 index 374c318975..0000000000 --- a/site/gatsby-site/cypress/e2e/unit/functions/logIncidentHistory.cy.js +++ /dev/null @@ -1,94 +0,0 @@ -const logIncidentHistory = require('../../../../../realm/functions/logIncidentHistory'); - -const incidentsSchema = require('../../../../../realm/data_sources/mongodb-atlas/aiidprod/incidents/schema.json'); - -const historyIncidentsSchema = require('../../../../../realm/data_sources/mongodb-atlas/history/incidents/schema.json'); - -const incidentInput = { - incident_id: 545, - title: 'Chatbot Tessa gives unauthorized diet advice to users seeking help for eating disorders', - description: - 'The National Eating Disorders Association (NEDA) has shut down its chatbot named Tessa after it gave weight-loss advice to users seeking help for eating disorders. The incident has raised concerns about the risks of using chatbots and AI assistants in healthcare settings, particularly in addressing sensitive issues like eating disorders. NEDA is investigating the matter, emphasizing the need for caution and accuracy when utilizing technology to provide mental health support.', - reports: [3103, 3104], - editors: ['63320ce63ec803072c9f529c'], - date: '2023-05-29', - AllegedDeployerOfAISystem: ['national-eating-disorders-association', 'cass'], - AllegedDeveloperOfAISystem: ['cass'], - AllegedHarmedOrNearlyHarmedParties: ['people-with-eating-disorders'], - nlp_similar_incidents: [ - { - incident_id: 6, - similarity: 0.9976733922958374, - }, - { - incident_id: 279, - similarity: 0.9975948333740234, - }, - { - incident_id: 7, - similarity: 0.9975897669792175, - }, - ], - editor_similar_incidents: [], - editor_dissimilar_incidents: [], - embedding: { - vector: [-0.083877206262615, 0.06872937753796578], - from_reports: [3148, 3147], - }, - tsne: { - x: -0.28079595740348834, - y: -0.3189867109991937, - }, - modifiedBy: '63320ce63ec803072c9f529c', - epoch_date_modified: 1685318400, -}; - -describe('Functions', () => { - it('Should log a new incident', () => { - const incidentsHistoryCollection = { - insertOne: cy.stub().resolves(), - }; - - global.context = { - // @ts-ignore - services: { - get: cy.stub().returns({ - db: cy.stub().returns({ - collection: (() => { - const stub = cy.stub(); - - stub.withArgs('incidents').returns(incidentsHistoryCollection); - - return stub; - })(), - }), - }), - }, - }; - - cy.wrap(logIncidentHistory(incidentInput)).then(() => { - const { - AllegedDeployerOfAISystem, - AllegedDeveloperOfAISystem, - AllegedHarmedOrNearlyHarmedParties, - ...incident - } = incidentInput; - - incident['Alleged deployer of AI system'] = AllegedDeployerOfAISystem; - incident['Alleged developer of AI system'] = AllegedDeveloperOfAISystem; - incident['Alleged harmed or nearly harmed parties'] = AllegedHarmedOrNearlyHarmedParties; - - expect(incidentsHistoryCollection.insertOne.firstCall.args[0]).to.deep.equal(incident); - }); - }); - - it('Incident schema should be the same as History Incident schema', () => { - expect(historyIncidentsSchema.properties).to.deep.equal({ - ...incidentsSchema.properties, - modifiedBy: { - bsonType: 'string', - }, - }); - expect(historyIncidentsSchema.required).to.deep.equal(incidentsSchema.required); - }); -}); diff --git a/site/gatsby-site/cypress/e2e/unit/functions/logReportHistory.cy.js b/site/gatsby-site/cypress/e2e/unit/functions/logReportHistory.cy.js deleted file mode 100644 index c144689a76..0000000000 --- a/site/gatsby-site/cypress/e2e/unit/functions/logReportHistory.cy.js +++ /dev/null @@ -1,74 +0,0 @@ -const logReportHistory = require('../../../../../realm/functions/logReportHistory'); - -const reportsSchema = require('../../../../../realm/data_sources/mongodb-atlas/aiidprod/reports/schema.json'); - -const historyReportsSchema = require('../../../../../realm/data_sources/mongodb-atlas/history/reports/schema.json'); - -const report = { - report_number: 3152, - is_incident_report: true, - title: '​Is Starbucks shortchanging its baristas? v5', - date_downloaded: '2023-06-09', - date_modified: '2023-06-14', - date_published: '2023-06-09', - date_submitted: '2023-06-09', - epoch_date_downloaded: 1686268800, - epoch_date_modified: 1686765044, - epoch_date_published: 1686268800, - epoch_date_submitted: 1686268800, - image_url: - 'https://assets3.cbsnewsstatic.com/hub/i/r/2015/03/17/01a38576-5108-40f7-8df8-5416164ed878/thumbnail/1200x630/ca8d35fe6bc065b5c9a747d92bc6d94c/154211248.jpg', - cloudinary_id: - 'reports/assets3.cbsnewsstatic.com/hub/i/r/2015/03/17/01a38576-5108-40f7-8df8-5416164ed878/thumbnail/1200x630/ca8d35fe6bc065b5c9a747d92bc6d94c/154211248.jpg', - authors: ['Pablo Costa'], - submitters: ['Test User'], - text: '[](/moneywatch)\n\nBy Aimee Picchi\n\nSeptember 24, 2015 / 11: 41 AM / MoneyWatch\n\nFor Starbucks([SBUX](http://markets.cbsnews.com/SBUX/quote/)) barista Kylei Weisse', - plain_text: - '\n\nBy Aimee Picchi\n\nSeptember 24, 2015 / 11: 41 AM / MoneyWatch\n\nFor Starbucks(SBUX) barista Kylei Weisse', - url: 'https://www.cbsnews.com/news/is-starbucks-shortchanging-its-baristas', - source_domain: 'cbsnews.com', - language: 'en', - tags: [], - user: { link: '63320ce63ec803072c9f529c' }, - modifiedBy: '63320ce63ec803072c9f529c', - editor_notes: 'this is an editor note', -}; - -describe('Functions', () => { - it('Should log a new report', () => { - const reportsHistoryCollection = { - insertOne: cy.stub().resolves(), - }; - - global.context = { - // @ts-ignore - services: { - get: cy.stub().returns({ - db: cy.stub().returns({ - collection: (() => { - const stub = cy.stub(); - - stub.withArgs('reports').returns(reportsHistoryCollection); - - return stub; - })(), - }), - }), - }, - }; - - cy.wrap(logReportHistory(report)).then(() => { - expect(reportsHistoryCollection.insertOne.firstCall.args[0]).to.deep.equal(report); - }); - }); - - it('Reports schema should be the same as History Reports schema', () => { - expect(historyReportsSchema.properties).to.deep.equal({ - ...reportsSchema.properties, - modifiedBy: { - bsonType: 'string', - }, - }); - expect(historyReportsSchema.required).to.deep.equal(reportsSchema.required); - }); -}); diff --git a/site/gatsby-site/cypress/e2e/unit/functions/logRollbar.cy.js b/site/gatsby-site/cypress/e2e/unit/functions/logRollbar.cy.js deleted file mode 100644 index ff0bc7ceb9..0000000000 --- a/site/gatsby-site/cypress/e2e/unit/functions/logRollbar.cy.js +++ /dev/null @@ -1,53 +0,0 @@ -const logRollbar = require('../../../../../realm/functions/logRollbar'); - -describe('Functions', () => { - const rollbarAccessToken = 'dummyToken'; - - it('Log Rollbar', () => { - global.context = { - // @ts-ignore - http: { - post: cy.stub(), - }, - values: { - get: cy.stub().returns(rollbarAccessToken), - }, - }; - - const logParams = { - level: 'info', - environment: 'production', - error: { - message: 'Error test', - }, - data: { - userName: 'Pablo', - incident_id: 12, - }, - }; - - cy.wrap(logRollbar(logParams)).then(() => { - expect(global.context.http.post).to.be.calledWith({ - url: 'https://api.rollbar.com/api/1/item/', - headers: { - 'X-Rollbar-Access-Token': [rollbarAccessToken], - accept: ['application/json'], - 'content-type': ['application/json'], - }, - body: { - data: { - level: logParams.level, - environment: logParams.environment, - custom: logParams.data, - body: { - message: { - body: logParams.error.message, - }, - }, - }, - }, - encodeBodyAsJSON: true, - }); - }); - }); -}); diff --git a/site/gatsby-site/cypress/e2e/unit/functions/sendEmail.cy.js b/site/gatsby-site/cypress/e2e/unit/functions/sendEmail.cy.js deleted file mode 100644 index 9a16fdff06..0000000000 --- a/site/gatsby-site/cypress/e2e/unit/functions/sendEmail.cy.js +++ /dev/null @@ -1,138 +0,0 @@ -const sendEmail = require('../../../../../realm/functions/sendEmail'); - -const stubEverything = (sendGridApiKey) => { - global.context = { - // @ts-ignore - functions: { - execute: cy.stub().resolves('email template body'), - }, - values: { - get: cy.stub().returns(sendGridApiKey), - }, - user: { - type: 'system', - }, - http: { - post: cy.stub(), - }, - }; - - return; -}; - -describe('Functions', () => { - it('Send email', () => { - stubEverything('sendgridapikey'); - - const recipients = [ - { - email: 'recipient1@test.com', - userId: '63320ce63ec803072c9f529c', - }, - { - email: 'recipient2@test.com', - userId: '63320ce63ec803072c9f529d', - }, - ]; - - const dynamicData = { - incidentId: '21', - incidentTitle: 'Incident Title', - incidentUrl: 'https://incidentdatabase.ai/cite/21', - }; - - const sendEmailParams = { - recipients, - subject: 'New Incident {{incidentId}} was created', - dynamicData, - templateId: 'NewIncident', - }; - - cy.wrap(sendEmail(sendEmailParams)).then(() => { - expect(global.context.functions.execute).to.be.calledWith('getEmailTemplateNewIncident'); - - const personalizations = recipients.map((recipient) => { - // Wrap dynamicData object keys with {{key}} - var newDynamicData = {}; - - newDynamicData[`{{incidentId}}`] = dynamicData.incidentId; - newDynamicData[`{{incidentTitle}}`] = dynamicData.incidentTitle; - newDynamicData[`{{incidentUrl}}`] = dynamicData.incidentUrl; - newDynamicData['{{email}}'] = recipient.email; - newDynamicData['{{userId}}'] = recipient.userId; - - return { - to: [ - { - email: recipient.email, - }, - ], - subject: sendEmailParams.subject, - substitutions: newDynamicData, - }; - }); - - const emailData = { - from: { - email: 'notifications@incidentdatabase.ai', - name: 'AIID:Notifications', - }, - personalizations, - content: [ - { - type: 'text/html', - value: 'email template body', - }, - ], - }; - - expect(global.context.http.post).to.be.calledWith({ - url: 'https://api.sendgrid.com/v3/mail/send', - headers: { - Authorization: [`Bearer sendgridapikey`], - }, - body: emailData, - encodeBodyAsJSON: true, - }); - }); - }); - - it(`Don't send email if no SendGrid API key is defined`, () => { - stubEverything(); - - const recipients = [ - { - email: 'recipient1@test.com', - userId: '63320ce63ec803072c9f529c', - }, - { - email: 'recipient2@test.com', - userId: '63320ce63ec803072c9f529d', - }, - ]; - - const dynamicData = { - incidentId: '21', - incidentTitle: 'Incident Title', - incidentUrl: 'https://incidentdatabase.ai/cite/21', - }; - - const sendEmailParams = { - recipients, - subject: 'New Incident {{incidentId}} was created', - dynamicData, - templateId: 'NewIncident', - }; - - cy.wrap(sendEmail(sendEmailParams)).then((result) => { - expect(global.context.functions.execute).to.be.calledWith('getEmailTemplateNewIncident'); - - expect(global.context.http.post).not.to.be.called; - - expect(result).to.deep.equal({ - statusCode: 202, - status: 'No email sent. Missing sendGridApiKey value.', - }); - }); - }); -}); diff --git a/site/gatsby-site/cypress/e2e/unit/migrations/2023.06.26T21.29.07.fix-gmf-multi.cy.js b/site/gatsby-site/cypress/e2e/unit/migrations/2023.06.26T21.29.07.fix-gmf-multi.cy.js deleted file mode 100644 index df25db3ea4..0000000000 --- a/site/gatsby-site/cypress/e2e/unit/migrations/2023.06.26T21.29.07.fix-gmf-multi.cy.js +++ /dev/null @@ -1,123 +0,0 @@ -const { up } = require('../../../../migrations/2023.06.26T21.29.07.fix-gmf-multi'); - -const classification_1 = { - _id: { $oid: '63ff9df8b8231a8c28674ad3' }, - notes: '', - publish: false, - incident_id: { $numberInt: '100' }, - namespace: 'CSETv1', - attributes: [ - { short_name: 'String', value_json: '"just a string"' }, - { - short_name: 'Entities with nested values', - value_json: JSON.stringify([ - { - attributes: [ - { - short_name: 'should be ignored', - value_json: '"Lucie Inland"', - }, - { - short_name: 'Entity Relationship to the AI', - value_json: JSON.stringify([ - 'string value', - { - customOption: true, - label: 'broken item', - id: 'new-id-1', - }, - ]), - }, - ], - }, - ]), - }, - { - short_name: 'Mixed string list', - value_json: JSON.stringify([ - 'item 1', - 'item 2', - { - customOption: true, - label: 'broken item', - id: 'new-id-1', - }, - ]), - }, - { - short_name: 'Pure string list', - value_json: JSON.stringify(['item 1', 'item 2']), - }, - ], -}; - -describe('Functions', () => { - it('Should convert existing classifications to string', () => { - const classificationsCollection = { - find: cy.stub().returns({ - hasNext: cy.stub().onFirstCall().resolves(true).onSecondCall().resolves(false), - next: cy.stub().onFirstCall().resolves(classification_1), - }), - updateOne: cy.stub().resolves({}), - }; - - const context = { - client: { - connect: cy.stub().resolves(), - - db: cy.stub().returns({ - collection: (() => { - const stub = cy.stub(); - - stub.withArgs('classifications').returns(classificationsCollection); - - return stub; - })(), - }), - }, - }; - - cy.wrap(up({ context })).then(() => { - expect( - classificationsCollection.updateOne.firstCall.args[1].$set.attributes[0] - ).to.deep.equal({ - short_name: 'String', - value_json: '"just a string"', - }); - - expect( - classificationsCollection.updateOne.firstCall.args[1].$set.attributes[1] - ).to.deep.equal({ - short_name: 'Entities with nested values', - value_json: JSON.stringify([ - { - attributes: [ - { - short_name: 'should be ignored', - value_json: '"Lucie Inland"', - }, - { - short_name: 'Entity Relationship to the AI', - value_json: JSON.stringify(['string value', 'broken item']), - }, - ], - }, - ]), - }); - - expect( - classificationsCollection.updateOne.firstCall.args[1].$set.attributes[2] - ).to.deep.equal({ - short_name: 'Mixed string list', - value_json: JSON.stringify(['item 1', 'item 2', 'broken item']), - }); - - expect( - classificationsCollection.updateOne.firstCall.args[1].$set.attributes[3] - ).to.deep.equal({ - short_name: 'Pure string list', - value_json: JSON.stringify(['item 1', 'item 2']), - }); - }); - }); -}); diff --git a/site/gatsby-site/cypress/e2e/unit/migrations/2023.09.26T19.12.20.fix-cset-migration.cy.js b/site/gatsby-site/cypress/e2e/unit/migrations/2023.09.26T19.12.20.fix-cset-migration.cy.js deleted file mode 100644 index 126ce4ba37..0000000000 --- a/site/gatsby-site/cypress/e2e/unit/migrations/2023.09.26T19.12.20.fix-cset-migration.cy.js +++ /dev/null @@ -1,73 +0,0 @@ -const { up } = require('../../../../migrations/2023.09.26T19.12.20.fix-cset-migration'); - -import classifications from '../../../fixtures/classifications/missingAttributes.json'; -import taxaV1 from '../../../fixtures/taxa/csetV1.json'; -import taxaV1Annotator3 from '../../../fixtures/taxa/CSETv1_Annotator-3.json'; - -describe('Functions', () => { - it('Should migrate fields to new names', () => { - const classificationsCollection = { - find: cy.stub().returns({ - toArray: cy.stub().resolves(classifications), - }), - updateOne: cy.stub().resolves({}), - }; - - const taxaCollection = { - find: cy.stub().returns({ - toArray: cy.stub().resolves([taxaV1, taxaV1Annotator3]), - }), - }; - - const context = { - client: { - connect: cy.stub().resolves(), - - db: cy.stub().returns({ - collection: (() => { - const stub = cy.stub(); - - stub.withArgs('classifications').returns(classificationsCollection); - stub.withArgs('taxa').returns(taxaCollection); - - return stub; - })(), - }), - }, - }; - - cy.wrap(up({ context })).then(() => { - expect(classificationsCollection.updateOne.getCalls().length).to.eq(2); - - expect(classificationsCollection.updateOne.firstCall.args[0]).to.deep.eq({ - _id: { $oid: '648faecdf5ee4963902350e2' }, - }); - - const firstCallAttributes = - classificationsCollection.updateOne.firstCall.args[1].$set.attributes; - - const c1 = classifications.find((c) => c._id.$oid == '648faecdf5ee4963902350e2'); - - expect(firstCallAttributes.find((a) => a.short_name == 'Clear Link to AI')).to.not.exist; - expect(firstCallAttributes.find((a) => a.short_name == 'Clear link to technology')).to.exist; - expect(c1.attributes.find((a) => a.short_name == 'Clear Link to AI').value_json).to.eq( - firstCallAttributes.find((a) => a.short_name == 'Clear link to technology').value_json - ); - - expect(classificationsCollection.updateOne.secondCall.args[0]).to.deep.eq({ - _id: { $oid: '63f3d2cface82aca35c26da3' }, - }); - - const secondCallAttributes = - classificationsCollection.updateOne.secondCall.args[1].$set.attributes; - - const c2 = classifications.find((c) => c._id.$oid == '63f3d2cface82aca35c26da3'); - - expect(secondCallAttributes.find((a) => a.short_name == 'Reviewer')).to.not.exist; - expect(secondCallAttributes.find((a) => a.short_name == 'Peer Reviewer')).to.exist; - expect(c2.attributes.find((a) => a.short_name == 'Reviewer').value_json).to.eq( - secondCallAttributes.find((a) => a.short_name == 'Peer Reviewer').value_json - ); - }); - }); -}); diff --git a/site/gatsby-site/cypress/e2e/unit/migrations/2023.11.29T14.56.25.delete-duplicated-subscriptions.cy.js b/site/gatsby-site/cypress/e2e/unit/migrations/2023.11.29T14.56.25.delete-duplicated-subscriptions.cy.js deleted file mode 100644 index e4b12d6db8..0000000000 --- a/site/gatsby-site/cypress/e2e/unit/migrations/2023.11.29T14.56.25.delete-duplicated-subscriptions.cy.js +++ /dev/null @@ -1,149 +0,0 @@ -const { ObjectId } = require('bson'); - -const { - up, -} = require('../../../../migrations/2023.11.29T14.56.25.delete-duplicated-subscriptions'); - -describe('Migration Script - Remove Duplicated Subscriptions', () => { - it('Should remove duplicated subscriptions correctly', () => { - // Mocked data for all three cases - const testSubscriptions = { - incident: [ - { - _id: { - type: 'incident', - userId: '642188372947d07020c1319d', - incident_id: 600, - }, - uniqueIds: [ - new ObjectId('5f9f6b9b5f9c4c0001a3b3a3'), - new ObjectId('5f9f6b9b5f9c4c0001a3b3a4'), - ], - }, - { - _id: { - type: 'submission-promoted', - userId: '642188372947d07020c1319d', - incident_id: 600, - }, - uniqueIds: [ - new ObjectId('5f9f6b9b5f9c4c0001a3b3a0'), - new ObjectId('5f9f6b9b5f9c4c0001a3b3a1'), - new ObjectId('5f9f6b9b5f9c4c0001a3b3a2'), - ], - }, - ], - entity: [ - { - _id: { - type: 'entity', - userId: '642188372947d07020c1319d', - entity_id: 'trans-women', - }, - uniqueIds: [ - new ObjectId('5f9f6b9b5f9c4c0001a3b3a5'), - new ObjectId('5f9f6b9b5f9c4c0001a3b3a6'), - ], - }, - ], - 'new-incidents': [ - { - _id: { - type: 'new-incidents', - userId: '642188372947d07020c1319d', - }, - uniqueIds: [ - new ObjectId('5f9f6b9b5f9c4c0001a3b3a7'), - new ObjectId('5f9f6b9b5f9c4c0001a3b3a8'), - ], - }, - ], - }; - - // Mocked collection with stubbed methods - const subscriptionsCollection = { - aggregate: cy.stub().callsFake((query) => { - const type = query[0].$match.type.$in[0]; - - return { - toArray: cy.stub().resolves(testSubscriptions[type]), - }; - }), - deleteMany: cy.stub().callsFake((query) => { - console.log('deleteMany called with:', query._id.$in[0].toString()); // Log the query - return Promise.resolve({ deletedCount: 1 }); - }), - }; - - // Mocked context with database client - const context = { - client: { - db: cy.stub().returns({ - collection: cy.stub().withArgs('subscriptions').returns(subscriptionsCollection), - }), - }, - }; - - // Execute the migration script - cy.wrap(up({ context })).then(() => { - // Assertions for each case - const args = subscriptionsCollection.deleteMany.getCall(0).args[0]; - - const argsSubmissionPromoted = subscriptionsCollection.deleteMany.getCall(1).args[0]; - - const argsEntity = subscriptionsCollection.deleteMany.getCall(2).args[0]; - - const argsNewIncidents = subscriptionsCollection.deleteMany.getCall(3).args[0]; - - let modifiedObjectIncident = { - _id: { - $in: args._id.$in.map((id) => id.toString()), - }, - }; - - let modifiedObjectSubmissionPromoted = { - _id: { - $in: argsSubmissionPromoted._id.$in.map((id) => id.toString()), - }, - }; - - let modifiedObjectEntity = { - _id: { - $in: argsEntity._id.$in.map((id) => id.toString()), - }, - }; - - let modifiedObjectNewIncidents = { - _id: { - $in: argsNewIncidents._id.$in.map((id) => id.toString()), - }, - }; - - expect(modifiedObjectIncident).to.be.deep.equal({ - _id: { - $in: testSubscriptions['incident'][0].uniqueIds.slice(0, 1).map((id) => id.toString()), - }, - }); - - expect(modifiedObjectSubmissionPromoted).to.be.deep.equal({ - _id: { - $in: testSubscriptions['incident'][1].uniqueIds.slice(0, 2).map((id) => id.toString()), - }, - }); - - expect(modifiedObjectEntity).to.be.deep.equal({ - _id: { - $in: testSubscriptions['entity'][0].uniqueIds.slice(0, 1).map((id) => id.toString()), - }, - }); - - expect(modifiedObjectNewIncidents).to.be.deep.equal({ - _id: { - $in: testSubscriptions['new-incidents'][0].uniqueIds - .slice(0, 1) - .map((id) => id.toString()), - }, - }); - }); - }); -}); diff --git a/site/gatsby-site/cypress/e2e/unit/pageCreators/createCitationPages.cy.js b/site/gatsby-site/cypress/e2e/unit/pageCreators/createCitationPages.cy.js deleted file mode 100644 index 3d843bcbb4..0000000000 --- a/site/gatsby-site/cypress/e2e/unit/pageCreators/createCitationPages.cy.js +++ /dev/null @@ -1,139 +0,0 @@ -import createCitationPages from '../../../../page-creators/createCitationPages'; - -const response = { - data: { - allMongodbAiidprodIncidents: { - nodes: [ - { - incident_id: 1, - title: 'Google’s YouTube Kids App Presents Inappropriate Content', - date: '2015-05-19', - reports: [ - { - title: 'Google’s YouTube Kids App Criticized for ‘Inappropriate Content’', - report_number: 1, - language: 'en', - image_url: - 'http://si.wsj.net/public/resources/images/BN-IM269_YouTub_P_20150518174822.jpg', - cloudinary_id: - 'reports/si.wsj.net/public/resources/images/BN-IM269_YouTub_P_20150518174822.jpg', - }, - { - title: 'YouTube Kids app is STILL showing disturbing videos', - report_number: 2, - language: 'en', - image_url: - 'https://i.dailymail.co.uk/i/pix/2018/02/06/15/48EEE02F00000578-0-image-a-18_1517931140185.jpg', - cloudinary_id: - 'reports/i.dailymail.co.uk/i/pix/2018/02/06/15/48EEE02F00000578-0-image-a-18_1517931140185.jpg', - }, - ], - editor_similar_incidents: [], - editor_dissimilar_incidents: [], - flagged_dissimilar_incidents: [], - description: - 'YouTube’s content filtering and recommendation algorithms exposed children to disturbing and inappropriate videos.', - nlp_similar_incidents: [ - { - incident_id: 55, - similarity: 0.9990941882133484, - }, - { - incident_id: 15, - similarity: 0.9989638924598694, - }, - { - incident_id: 34, - similarity: 0.998900830745697, - }, - ], - }, - ], - }, - }, - extensions: {}, -}; - -const languages = [ - { - code: 'en', - hrefLang: 'en-US', - name: 'English', - localName: 'English', - langDir: 'ltr', - dateFormat: 'MM/DD/YYYY', - }, - { - code: 'es', - hrefLang: 'es', - name: 'Spanish', - localName: 'Español', - langDir: 'ltr', - dateFormat: 'DD-MM-YYYY', - }, - { - code: 'fr', - hrefLang: 'fr', - name: 'French', - localName: 'Français', - langDir: 'ltr', - dateFormat: 'DD-MM-YYYY', - }, - { - code: 'ja', - hrefLang: 'ja', - name: 'Japanese', - localName: '日本語', - langDir: 'ltr', - dateFormat: 'YYYY/MM/DD', - }, -]; - -describe('createCitationPages', () => { - it('Should create cite pages for each avaliable language', () => { - const graphql = cy.stub().resolves(response); - - const createPage = cy.stub(); - - cy.wrap(createCitationPages(graphql, createPage, { languages })).then(() => { - expect(createPage.callCount).to.eq(4); - - cy.wrap(createPage.getCall(0).args[0]).then((page) => { - expect(page.path).contain('/cite/1'); - expect(page.context.locale).eq('en'); - expect(page.context.translate_es).eq(true); - expect(page.context.translate_en).eq(false); - expect(page.context.translate_fr).eq(true); - expect(page.context.translate_ja).eq(true); - expect(page.component).contain('/templates/cite.js'); - }); - - cy.wrap(createPage.getCall(1).args[0]).then((page) => { - expect(page.path).contain('/es/cite/1'); - expect(page.context.locale).eq('es'); - expect(page.context.translate_es).eq(true); - expect(page.context.translate_en).eq(false); - expect(page.context.translate_fr).eq(true); - expect(page.context.translate_ja).eq(true); - }); - - cy.wrap(createPage.getCall(2).args[0]).then((page) => { - expect(page.path).contain('/fr/cite/1'); - expect(page.context.locale).eq('fr'); - expect(page.context.translate_es).eq(true); - expect(page.context.translate_en).eq(false); - expect(page.context.translate_fr).eq(true); - expect(page.context.translate_ja).eq(true); - }); - - cy.wrap(createPage.getCall(3).args[0]).then((page) => { - expect(page.path).contain('/ja/cite/1'); - expect(page.context.locale).eq('ja'); - expect(page.context.translate_es).eq(true); - expect(page.context.translate_en).eq(false); - expect(page.context.translate_fr).eq(true); - expect(page.context.translate_ja).eq(true); - }); - }); - }); -}); diff --git a/site/gatsby-site/cypress/e2e/unit/pageCreators/createDocPages.cy.js b/site/gatsby-site/cypress/e2e/unit/pageCreators/createDocPages.cy.js deleted file mode 100644 index 7175d52d5e..0000000000 --- a/site/gatsby-site/cypress/e2e/unit/pageCreators/createDocPages.cy.js +++ /dev/null @@ -1,78 +0,0 @@ -import createDocPages from '../../../../page-creators/createDocPages'; - -const response = { - data: { - allPrismicDoc: { - edges: [ - { - node: { - data: { - slug: 'doc-1-prismic', - }, - }, - }, - { - node: { - data: { - slug: 'doc-2-prismic', - }, - }, - }, - ], - }, - allFile: { - nodes: [ - { - childMdx: { - frontmatter: { - slug: 'doc-1', - }, - internal: { - contentFilePath: 'doc-1.mdx', - }, - }, - }, - { - childMdx: { - frontmatter: { - slug: 'doc-2', - }, - internal: { - contentFilePath: 'doc-2.mdx', - }, - }, - }, - ], - }, - }, -}; - -describe('createDocPages', () => { - it('Should parse properly', () => { - const graphql = cy.stub().resolves(response); - - const createPage = cy.stub(); - - const reporter = { log: cy.stub() }; - - cy.wrap(createDocPages(graphql, createPage, { reporter })).then(() => { - expect(createPage.callCount).to.eq(4); - - cy.wrap(createPage.getCall(0).args[0]).then((page) => { - expect(page.context.slug).eq('doc-1-prismic'); - }); - - cy.wrap(createPage.getCall(1).args[0]).then((page) => { - expect(page.context.slug).eq('doc-2-prismic'); - }); - - cy.wrap(createPage.getCall(2).args[0]).then((page) => { - expect(page.context.slug).eq('doc-1'); - }); - - cy.wrap(createPage.getCall(3).args[0]).then((page) => { - expect(page.context.slug).eq('doc-2'); - }); - }); - }); -}); diff --git a/site/gatsby-site/cypress/e2e/unit/pageCreators/createEntitiesPages.cy.js b/site/gatsby-site/cypress/e2e/unit/pageCreators/createEntitiesPages.cy.js deleted file mode 100644 index 311089833d..0000000000 --- a/site/gatsby-site/cypress/e2e/unit/pageCreators/createEntitiesPages.cy.js +++ /dev/null @@ -1,244 +0,0 @@ -import createEntitiesPages from '../../../../page-creators/createEntitiesPages'; - -const response = { - data: { - incidents: { - nodes: [ - { - incident_id: 1, - title: 'Incident 1', - Alleged_deployer_of_AI_system: ['ai-developer-1'], - Alleged_developer_of_AI_system: ['ai-developer-1'], - Alleged_harmed_or_nearly_harmed_parties: ['party-1'], - reports: [{ report_number: 1 }, { report_number: 2 }], - }, - { - incident_id: 2, - title: 'Incident 2', - Alleged_deployer_of_AI_system: ['ai-deployer-1'], - Alleged_developer_of_AI_system: ['ai-developer-1'], - Alleged_harmed_or_nearly_harmed_parties: ['party-1', 'party-2'], - reports: [{ report_number: 3 }], - }, - { - incident_id: 3, - title: 'Incident 3', - Alleged_deployer_of_AI_system: ['ai-developer-2', 'ai-deployer-2'], - Alleged_developer_of_AI_system: ['ai-developer-2'], - Alleged_harmed_or_nearly_harmed_parties: ['party-2'], - reports: [{ report_number: 4 }, { report_number: 5 }], - }, - { - incident_id: 4, - title: 'Incident 4', - Alleged_deployer_of_AI_system: ['ai-deployer-3'], - Alleged_developer_of_AI_system: ['ai-developer-1', 'ai-developer-2'], - Alleged_harmed_or_nearly_harmed_parties: ['party-3'], - reports: [{ report_number: 6 }, { report_number: 7 }, { report_number: 8 }], - }, - ], - }, - entities: { - nodes: [ - { - entity_id: 'ai-deployer-1', - name: 'AI Deployer 1', - }, - { - entity_id: 'ai-deployer-2', - name: 'AI Deployer 2', - }, - { - entity_id: 'ai-deployer-3', - name: 'AI Deployer 3', - }, - { - entity_id: 'ai-developer-1', - name: 'AI Developer 1', - }, - { - entity_id: 'ai-developer-2', - name: 'AI Developer 2', - }, - { - entity_id: 'party-1', - name: 'Party 1', - }, - { - entity_id: 'party-2', - name: 'Party 2', - }, - { - entity_id: 'party-3', - name: 'Party 3', - }, - ], - }, - responses: { - nodes: [ - { - report_number: 2, - }, - { - report_number: 3, - }, - { - report_number: 5, - }, - ], - }, - }, -}; - -describe('createEntitiesPages', () => { - it('Should parse properly', () => { - const graphql = cy.stub().resolves(response); - - const createPage = cy.stub(); - - cy.wrap(createEntitiesPages(graphql, createPage)).then(() => { - expect(createPage.callCount).to.eq(8 + 1); - - cy.wrap(createPage.getCall(0).args[0]).then((page) => { - expect(page.context.id).eq('ai-developer-1'); - expect(page.path).eq('/entities/ai-developer-1'); - expect(page.context.name).eq('AI Developer 1'); - expect(page.context.incidentsAsDeployer).to.deep.eq([]); - expect(page.context.incidentsAsDeveloper).to.deep.eq([2, 4]); - expect(page.context.incidentsAsBoth).to.deep.eq([1]); - expect(page.context.incidentsHarmedBy).to.deep.eq([]); - expect(page.context.relatedEntities).to.deep.eq([ - 'party-1', - 'ai-deployer-1', - 'party-2', - 'ai-deployer-3', - 'ai-developer-2', - 'party-3', - ]); - expect(page.context.responses).to.deep.eq([ - { report_number: 2, incident_id: 1 }, - { report_number: 3, incident_id: 2 }, - ]); - }); - - cy.wrap(createPage.getCall(1).args[0]).then((page) => { - expect(page.context.id).eq('party-1'); - expect(page.path).eq('/entities/party-1'); - expect(page.context.name).eq('Party 1'); - expect(page.context.incidentsAsDeployer).to.deep.eq([]); - expect(page.context.incidentsAsDeveloper).to.deep.eq([]); - expect(page.context.incidentsAsBoth).to.deep.eq([]); - expect(page.context.incidentsHarmedBy).to.deep.eq([1, 2]); - expect(page.context.relatedEntities).to.deep.eq([ - 'ai-developer-1', - 'ai-deployer-1', - 'party-2', - ]); - expect(page.context.responses).to.deep.eq([ - { report_number: 2, incident_id: 1 }, - { report_number: 3, incident_id: 2 }, - ]); - }); - - cy.wrap(createPage.getCall(2).args[0]).then((page) => { - expect(page.context.id).eq('ai-deployer-1'); - expect(page.path).eq('/entities/ai-deployer-1'); - expect(page.context.name).eq('AI Deployer 1'); - expect(page.context.incidentsAsDeployer).to.deep.eq([2]); - expect(page.context.incidentsAsDeveloper).to.deep.eq([]); - expect(page.context.incidentsAsBoth).to.deep.eq([]); - expect(page.context.incidentsHarmedBy).to.deep.eq([]); - expect(page.context.relatedEntities).to.deep.eq(['ai-developer-1', 'party-1', 'party-2']); - expect(page.context.responses).to.deep.eq([{ report_number: 3, incident_id: 2 }]); - }); - - cy.wrap(createPage.getCall(3).args[0]).then((page) => { - expect(page.context.id).eq('party-2'); - expect(page.path).eq('/entities/party-2'); - expect(page.context.name).eq('Party 2'); - expect(page.context.incidentsAsDeployer).to.deep.eq([]); - expect(page.context.incidentsAsDeveloper).to.deep.eq([]); - expect(page.context.incidentsAsBoth).to.deep.eq([]); - expect(page.context.incidentsHarmedBy).to.deep.eq([2, 3]); - expect(page.context.relatedEntities).to.deep.eq([ - 'ai-deployer-1', - 'ai-developer-1', - 'party-1', - 'ai-developer-2', - 'ai-deployer-2', - ]); - expect(page.context.responses).to.deep.eq([ - { report_number: 3, incident_id: 2 }, - { report_number: 5, incident_id: 3 }, - ]); - }); - - cy.wrap(createPage.getCall(4).args[0]).then((page) => { - expect(page.context.id).eq('ai-developer-2'); - expect(page.path).eq('/entities/ai-developer-2'); - expect(page.context.name).eq('AI Developer 2'); - expect(page.context.incidentsAsDeployer).to.deep.eq([]); - expect(page.context.incidentsAsDeveloper).to.deep.eq([4]); - expect(page.context.incidentsAsBoth).to.deep.eq([3]); - expect(page.context.incidentsHarmedBy).to.deep.eq([]); - expect(page.context.relatedEntities).to.deep.eq([ - 'ai-deployer-2', - 'party-2', - 'ai-deployer-3', - 'ai-developer-1', - 'party-3', - ]); - expect(page.context.responses).to.deep.eq([{ report_number: 5, incident_id: 3 }]); - }); - - cy.wrap(createPage.getCall(5).args[0]).then((page) => { - expect(page.context.id).eq('ai-deployer-2'); - expect(page.path).eq('/entities/ai-deployer-2'); - expect(page.context.name).eq('AI Deployer 2'); - expect(page.context.incidentsAsDeployer).to.deep.eq([3]); - expect(page.context.incidentsAsDeveloper).to.deep.eq([]); - expect(page.context.incidentsAsBoth).to.deep.eq([]); - expect(page.context.incidentsHarmedBy).to.deep.eq([]); - expect(page.context.relatedEntities).to.deep.eq(['ai-developer-2', 'party-2']); - expect(page.context.responses).to.deep.eq([{ report_number: 5, incident_id: 3 }]); - }); - - cy.wrap(createPage.getCall(6).args[0]).then((page) => { - expect(page.context.id).eq('ai-deployer-3'); - expect(page.path).eq('/entities/ai-deployer-3'); - expect(page.context.name).eq('AI Deployer 3'); - expect(page.context.incidentsAsDeployer).to.deep.eq([4]); - expect(page.context.incidentsAsDeveloper).to.deep.eq([]); - expect(page.context.incidentsAsBoth).to.deep.eq([]); - expect(page.context.incidentsHarmedBy).to.deep.eq([]); - expect(page.context.relatedEntities).to.deep.eq([ - 'ai-developer-1', - 'ai-developer-2', - 'party-3', - ]); - expect(page.context.responses).to.deep.eq([]); - }); - - cy.wrap(createPage.getCall(7).args[0]).then((page) => { - expect(page.context.id).eq('party-3'); - expect(page.path).eq('/entities/party-3'); - expect(page.context.name).eq('Party 3'); - expect(page.context.incidentsAsDeployer).to.deep.eq([]); - expect(page.context.incidentsAsDeveloper).to.deep.eq([]); - expect(page.context.incidentsAsBoth).to.deep.eq([]); - expect(page.context.incidentsHarmedBy).to.deep.eq([4]); - expect(page.context.relatedEntities).to.deep.eq([ - 'ai-deployer-3', - 'ai-developer-1', - 'ai-developer-2', - ]); - expect(page.context.responses).to.deep.eq([]); - }); - - cy.wrap(createPage.getCall(8).args[0]).then((page) => { - expect(page.path).eq('/entities'); - expect(page.context.entities.length).eq(8); - }); - }); - }); -}); diff --git a/site/gatsby-site/cypress/e2e/unit/pageCreators/createReportPages.cy.js b/site/gatsby-site/cypress/e2e/unit/pageCreators/createReportPages.cy.js deleted file mode 100644 index 2e43485d83..0000000000 --- a/site/gatsby-site/cypress/e2e/unit/pageCreators/createReportPages.cy.js +++ /dev/null @@ -1,137 +0,0 @@ -import createReportPages from '../../../../page-creators/createReportPages'; - -const response = { - data: { - reports: { - nodes: [ - { - report_number: 1, - language: 'en', - }, - { - report_number: 2, - language: 'es', - }, - ], - }, - }, -}; - -const languages = [ - { - code: 'en', - hrefLang: 'en-US', - name: 'English', - localName: 'English', - langDir: 'ltr', - dateFormat: 'MM/DD/YYYY', - }, - { - code: 'es', - hrefLang: 'es', - name: 'Spanish', - localName: 'Español', - langDir: 'ltr', - dateFormat: 'DD-MM-YYYY', - }, - { - code: 'fr', - hrefLang: 'fr', - name: 'French', - localName: 'Français', - langDir: 'ltr', - dateFormat: 'DD-MM-YYYY', - }, - { - code: 'ja', - hrefLang: 'ja', - name: 'Japanese', - localName: '日本語', - langDir: 'ltr', - dateFormat: 'YYYY/MM/DD', - }, -]; - -describe('createReportPages', () => { - it('Should parse properly', () => { - const graphql = cy.stub().resolves(response); - - const createPage = cy.stub(); - - cy.wrap(createReportPages(graphql, createPage, { languages })).then(() => { - expect(createPage.callCount).to.eq(8); - - cy.wrap(createPage.getCall(0).args[0]).then((page) => { - expect(page.path).contain('/reports/1/'); - expect(page.context.originalPath).eq('/reports/1/'); - expect(page.context.locale).eq('en'); - expect(page.context.hrefLang).eq('en-US'); - expect(page.context.report_number).eq(1); - expect(page.context.language).eq('en'); - expect(page.context.translate_es).eq(true); - expect(page.context.translate_en).eq(false); - expect(page.context.translate_fr).eq(true); - }); - - cy.wrap(createPage.getCall(1).args[0]).then((page) => { - expect(page.path).contain('/reports/2/'); - expect(page.context.originalPath).eq('/reports/2/'); - expect(page.context.locale).eq('en'); - expect(page.context.hrefLang).eq('en-US'); - expect(page.context.report_number).eq(2); - expect(page.context.language).eq('es'); - expect(page.context.translate_es).eq(false); - expect(page.context.translate_en).eq(true); - expect(page.context.translate_fr).eq(true); - }); - - cy.wrap(createPage.getCall(2).args[0]).then((page) => { - expect(page.path).contain('/es/reports/1/'); - expect(page.context.originalPath).eq('/es/reports/1/'); - expect(page.context.locale).eq('es'); - expect(page.context.hrefLang).eq('es'); - expect(page.context.report_number).eq(1); - expect(page.context.language).eq('en'); - expect(page.context.translate_es).eq(true); - expect(page.context.translate_en).eq(false); - expect(page.context.translate_fr).eq(true); - }); - - cy.wrap(createPage.getCall(3).args[0]).then((page) => { - expect(page.path).contain('/es/reports/2/'); - expect(page.context.originalPath).eq('/es/reports/2/'); - expect(page.context.locale).eq('es'); - expect(page.context.hrefLang).eq('es'); - expect(page.context.report_number).eq(2); - expect(page.context.language).eq('es'); - expect(page.context.translate_es).eq(false); - expect(page.context.translate_en).eq(true); - expect(page.context.translate_fr).eq(true); - }); - - cy.wrap(createPage.getCall(4).args[0]).then((page) => { - expect(page.path).contain('/fr/reports/1/'); - expect(page.context.originalPath).eq('/fr/reports/1/'); - expect(page.context.locale).eq('fr'); - expect(page.context.hrefLang).eq('fr'); - expect(page.context.report_number).eq(1); - expect(page.context.language).eq('en'); - expect(page.context.translate_es).eq(true); - expect(page.context.translate_en).eq(false); - expect(page.context.translate_fr).eq(true); - }); - - cy.wrap(createPage.getCall(5).args[0]).then((page) => { - expect(page.path).contain('/fr/reports/2/'); - expect(page.context.originalPath).eq('/fr/reports/2/'); - expect(page.context.locale).eq('fr'); - expect(page.context.hrefLang).eq('fr'); - expect(page.context.report_number).eq(2); - expect(page.context.language).eq('es'); - expect(page.context.translate_es).eq(false); - expect(page.context.translate_en).eq(true); - expect(page.context.translate_fr).eq(true); - }); - }); - }); -}); diff --git a/site/gatsby-site/cypress/fixtures/api/parseNews.json b/site/gatsby-site/cypress/fixtures/api/parseNews.json deleted file mode 100644 index 73f6fa7fab..0000000000 --- a/site/gatsby-site/cypress/fixtures/api/parseNews.json +++ /dev/null @@ -1,8 +0,0 @@ -{ - "title": "YouTube to crack down on inappropriate content masked as kids’ cartoons", - "authors": "Valentina Palladino", - "date_published": "2017-11-10", - "date_downloaded": "2022-05-26", - "image_url": "https://cdn.arstechnica.net/wp-content/uploads/2017/11/Screen-Shot-2017-11-10-at-9.25.47-AM-760x380.png", - "text": "## Recent news stories and blog\n\nposts _highlighted_ the underbelly of YouTube Kids, Google's children-friendly version. This is more text to reach the 256 charactrs minimum, becuase otherwise the text by similarity component doesnt fetch, which surprisingly is way more character that I initially imagined when I started writing this." -} diff --git a/site/gatsby-site/cypress/fixtures/api/semanticallyRelated.json b/site/gatsby-site/cypress/fixtures/api/semanticallyRelated.json deleted file mode 100644 index 83aea217ac..0000000000 --- a/site/gatsby-site/cypress/fixtures/api/semanticallyRelated.json +++ /dev/null @@ -1,789 +0,0 @@ -{ - "embedding": { - "vector": [ - -0.023964308202266693, - 0.05607481673359871, - -0.00004077623088960536, - -0.11318912357091904, - 0.12186858057975769, - -0.018450608476996422, - -0.004034414421766996, - 0.04273730516433716, - 0.06428910046815872, - -0.1037835106253624, - 0.04839520528912544, - 0.0703505277633667, - 0.015900133177638054, - -0.06311564892530441, - 0.05773233622312546, - -0.06310977786779404, - -0.12136034667491913, - -0.05006597563624382, - 0.02043905109167099, - 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"data": { - "classifications": [ - { - "__typename": "Classification", - "_id": "60dd465f80935bc89e6f9b00", - "publish": true, - "attributes": [ - { - "__typename": "ClassificationAttribute", - "short_name": "Annotator", - "value_json": "\"1\"" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "Annotation Status", - "value_json": "\"6. Complete and final\"" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "Reviewer", - "value_json": "\"5\"" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "Quality Control", - "value_json": "false" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "Full Description", - "value_json": "\"On December 5, 2018, a robot punctured a can of bear spray in Amazon's fulfillment center in Robbinsville, New Jersey. Amazon's spokesman stated that \\\"an automated machine punctured a 9-oz can of bear repellent.\\\" The punctured can released capsaicin, an irritant, into the air. Several dozen workers were exposed to the fumes, causing symptoms including trouble breathing and a burning sensation in the eyes and throat. 24 workers were hospitalized, and one was sent to intensive care and intubated.\"" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "Short Description", - "value_json": "\"Twenty-four Amazon workers in New Jersey were hospitalized after a robot punctured a can of bear repellent spray in a warehouse.\"" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "Beginning Date", - "value_json": "\"2018-12-05T00:00:00.000Z\"" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "Ending Date", - "value_json": "\"2018-12-05T00:00:00.000Z\"" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "Location", - "value_json": "\"Robbinsville, New Jersey, United States of America\"" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "Near Miss", - "value_json": "\"Harm caused\"" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "Named Entities", - "value_json": "[\"Amazon\"]" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "Technology Purveyor", - "value_json": "[\"Amazon\"]" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "Intent", - "value_json": "\"Accident\"" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "Severity", - "value_json": "\"Moderate\"" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "Harm Type", - "value_json": "[\"Harm to physical health/safety\",\"Harm to physical property\"]" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "Lives Lost", - "value_json": "false" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "Harm Distribution Basis", - "value_json": "[]" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "Infrastructure Sectors", - "value_json": "[]" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "Financial Cost", - "value_json": "\"\"" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "Laws Implicated", - "value_json": "[\"Workplace safety laws\",\"OSHA regulations\"]" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "AI System Description", - "value_json": "\"An automated machine operating within an Amazon fulfillment center.\"" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "Data Inputs", - "value_json": "[]" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "System Developer", - "value_json": "[]" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "Sector of Deployment", - "value_json": "[\"Transportation and storage\"]" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "Public Sector Deployment", - "value_json": "false" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "Nature of End User", - "value_json": "\"\"" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "Level of Autonomy", - "value_json": "\"Unclear/unknown\"" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "Relevant AI functions", - "value_json": "[\"Unclear\"]" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "AI Techniques", - "value_json": "[]" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "AI Applications", - "value_json": "[\"robotics\"]" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "Physical System", - "value_json": "[\"Unknown/unclear\"]" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "Problem Nature", - "value_json": "[\"Unknown/unclear\"]" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "Publish", - "value_json": "true" - } - ], - "incidents": [ - { - "incident_id": 2 - } - ], - "namespace": "CSET", - "notes": "{}" - }, - { - "__typename": "Classification", - "_id": "60dd465f80935bc89e6f9b01", - "publish": true, - "attributes": [ - { - "__typename": "ClassificationAttribute", - "short_name": "Annotator", - "value_json": "\"1\"" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "Annotation Status", - "value_json": "\"6. Complete and final\"" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "Reviewer", - "value_json": "\"6\"" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "Quality Control", - "value_json": "false" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "Full Description", - "value_json": "\"A Boeing 737 MAX aircraft equipped with a new, automated flight control system plunged into the Java Sea, killing 189 people on board. On October 29, 2018, shortly after takeoff from Jakarta, Lion Air Flight 610 reported a malfunction in its \\\"angle of attack\\\" sensor. Based on faulty readings from the single sensor, the plane's Maneuvering Characteristics Augmentation System (MCAS) forced the plane's nose down in order to prevent stalling. The pilots pulled the nose back up over twenty times, but the MCAS system counteracted their actions. Eventually, the plane hit the ocean at a speed of several hundred miles per hour. Flight 610 had experienced a similar problem on a previous flight, but in that case, the pilots were able to override MCAS using an emergency procedure. Commentators speculated that the pilots on the day of the fatal crash may have been confused or startled, and that they may not have been fully aware of the MCAS system and how they could override it.\"" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "Short Description", - "value_json": "\"A Boeing 737 crashed into the sea, killing 189 people, after faulty sensor data caused an automated manuevering system to repeatedly push the plane's nose downward.\"" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "Beginning Date", - "value_json": "\"2018-10-29\"" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "Ending Date", - "value_json": "\"2018-10-29\"" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "Location", - "value_json": "\"Java Sea\"" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "Near Miss", - "value_json": "\"Harm caused\"" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "Named Entities", - "value_json": "[\"Lion Air\",\"Boeing\"]" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "Technology Purveyor", - "value_json": "[\"Lion Air\"]" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "Intent", - "value_json": "\"Accident\"" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "Severity", - "value_json": "\"Critical\"" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "Harm Type", - "value_json": "[\"Harm to physical health/safety\",\"Psychological harm\",\"Harm to physical property\",\"Harm to intangible property\"]" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "Lives Lost", - "value_json": "true" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "Harm Distribution Basis", - "value_json": "[]" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "Infrastructure Sectors", - "value_json": "[\"Transportation\"]" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "Financial Cost", - "value_json": "\"\"" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "Laws Implicated", - "value_json": "[\"Federal Aviation Administration regulations\",\"Indonesian air safety regulations\"]" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "AI System Description", - "value_json": "\"The Maneuvering Characteristics Augmentation System (MCAS) is an aircraft maneuvering system that can autonomously steer the plane based on sensor input\"" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "Data Inputs", - "value_json": "[\"Aircraft sensor data\"]" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "System Developer", - "value_json": "[\"Boeing\"]" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "Sector of Deployment", - "value_json": "[\"Transportation and storage\"]" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "Public Sector Deployment", - "value_json": "false" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "Nature of End User", - "value_json": "\"Expert\"" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "Level of Autonomy", - "value_json": "\"High\"" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "Relevant AI functions", - "value_json": "[\"Perception\",\"Cognition\",\"Action\"]" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "AI Techniques", - "value_json": "[]" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "AI Applications", - "value_json": "[\"Aircraft maneuvering\",\"sensor input processing\"]" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "Physical System", - "value_json": "[\"Vehicle/mobile robot\"]" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "Problem Nature", - "value_json": "[\"Robustness\",\"Assurance\"]" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "Publish", - "value_json": null - } - ], - "incidents": [ - { - "incident_id": 3 - } - ], - "namespace": "CSET", - "notes": "" - } - ] - } -} \ No newline at end of file diff --git a/site/gatsby-site/cypress/fixtures/call/resources.json b/site/gatsby-site/cypress/fixtures/call/resources.json deleted file mode 100644 index 0637a088a0..0000000000 --- a/site/gatsby-site/cypress/fixtures/call/resources.json +++ /dev/null @@ -1 +0,0 @@ -[] \ No newline at end of file diff --git a/site/gatsby-site/cypress/fixtures/call/taxa.json b/site/gatsby-site/cypress/fixtures/call/taxa.json deleted file mode 100644 index 3f8ae240c0..0000000000 --- a/site/gatsby-site/cypress/fixtures/call/taxa.json +++ /dev/null @@ -1,6042 +0,0 @@ -{ - "data": { - "taxas": [ - { - "__typename": "Taxa", - "description": "# What is the CSET Taxonomy?\n\nThe Center for Security and Emerging Technology (CSET) taxonomy is a general taxonomy of AI incidents. There are a large number of classified attributes, including ones pertaining to safety, fairness, industry, geography, timing, and cost.All classifications within the CSET taxonomy are first applied by one CSET annotator and reviewed by another CSET annotator before the classifications are finalized. The combination of a rigorously defined coding set and the completeness with which it has been applied make the CSET taxonomy the AIID's gold standard for taxonomies. Nevertheless, the CSET taxonomy is an ongoing effort and you are invited to report any errors you may discover in its application.\n\n## How do I explore the taxonomy?\n\nAll taxonomies can be used to filter incident reports within the [Discover Application](/apps/discover). The taxonomy filters work similarly to how you filter products on an E-commerce website. Use the search field at the bottom of the “Classifications” tab to find the taxonomy field you would like to filter with, then click the desired value to apply the filter.\n\n# About CSET\n\nA policy research organization within Georgetown University’s Walsh School of Foreign Service, CSET produces data-driven research at the intersection of security and technology, providing nonpartisan analysis to the policy community. CSET is currently focusing on the effects of progress in artificial intelligence (AI), advanced computing and biotechnology. CSET seeks to prepare a new generation of decision-makers to address the challenges and opportunities of emerging technologies. [(Read more)](https://cset.georgetown.edu/about-us/).", - "field_list": [ - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "enum", - "field_number": null, - "instant_facet": false, - "long_description": "An ID designating the individual who classified this incident according to the CSET taxonomy.", - "long_name": "Person responsible for the annotations", - "mongo_type": "string", - "permitted_values": [ - "1", - "2", - "3", - "4", - "5", - "6", - "7", - "8", - "9", - "Other" - ], - "placeholder": "Select name here", - "public": false, - "required": false, - "short_description": "This is the researcher that is responsible for applying the classifications of the CSET taxonomy.", - "short_name": "Annotator", - "weight": 5 - }, - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "enum", - "field_number": null, - "instant_facet": false, - "long_description": "What is the quality assurance status of the CSET classifications for this incident?", - "long_name": "Where in the annotation process is this incident?", - "mongo_type": "string", - "permitted_values": [ - "1. Annotation in progress", - "2. Initial annotation complete", - "3. In peer review", - "4. Peer review complete", - "5. In quality control", - "6. Complete and final" - ], - "placeholder": "Select process status here", - "public": false, - "required": false, - "short_description": "What is the quality assurance status of the CSET classifications for this incident?", - "short_name": "Annotation Status", - "weight": 10 - }, - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "enum", - "field_number": null, - "instant_facet": false, - "long_description": "The CSET taxonomy assigns individual researchers to each incident as the primary parties responsible for classifying the incident according to the taxonomy. This is the person responsible for assuring the integrity of annotator's classifications.", - "long_name": "Person responsible for reviewing annotations", - "mongo_type": "string", - "permitted_values": [ - "1", - "2", - "3", - "4", - "5", - "6", - "7", - "8", - "9", - "Other" - ], - "placeholder": "Select name here", - "public": false, - "required": false, - "short_description": "This is the researcher that is responsible for ensuring the quality of the classifications applied to this incident.", - "short_name": "Reviewer", - "weight": 20 - }, - { - "__typename": "TaxaField_list", - "default": "false", - "display_type": "bool", - "field_number": null, - "instant_facet": false, - "long_description": "The peer review process sometimes uncovers issues with the classifications that have been applied by the annotator. This field serves as a flag when there is a need for additional thought and input on the classifications applied", - "long_name": "Was this incident randomly selected for additional quality control?", - "mongo_type": "bool", - "permitted_values": [], - "placeholder": "", - "public": false, - "required": false, - "short_description": "Has someone flagged a potential issue with this incident's classifications?", - "short_name": "Quality Control", - "weight": 15 - }, - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "string", - "field_number": null, - "instant_facet": false, - "long_description": "A plain-language description of the incident in one paragraph or less.", - "long_name": "Full description of the incident", - "mongo_type": "string", - "permitted_values": [], - "placeholder": "Describe the incident here", - "public": null, - "required": false, - "short_description": "A plain-language description of the incident in one paragraph or less.", - "short_name": "Full Description", - "weight": 160 - }, - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "string", - "field_number": null, - "instant_facet": false, - "long_description": "A one-sentence description of the incident.", - "long_name": "Short description of the incident", - "mongo_type": "string", - "permitted_values": [], - "placeholder": "Describe the incident here", - "public": null, - "required": false, - "short_description": "A one-sentence description of the incident.", - "short_name": "Short Description", - "weight": 155 - }, - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "date", - "field_number": null, - "instant_facet": false, - "long_description": "The date the incident began.", - "long_name": "Beginning date", - "mongo_type": "date", - "permitted_values": [], - "placeholder": "", - "public": null, - "required": false, - "short_description": "The date the incident began.", - "short_name": "Beginning Date", - "weight": 90 - }, - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "date", - "field_number": null, - "instant_facet": false, - "long_description": "The date the incident ended.", - "long_name": "Ending date", - "mongo_type": "date", - "permitted_values": [], - "placeholder": "", - "public": null, - "required": false, - "short_description": "The date the incident ended.", - "short_name": "Ending Date", - "weight": 85 - }, - { - "__typename": "TaxaField_list", - "default": "global", - "display_type": "location", - "field_number": null, - "instant_facet": true, - "long_description": "The location or locations where the incident played out.", - "long_name": "Location", - "mongo_type": "string", - "permitted_values": [], - "placeholder": "Input a named place as it could be found in Google maps", - "public": null, - "required": false, - "short_description": "The location or locations where the incident played out.", - "short_name": "Location", - "weight": 105 - }, - { - "__typename": "TaxaField_list", - "default": "Harm caused", - "display_type": "enum", - "field_number": null, - "instant_facet": true, - "long_description": "Was harm caused, or was it a near miss?", - "long_name": "Harm nearly missed?", - "mongo_type": "string", - "permitted_values": [ - "Unclear/unknown", - "Near miss", - "Harm caused" - ], - "placeholder": "", - "public": null, - "required": false, - "short_description": "Was harm caused, or was it a near miss?", - "short_name": "Near Miss", - "weight": 80 - }, - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "list", - "field_number": null, - "instant_facet": true, - "long_description": "All named entities (such as people, organizations, locations, and products - generally proper nouns) that seem to have a significant relationship with this event, as indicated by the available evidence.", - "long_name": "Named entities", - "mongo_type": "array", - "permitted_values": [], - "placeholder": "", - "public": null, - "required": false, - "short_description": "All named entities (such as people, organizations, locations, and products - generally proper nouns) that seem to have a significant relationship with this event, as indicated by the available evidence.", - "short_name": "Named Entities", - "weight": 100 - }, - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "list", - "field_number": null, - "instant_facet": true, - "long_description": "A list of parties (up to three) that were responsible for the relevant AI tool or system, i.e. that had operational control over the AI-related system causing harm (or control over those who did).", - "long_name": "Party responsible for AI system", - "mongo_type": "array", - "permitted_values": [], - "placeholder": "", - "public": null, - "required": false, - "short_description": "A list of parties (up to three) that were responsible for the relevant AI tool or system, i.e. that had operational control over the AI-related system causing harm (or control over those who did).", - "short_name": "Technology Purveyor", - "weight": 95 - }, - { - "__typename": "TaxaField_list", - "default": "Accident", - "display_type": "enum", - "field_number": null, - "instant_facet": true, - "long_description": "Indicates whether the incident was deliberate/expected or accidental, based on the available evidence. \"Deliberate or expected\" applies if it is established or highly likely that the system acted more or less as expected, from the perspective of at least one of the people or entities responsible for it. “Accident” applies if it is established or highly likely that the harm arose from the system acting in an unexpected way. \"Unclear\" applies if the evidence is contradictory or too thin to apply either of the above labels.", - "long_name": "Probable level of intent", - "mongo_type": "string", - "permitted_values": [ - "Accident", - "Deliberate or expected", - "Unclear" - ], - "placeholder": "Accident", - "public": null, - "required": false, - "short_description": "Was the incident an accident, intentional, or is the intent unclear?", - "short_name": "Intent", - "weight": 75 - }, - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "enum", - "field_number": null, - "instant_facet": true, - "long_description": "An estimate of the overall severity of harm caused. \"Negligible\" harm means minor inconvenience or expense, easily remedied. “Minor” harm means limited damage to property, social stability, the political system, or civil liberties occurred or nearly occurred. \"Moderate\" harm means that humans were injured (but not killed) or nearly injured, or that financial, property, social, or political interests or civil liberties were materially affected (or nearly so affected). \"Severe\" harm means that a small number of humans were or were almost gravely injured or killed, or that financial, property, social, or political interests or civil liberties were significantly disrupted at at least a regional or national scale (or nearly so disrupted). \"Critical\" harm means that many humans were or were almost killed, or that financial, property, social, or political interests were seriously disrupted at a national or global scale (or nearly so disrupted).", - "long_name": "Overall severity of harm", - "mongo_type": "string", - "permitted_values": [ - "Negligible", - "Minor", - "Moderate", - "Severe", - "Critical", - "Unclear/unknown" - ], - "placeholder": "", - "public": null, - "required": false, - "short_description": "How bad is the harm for the most affected person or organization?", - "short_name": "Severity", - "weight": 150 - }, - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "multi", - "field_number": null, - "instant_facet": true, - "long_description": "Indicates the type(s) of harm caused or nearly caused by the incident.", - "long_name": "Harm type", - "mongo_type": "array", - "permitted_values": [ - "Harm to physical health/safety", - "Psychological harm", - "Financial harm", - "Harm to physical property", - "Harm to intangible property", - "Harm to social or political systems", - "Harm to civil liberties", - "Other" - ], - "placeholder": "", - "public": null, - "required": false, - "short_description": "Indicates the type(s) of harm caused or nearly caused by the incident.", - "short_name": "Harm Type", - "weight": 140 - }, - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "bool", - "field_number": null, - "instant_facet": true, - "long_description": "Marked \"trur\" if one or more people died as a result of the accident, \"false\" if there is no evidence of lives being lost, \"unclear\" otherwise.", - "long_name": "Human lives lost", - "mongo_type": "bool", - "permitted_values": [], - "placeholder": "", - "public": null, - "required": false, - "short_description": "Were human lives lost as a result of the incident?", - "short_name": "Lives Lost", - "weight": 70 - }, - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "multi", - "field_number": null, - "instant_facet": true, - "long_description": "If harms were unevenly distributed, this field indicates the basis or bases on which they were unevenly distributed.", - "long_name": "Uneven distribution of harms basis", - "mongo_type": "array", - "permitted_values": [ - "Race", - "Religion", - "National origin or immigrant status", - "Geography", - "Age", - "Sex", - "Sexual orientation or gender identity", - "Familial status or pregnancy", - "Disability", - "Veteran status", - "Genetic information", - "Financial means", - "Ideology", - "Other" - ], - "placeholder": "", - "public": null, - "required": false, - "short_description": "If harms were unevenly distributed, this field indicates the basis or bases on which they were unevenly distributed.", - "short_name": "Harm Distribution Basis", - "weight": 145 - }, - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "multi", - "field_number": null, - "instant_facet": true, - "long_description": "Where applicable, this field indicates if the incident caused harm to any of the economic sectors designated by the U.S. government as critical infrastructure.", - "long_name": "Critical infrastructure sectors affected", - "mongo_type": "array", - "permitted_values": [ - "Chemical", - "Commercial facilities", - "Communications", - "Critical manufacturing", - "Dams", - "Defense-industrial base", - "Emergency services", - "Energy", - "Financial services", - "Food and agriculture", - "Government facilities", - "Healthcare and public health", - "Information technology", - "Nuclear", - "Transportation", - "Water and wastewater" - ], - "placeholder": "", - "public": null, - "required": false, - "short_description": "Where applicable, this field indicates if the incident caused harm to any of the economic sectors designated by the U.S. government as critical infrastructure.", - "short_name": "Infrastructure Sectors", - "weight": 65 - }, - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "string", - "field_number": null, - "instant_facet": false, - "long_description": "The stated or estimated financial cost of the incident, if reported.", - "long_name": "Total financial cost", - "mongo_type": "string", - "permitted_values": [], - "placeholder": "", - "public": null, - "required": false, - "short_description": "The stated or estimated financial cost of the incident, if reported.", - "short_name": "Financial Cost", - "weight": 60 - }, - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "list", - "field_number": null, - "instant_facet": false, - "long_description": "Relevant laws under which entities involved in the incident may face legal liability as a result of the incident.", - "long_name": "Laws covering the incident", - "mongo_type": "array", - "permitted_values": [], - "placeholder": "", - "public": null, - "required": false, - "short_description": "Relevant laws under which entities involved in the incident may face legal liability as a result of the incident.", - "short_name": "Laws Implicated", - "weight": 55 - }, - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "string", - "field_number": null, - "instant_facet": false, - "long_description": "A brief description of the AI system(s) involved in the incident, including the system’s intended function, the context in which it was deployed, and any available details about the algorithms, hardware, and training data involved in the system.", - "long_name": "Description of AI system involved", - "mongo_type": "string", - "permitted_values": [], - "placeholder": "Describe the AI system here", - "public": null, - "required": false, - "short_description": "A brief description of the AI system(s) involved in the incident, including the system’s intended function, the context in which it was deployed, and any available details about the algorithms, hardware, and training data involved in the system.", - "short_name": "AI System Description", - "weight": 135 - }, - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "list", - "field_number": null, - "instant_facet": false, - "long_description": "A brief description of the data that the AI system(s) used or were trained on.", - "long_name": "Description of the data inputs to the AI systems", - "mongo_type": "array", - "permitted_values": [], - "placeholder": "Describe the AI system here", - "public": null, - "required": false, - "short_description": "A brief description of the data that the AI system(s) used or were trained on.", - "short_name": "Data Inputs", - "weight": 50 - }, - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "list", - "field_number": null, - "instant_facet": true, - "long_description": "The entity that created the AI system.", - "long_name": "System developer", - "mongo_type": "array", - "permitted_values": [], - "placeholder": "", - "public": null, - "required": false, - "short_description": "The entity that created the AI system.", - "short_name": "System Developer", - "weight": 130 - }, - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "multi", - "field_number": null, - "instant_facet": true, - "long_description": "The primary economic sector in which the AI system(s) involved in the incident were operating.", - "long_name": "Sector of deployment", - "mongo_type": "array", - "permitted_values": [ - "Manufacturing", - "Electricity, gas, steam and air conditioning supply", - "Water supply", - "Construction", - "Wholesale and retail trade", - "Transportation and storage", - "Accommodation and food service activities", - "Information and communication", - "Financial and insurance activities", - "Real estate activities", - "Professional, scientific and technical activities", - "Administrative and support service activities", - "Public administration and defence", - "Education", - "Human health and social work activities", - "Arts, entertainment and recreation", - "Other service activities", - "Activities of households as employers", - "Activities of extraterritorial organizations and bodies" - ], - "placeholder": "", - "public": null, - "required": false, - "short_description": "The primary economic sector in which the AI system(s) involved in the incident were operating.", - "short_name": "Sector of Deployment", - "weight": 125 - }, - { - "__typename": "TaxaField_list", - "default": "false", - "display_type": "bool", - "field_number": null, - "instant_facet": true, - "long_description": "\"Yes\" if the AI system(s) involved in the accident were being used by the public sector or for the administration of public goods (for example, public transportation). \"No\" if the system(s) were being used in the private sector or for commercial purposes (for example, a ride-sharing company), on the other.", - "long_name": "Public sector deployment", - "mongo_type": "bool", - "permitted_values": [], - "placeholder": "", - "public": null, - "required": false, - "short_description": "\"Yes\" if the AI system(s) involved in the accident were being used by the public sector or for the administration of public goods (for example, public transportation). \"No\" if the system(s) were being used in the private sector or for commercial purposes (for example, a ride-sharing company), on the other.", - "short_name": "Public Sector Deployment", - "weight": 45 - }, - { - "__typename": "TaxaField_list", - "default": "Accident", - "display_type": "enum", - "field_number": null, - "instant_facet": true, - "long_description": "\"Expert\" if users with special training or technical expertise were the ones meant to benefit from the AI system(s)’ operation; \"Amateur\" if the AI systems were primarily meant to benefit the general public or untrained users.", - "long_name": "Nature of end user", - "mongo_type": "string", - "permitted_values": [ - "Expert", - "Amateur" - ], - "placeholder": "Accident", - "public": null, - "required": false, - "short_description": "\"Expert\" if users with special training or technical expertise were the ones meant to benefit from the AI system(s)’ operation; \"Amateur\" if the AI systems were primarily meant to benefit the general public or untrained users.", - "short_name": "Nature of End User", - "weight": 40 - }, - { - "__typename": "TaxaField_list", - "default": "Accident", - "display_type": "enum", - "field_number": null, - "instant_facet": true, - "long_description": "The degree to which the AI system(s) functions independently from human intervention. \"High\" means there is no human involved in the system action execution; \"Medium\" means the system generates a decision and a human oversees the resulting action; \"low\" means the system generates decision-support output and a human makes a decision and executes an action.", - "long_name": "Level of autonomy", - "mongo_type": "string", - "permitted_values": [ - "High", - "Medium", - "Low" - ], - "placeholder": "Accident", - "public": null, - "required": false, - "short_description": "The degree to which the AI system(s) functions independently from human intervention. \"High\" means there is no human involved in the system action execution; \"Medium\" means the system generates a decision and a human oversees the resulting action; \"low\" means the system generates decision-support output and a human makes a decision and executes an action.", - "short_name": "Level of Autonomy", - "weight": 35 - }, - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "multi", - "field_number": null, - "instant_facet": true, - "long_description": "Indicates whether the AI system(s) were intended to perform any of the following high-level functions: \"Perception,\" i.e. sensing and understanding the environment; \"Cognition,\" i.e. making decisions; or \"Action,\" i.e. carrying out decisions through physical or digital means.", - "long_name": "Relevant AI functions", - "mongo_type": "array", - "permitted_values": [ - "Perception", - "Cognition", - "Action", - "Unclear" - ], - "placeholder": "", - "public": null, - "required": false, - "short_description": "Indicates whether the AI system(s) were intended to perform any of the following high-level functions: \"Perception,\" i.e. sensing and understanding the environment; \"Cognition,\" i.e. making decisions; or \"Action,\" i.e. carrying out decisions through physical or digital means.", - "short_name": "Relevant AI functions", - "weight": 120 - }, - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "list", - "field_number": null, - "instant_facet": true, - "long_description": "Open-ended tags that indicate the hardware and software involved in the AI system(s).", - "long_name": "AI tools and techniques used", - "mongo_type": "array", - "permitted_values": [], - "placeholder": "", - "public": null, - "required": false, - "short_description": "Open-ended tags that indicate the hardware and software involved in the AI system(s).", - "short_name": "AI Techniques", - "weight": 115 - }, - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "list", - "field_number": null, - "instant_facet": true, - "long_description": "Open-ended tags that describe the functions and applications of the AI system.", - "long_name": "AI functions and applications used", - "mongo_type": "array", - "permitted_values": [], - "placeholder": "", - "public": null, - "required": false, - "short_description": "Open-ended tags that describe the functions and applications of the AI system.", - "short_name": "AI Applications", - "weight": 110 - }, - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "multi", - "field_number": null, - "instant_facet": true, - "long_description": "Where relevant, indicates whether the AI system(s) was embedded into or tightly associated with specific types of hardware.", - "long_name": "Physical system", - "mongo_type": "array", - "permitted_values": [ - "Consumer device", - "Industrial process system", - "Weapons system", - "Vehicle/mobile robot", - "Software only", - "Unknown/unclear" - ], - "placeholder": "", - "public": null, - "required": false, - "short_description": "Where relevant, indicates whether the AI system(s) was embedded into or tightly associated with specific types of hardware.", - "short_name": "Physical System", - "weight": 30 - }, - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "multi", - "field_number": null, - "instant_facet": true, - "long_description": "Indicates which, if any, of the following types of AI failure describe the incident: \"Specification,\" i.e. the system's behavior did not align with the true intentions of its designer, operator, etc; \"Robustness,\" i.e. the system operated unsafely because of features or changes in its environment, or in the inputs the system received; \"Assurance,\" i.e. the system could not be adequately monitored or controlled during operation.", - "long_name": "Causative factors within AI system", - "mongo_type": "array", - "permitted_values": [ - "Specification", - "Robustness", - "Assurance", - "Unknown/unclear" - ], - "placeholder": "", - "public": null, - "required": false, - "short_description": "Indicates which, if any, of the following types of AI failure describe the incident: \"Specification,\" i.e. the system's behavior did not align with the true intentions of its designer, operator, etc; \"Robustness,\" i.e. the system operated unsafely because of features or changes in its environment, or in the inputs the system received; \"Assurance,\" i.e. the system could not be adequately monitored or controlled during operation.", - "short_name": "Problem Nature", - "weight": 25 - }, - { - "__typename": "TaxaField_list", - "default": null, - "display_type": "bool", - "field_number": null, - "instant_facet": null, - "long_description": null, - "long_name": null, - "mongo_type": "bool", - "permitted_values": null, - "placeholder": null, - "public": null, - "required": null, - "short_description": null, - "short_name": "Publish", - "weight": null - } - ], - "namespace": "CSET", - "weight": 70 - }, - { - "__typename": "Taxa", - "description": "# What is the CSET Taxonomy?\n\nThe Center for Security and Emerging Technology (CSET) taxonomy is\na general taxonomy of AI incidents. There are a large number of \nclassified attributes, including ones pertaining to safety, fairness, \nindustry, geography, timing, and cost. All classifications within \nthe CSET taxonomy are first applied by one CSET annotator and \nreviewed by another CSET annotator before the classifications are \nfinalized. The combination of a rigorously defined coding set and \nthe completeness with which it has been applied make the CSET \ntaxonomy the AIID's gold standard for taxonomies. Nevertheless, \nthe CSET taxonomy is an ongoing effort and you are invited to \nreport any errors you may discover in its application.\n\n## How do I explore the taxonomy?\n\nAll taxonomies can be used to filter incident reports within the \n[Discover Application](/apps/discover). The taxonomy filters work\nsimilarly to how you filter products on an E-commerce website. \nUse the search field at the bottom of the “Classifications” tab to \nfind the taxonomy field you would like to filter with, then click \nthe desired value to apply the filter.\n\n# About CSET\n\nA policy research organization within Georgetown University’s \nWalsh School of Foreign Service, CSET produces data-driven \nresearch at the intersection of security and technology, \nproviding nonpartisan analysis to the policy community. CSET is \ncurrently focusing on the effects of progress in artificial \nintelligence (AI), advanced computing and biotechnology. CSET \nseeks to prepare a new generation of decision-makers to address \nthe challenges and opportunities of emerging technologies. [(Read \nmore)](https://cset.georgetown.edu/about-us/).", - "field_list": [ - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "int", - "field_number": "1.1", - "instant_facet": false, - "long_description": "The number of the incident in the AI Incident Database.", - "long_name": "The number of the incident in the AI Incident Database.", - "mongo_type": "int", - "permitted_values": null, - "placeholder": null, - "public": true, - "required": false, - "short_description": "The number of the incident in the AI Incident Database.", - "short_name": "Incident Number", - "weight": 5 - }, - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "string", - "field_number": "1.2", - "instant_facet": false, - "long_description": "An ID designating the individual who classified this incident according to the CSET taxonomy.", - "long_name": "Person responsible for the annotations", - "mongo_type": "string", - "permitted_values": [], - "placeholder": "Select name here", - "public": false, - "required": false, - "short_description": "This is the researcher that is responsible for applying the classifications of the CSET taxonomy.", - "short_name": "Annotator", - "weight": 5 - }, - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "enum", - "field_number": "1.3", - "instant_facet": false, - "long_description": "What is the quality assurance status of the CSET classifications for this incident?", - "long_name": "Where in the annotation process is this incident?", - "mongo_type": "string", - "permitted_values": [ - "1. Annotation in progress", - "2. Initial annotation complete", - "3. In peer review", - "4. Peer review complete", - "5. In quality control", - "6. Complete and final" - ], - "placeholder": "Select process status here", - "public": false, - "required": false, - "short_description": "What is the quality assurance status of the CSET classifications for this incident?", - "short_name": "Annotation Status", - "weight": 10 - }, - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "string", - "field_number": "1.4", - "instant_facet": false, - "long_description": "The CSET taxonomy assigns individual researchers to each incident as the primary parties responsible for classifying the incident according to the taxonomy. This is the person responsible for assuring the integrity of annotator's classifications.", - "long_name": "Person responsible for reviewing annotations", - "mongo_type": "string", - "permitted_values": [], - "placeholder": "Select name here", - "public": false, - "required": false, - "short_description": "This is the researcher that is responsible for ensuring the quality of the classifications applied to this incident.", - "short_name": "Peer Reviewer", - "weight": 20 - }, - { - "__typename": "TaxaField_list", - "default": "false", - "display_type": "bool", - "field_number": "1.5", - "instant_facet": false, - "long_description": "The peer review process sometimes uncovers issues with the classifications that have been applied by the annotator. This field serves as a flag when there is a need for additional thought and input on the classifications applied", - "long_name": "Was this incident randomly selected for additional quality control?", - "mongo_type": "bool", - "permitted_values": [], - "placeholder": "", - "public": false, - "required": false, - "short_description": "Has someone flagged a potential issue with this incident's classifications? Annotators should leave this field blank.", - "short_name": "Quality Control", - "weight": 15 - }, - { - "__typename": "TaxaField_list", - "default": "maybe", - "display_type": "enum", - "field_number": "2.1", - "instant_facet": true, - "long_description": "“Yes” if the AI system(s) is embedded in hardware that can interact with, affect, and change the physical objects (cars, robots, medical facilities, etc.). Mark “No” if the system cannot. This includes systems that inform, detect, predict, or recommend.", - "long_name": "Did the incident occur in a domain with physical objects ?", - "mongo_type": "string", - "permitted_values": [ - "yes", - "no", - "maybe" - ], - "placeholder": "", - "public": null, - "required": false, - "short_description": "Did the incident occur in a domain with physical objects ?", - "short_name": "Physical Objects", - "weight": 50 - }, - { - "__typename": "TaxaField_list", - "default": "maybe", - "display_type": "enum", - "field_number": "2.2", - "instant_facet": true, - "long_description": "“Yes” if the sector in which the AI was used is associated with entertainment. “No” if it was used in a different, clearly identifiable sector. “Maybe” if the sector of use could not be determined.", - "long_name": "Did the AI incident occur in the entertainment industry?", - "mongo_type": "string", - "permitted_values": [ - "yes", - "no", - "maybe" - ], - "placeholder": "", - "public": null, - "required": false, - "short_description": "Did the AI incident occur in the entertainment industry?", - "short_name": "Entertainment Industry", - "weight": 50 - }, - { - "__typename": "TaxaField_list", - "default": "maybe", - "display_type": "enum", - "field_number": "2.3", - "instant_facet": true, - "long_description": "“Yes” if the incident is about a report, test, or study of the data and does not discuss an instance of injury, damage, or loss. “Maybe” if it is unclear. Otherwise mark “No.”", - "long_name": "Was the incident about a report, test, or study of data instead of the AI itself?", - "mongo_type": "string", - "permitted_values": [ - "yes", - "no", - "maybe" - ], - "placeholder": "", - "public": null, - "required": false, - "short_description": "Was the incident about a report, test, or study of data instead of the AI itself?", - "short_name": "Report, Test, or Study of data", - "weight": 50 - }, - { - "__typename": "TaxaField_list", - "default": "maybe", - "display_type": "enum", - "field_number": "2.4", - "instant_facet": true, - "long_description": "“Yes” if the involved system was deployed or sold to users. “No” if it was not. “Maybe” if there is not enough information or if the use is unclear.", - "long_name": "Was the reported system (even if AI involvement is unknown) deployed or sold to users?", - "mongo_type": "string", - "permitted_values": [ - "yes", - "no", - "maybe" - ], - "placeholder": "", - "public": null, - "required": false, - "short_description": "Was the reported system (even if AI involvement is unknown) deployed or sold to users?", - "short_name": "Deployed", - "weight": 50 - }, - { - "__typename": "TaxaField_list", - "default": "maybe", - "display_type": "enum", - "field_number": "2.5", - "instant_facet": true, - "long_description": "“Yes” if it was a test/demonstration performed by developers, producers or journalists in controlled conditions. “No” if it was not a test/demonstration. “No” if the test/demonstration was done by a user. “No” if the test/demonstration was in operational or uncontrolled conditions. “Maybe” otherwise.", - "long_name": "Was this a test or demonstration of an AI system done by developers, producers or researchers (versus users) in controlled conditions?", - "mongo_type": "string", - "permitted_values": [ - "yes", - "no", - "maybe" - ], - "placeholder": "", - "public": null, - "required": false, - "short_description": "Was this a test or demonstration of an AI system done by developers, producers or researchers (versus users) in controlled conditions?", - "short_name": "Producer Test in Controlled Conditions", - "weight": 50 - }, - { - "__typename": "TaxaField_list", - "default": "maybe", - "display_type": "enum", - "field_number": "2.6", - "instant_facet": true, - "long_description": "“Yes” if it was a test/demonstration performed by developers, producers or journalists in controlled conditions. “No” if it was not a test/demonstration. “No” if the test/demonstration was done by a user. “No” if the test/demonstration was in controlled or non-operational conditions. “Maybe” otherwise.", - "long_name": "Was this a test or demonstration of an AI system done by developers, producers or researchers (versus users) in operational conditions?", - "mongo_type": "string", - "permitted_values": [ - "yes", - "no", - "maybe" - ], - "placeholder": "", - "public": null, - "required": false, - "short_description": "Was this a test or demonstration of an AI system done by developers, producers or researchers (versus users) in operational conditions?", - "short_name": "Producer Test in Operational Conditions", - "weight": 50 - }, - { - "__typename": "TaxaField_list", - "default": "maybe", - "display_type": "enum", - "field_number": "2.7", - "instant_facet": true, - "long_description": "“Yes” if it was a test/demonstration performed by users in controlled conditions. “No” if it was not a test/demonstration. “No” if the test/demonstration was done by developers, producers or researchers. “No” if the test/demonstration was in controlled or non-controlled conditions.“Maybe” otherwise.", - "long_name": "Was this a test or demonstration done by users in controlled conditions?", - "mongo_type": "string", - "permitted_values": [ - "yes", - "no", - "maybe" - ], - "placeholder": "", - "public": null, - "required": false, - "short_description": "Was this a test or demonstration done by users in controlled conditions?", - "short_name": "User Test in Controlled Conditions", - "weight": 50 - }, - { - "__typename": "TaxaField_list", - "default": "maybe", - "display_type": "enum", - "field_number": "2.8", - "instant_facet": true, - "long_description": "“Yes” if it was a test/demonstration performed by users in operational conditions. “No” if it was not a test/demonstration. “No” if the test/demonstration was done by developers, producers or researchers. “No” if the test/demonstration was in controlled or non-operational conditions.“Maybe” otherwise.", - "long_name": "Was this a test or demonstration done by users in operational conditions?", - "mongo_type": "string", - "permitted_values": [ - "yes", - "no", - "maybe" - ], - "placeholder": "", - "public": null, - "required": false, - "short_description": "Was this a test or demonstration done by users in operational conditions?", - "short_name": "User Test in Operational Conditions", - "weight": 50 - }, - { - "__typename": "TaxaField_list", - "default": "maybe", - "display_type": "enum", - "field_number": "2.9", - "instant_facet": false, - "long_description": "Using the answers to the 8 domain questions, assess if the incident occurred in a domain where harm could be expected to occur. If you are unclear, input “maybe.”", - "long_name": "Incident occurred in a domain where we could expect harm to occur?", - "mongo_type": "string", - "permitted_values": [ - "yes", - "no", - "maybe" - ], - "placeholder": "", - "public": null, - "required": false, - "short_description": "Incident occurred in a domain where we could likely expect harm to occur?", - "short_name": "Harm Domain", - "weight": 50 - }, - { - "__typename": "TaxaField_list", - "default": "unclear", - "display_type": "enum", - "field_number": "3.1", - "instant_facet": true, - "long_description": "An assessment of whether tangible harm, imminent tangible harm, or non-imminent tangible harm occurred. This assessment does not consider the context of the tangible harm, if an AI was involved, or if there is an identifiable, specific, and harmed entity. It is also not assessing if an intangible harm occurred. It is only asking if tangible harm occurred and what its imminency was.", - "long_name": "Did tangible harm (loss, damage or injury ) occur? ", - "mongo_type": "string", - "permitted_values": [ - "tangible harm definitively occurred", - "imminent risk of tangible harm (near miss) did occur", - "non-imminent risk of tangible harm (an issue) occurred", - "no tangible harm, near-miss, or issue", - "unclear" - ], - "placeholder": "", - "public": true, - "required": false, - "short_description": "Did tangible harm (loss, damage or injury ) occur? ", - "short_name": "Tangible Harm", - "weight": 50 - }, - { - "__typename": "TaxaField_list", - "default": "maybe", - "display_type": "enum", - "field_number": "3.2", - "instant_facet": true, - "long_description": "An assessment of whether or not an AI system was involved. It is sometimes difficult to judge between an AI and an automated system or expert rules system. In these cases select “maybe”", - "long_name": "Does the incident involve an AI system?", - "mongo_type": "string", - "permitted_values": [ - "yes", - "no", - "maybe" - ], - "placeholder": "", - "public": true, - "required": false, - "short_description": "Does the incident involve an AI system?", - "short_name": "AI System", - "weight": 50 - }, - { - "__typename": "TaxaField_list", - "default": "maybe", - "display_type": "enum", - "field_number": "3.3", - "instant_facet": true, - "long_description": "“Yes” if an AI was involved in harm, its behavior can be directly linked to the harm, and the harm may not have occurred if the AI acted differently. “Maybe” if the link is unclear. Otherwise, select “no.”", - "long_name": "Can an AI be directly and clearly linked to tangible harm?", - "mongo_type": "string", - "permitted_values": [ - "yes", - "no", - "maybe" - ], - "placeholder": "", - "public": true, - "required": false, - "short_description": "Can an AI be directly and clearly linked to tangible harm?", - "short_name": "Clear Link to AI", - "weight": 50 - }, - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "bool", - "field_number": "3.4", - "instant_facet": true, - "long_description": "“Yes” if it is theoretically possible to both specify and identify the entity. Having that information is not required. The information just needs to exist and be potentially discoverable. “No” if there are not any potentially identifiable specific entities or if the harmed entities are a class or subgroup that can only be characterized. ", - "long_name": "There is a potentially identifiable specific entity that experienced the harm", - "mongo_type": "bool", - "permitted_values": [], - "placeholder": "", - "public": true, - "required": false, - "short_description": "A potentially identifiable specific entity that experienced the harm can be characterized or identified.", - "short_name": "There is a potentially identifiable specific entity that experienced the harm", - "weight": 50 - }, - { - "__typename": "TaxaField_list", - "default": "unclear", - "display_type": "enum", - "field_number": "3.5", - "instant_facet": true, - "long_description": "An assessment of the AI tangible harm level, which takes into account the CSET definitions of AI tangible harm levels, along with the inputs for annotation fields about the AI, harm, chain of harm, and entity.", - "long_name": "Annotator's AI tangible harm level assessment", - "mongo_type": "string", - "permitted_values": [ - "AI tangible harm event", - "AI tangible harm near-miss", - "AI tangible harm issue", - "none", - "unclear" - ], - "placeholder": "", - "public": true, - "required": false, - "short_description": "An assessment of the AI tangible harm level, which takes into account the CSET definitions of AI tangible harm levels, along with the inputs for annotation fields about the AI, harm, chain of harm, and entity. ", - "short_name": "AI Harm Level", - "weight": 50 - }, - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "long_string", - "field_number": "3.6", - "instant_facet": false, - "long_description": "If for 3.5 you select unclear or leave it blank, please provide a brief description of why.\n\n You can also add notes if you want to provide justification for a level", - "long_name": "AI Tangible Harm Level Notes", - "mongo_type": "string", - "permitted_values": [], - "placeholder": "Notes about the AI tangible harm level assessment", - "public": true, - "required": false, - "short_description": "Notes about the AI tangible harm level assessment", - "short_name": "AI Tangible Harm Level Notes", - "weight": 5 - }, - { - "__typename": "TaxaField_list", - "default": "maybe", - "display_type": "enum", - "field_number": "4.1", - "instant_facet": true, - "long_description": "Did this impact people's access to critical or public services (health care, social services, voting, transportation, etc)?", - "long_name": "Did this impact people's access to critical or public services (health care, social services, voting, transportation, etc)?", - "mongo_type": "string", - "permitted_values": [ - "yes", - "no", - "maybe" - ], - "placeholder": "", - "public": true, - "required": false, - "short_description": "Indicates if people’s access to critical public services was impacted.", - "short_name": "Impact on Critical Services", - "weight": 50 - }, - { - "__typename": "TaxaField_list", - "default": "maybe", - "display_type": "enum", - "field_number": "4.2", - "instant_facet": true, - "long_description": "Indicate if a violation of human rights, civil rights, civil liberties, or democratic norms occurred.", - "long_name": "Was this a violation of human rights, civil liberties, civil rights, or democratic norms?", - "mongo_type": "string", - "permitted_values": [ - "yes", - "no", - "maybe" - ], - "placeholder": "", - "public": true, - "required": false, - "short_description": "Indicate if a violation of human rights, civil rights, civil liberties, or democratic norms occurred.", - "short_name": "Rights Violation", - "weight": 50 - }, - { - "__typename": "TaxaField_list", - "default": "maybe", - "display_type": "enum", - "field_number": "4.3", - "instant_facet": true, - "long_description": "Indicate if a minor was disproportionately targeted or affected", - "long_name": "Was a minor involved in the incident (disproportionally treated or specifically targeted/affected)", - "mongo_type": "string", - "permitted_values": [ - "yes", - "no", - "maybe" - ], - "placeholder": "", - "public": true, - "required": false, - "short_description": "Was a minor involved in the incident (disproportionally treated or specifically targeted/affected)", - "short_name": "Involving Minor", - "weight": 50 - }, - { - "__typename": "TaxaField_list", - "default": "maybe", - "display_type": "enum", - "field_number": "4.4", - "instant_facet": true, - "long_description": "Detrimental content can include deepfakes, identity misrepresentation, insults, threats of violence, eating disorder or self harm promotion, extremist content, misinformation, sexual abuse material, and scam emails. Detrimental content in itself is often not harmful, however, it can lead to or instigate injury, damage, or loss.", - "long_name": "Was detrimental content (misinformation, hate speech) involved?", - "mongo_type": "string", - "permitted_values": [ - "yes", - "no", - "maybe" - ], - "placeholder": "", - "public": true, - "required": false, - "short_description": "Was detrimental content (misinformation, hate speech) involved?", - "short_name": "Detrimental Content", - "weight": 50 - }, - { - "__typename": "TaxaField_list", - "default": "maybe", - "display_type": "enum", - "field_number": "4.5", - "instant_facet": true, - "long_description": "Protected characteristics include religion, commercial facilities, geography, age, sex, sexual orientation or gender identity, familial status (e.g., having or not having children) or pregnancy, disability, veteran status, genetic information, financial means, race or creed, Ideology, nation of origin, citizenship, and immigrant status.\n\nAt the federal level in the US, age is a protected characteristic for people over the age of 40. Minors are not considered a protected class. For this reason the CSET annotation taxonomy has a separate field to note if a minor was involved.\n\nOnly mark yes if there is clear evidence discrimination occurred. If there are conflicting accounts, mark unsure. Do not mark that discrimination occurred based on expectation alone.", - "long_name": "Was a group of people or an individual treated differently based upon a protected characteristic?", - "mongo_type": "string", - "permitted_values": [ - "yes", - "no", - "maybe" - ], - "placeholder": "", - "public": true, - "required": false, - "short_description": "Was a group of people treated differently based upon a protected characteristic (e.g. race, ethnicity, creed, immigrant status, color, religion, sex, national origin, age, disability, genetic information)?", - "short_name": "Protected Characteristic", - "weight": 50 - }, - { - "__typename": "TaxaField_list", - "default": "unclear", - "display_type": "multi", - "field_number": "4.6", - "instant_facet": true, - "long_description": "Multiple can occur.\n\nGenetic information refers to information about a person’s genetic tests or the genetic tests of their relatives. Genetic information can predict the manifestation of a disease or disorder.", - "long_name": "If harms were potentially unevenly distributed among people, on what basis?", - "mongo_type": "string", - "permitted_values": [ - "none", - "age", - "disability", - "familial status (e.g., having or not having children) or pregnancy", - "financial means", - "genetic information", - "geography", - "ideology", - "nation of origin, citizenship, immigrant status", - "race", - "religion", - "sex", - "sexual orientation or gender identity", - "veteran status", - "unclear", - "other" - ], - "placeholder": "", - "public": true, - "required": false, - "short_description": "Indicates how the harms were potentially distributed.", - "short_name": "Harm Distribution Basis", - "weight": 50 - }, - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "long_string", - "field_number": "4.7", - "instant_facet": false, - "long_description": "Input any notes that may help explain your answers.", - "long_name": "Input any notes that may help explain your answers.", - "mongo_type": "string", - "permitted_values": [], - "placeholder": "Notes", - "public": true, - "required": false, - "short_description": "Input any notes that may help explain your answers.", - "short_name": "Notes (special interest intangible harm)", - "weight": 5 - }, - { - "__typename": "TaxaField_list", - "default": "maybe", - "display_type": "enum", - "field_number": "5.1", - "instant_facet": false, - "long_description": "An assessment of whether a special interest intangible harm occurred. This assessment does not consider the context of the intangible harm, if an AI was involved, or if there is characterizable class or subgroup of harmed entities. It is also not assessing if an intangible harm occurred. It is only asking if a special interest intangible harm occurred.", - "long_name": "Was there a special interest intangible harm or risk of harm?", - "mongo_type": "string", - "permitted_values": [ - "yes", - "no", - "maybe" - ], - "placeholder": "", - "public": true, - "required": false, - "short_description": "An assessment of whether a special interest intangible harm occurred. This assessment does not consider the context of the intangible harm, if an AI was involved, or if there is characterizable class or subgroup of harmed entities. It is also not assessing if an intangible harm occurred. It is only asking if a special interest intangible harm occurred.", - "short_name": "Special Interest Intangible Harm", - "weight": 5 - }, - { - "__typename": "TaxaField_list", - "default": "maybe", - "display_type": "enum", - "field_number": "5.2", - "instant_facet": true, - "long_description": "An assessment of whether or not an AI system was involved. It is sometimes difficult to judge between an AI and an automated system or expert rules system. In these cases select “maybe”", - "long_name": "Does the incident involve an AI system?", - "mongo_type": "string", - "permitted_values": [ - "yes", - "no", - "maybe" - ], - "placeholder": "", - "public": true, - "required": false, - "short_description": "Does the incident involve an AI system?", - "short_name": "AI System", - "weight": 50 - }, - { - "__typename": "TaxaField_list", - "default": "maybe", - "display_type": "enum", - "field_number": "5.3", - "instant_facet": true, - "long_description": "“Yes” if an AI was involved in harm, its behavior can be directly linked to the harm, and the harm may not have occurred if the AI acted differently. “Maybe” if the link is unclear. Otherwise, select “no.”", - "long_name": "Can an AI be directly and clearly linked to the special interest intangible harm?", - "mongo_type": "string", - "permitted_values": [ - "yes", - "no", - "maybe" - ], - "placeholder": "", - "public": true, - "required": false, - "short_description": "Can an AI be directly and clearly linked to the special interest intangible harm?", - "short_name": "AI Linked to Special Interest Intangible Harm", - "weight": 50 - }, - { - "__typename": "TaxaField_list", - "default": null, - "display_type": "bool", - "field_number": "5.4", - "instant_facet": true, - "long_description": "A characterizable class or subgroup are descriptions of different populations of people. Often they are characteristics by which people qualify for special protection by a law, policy, or similar authority.\n\n Sometimes, groups may be characterized by their exposure to the incident via geographical proximity (e.g., ‘visitors to the park’) or participation in an activity (e.g.,‘Twitter users’).", - "long_name": "There is a characterizable class or subgroup of entities that experienced the harm", - "mongo_type": "bool", - "permitted_values": [], - "placeholder": "", - "public": true, - "required": false, - "short_description": "“Yes” if the harmed entity or entities can be characterized. “No” if there are not any characterizable entities.", - "short_name": "Harmed Class of Entities", - "weight": 50 - }, - { - "__typename": "TaxaField_list", - "default": "maybe", - "display_type": "enum", - "field_number": "5.5", - "instant_facet": true, - "long_description": "AI tangible harm is determined in a different field. The determination of a special interest intangible harm is not dependant upon the AI tangible harm level.", - "long_name": "The annotator’s assessment of if an AI special interest intangible harm occurred.", - "mongo_type": "string", - "permitted_values": [ - "yes", - "no", - "maybe" - ], - "placeholder": "", - "public": true, - "required": false, - "short_description": "The annotator’s assessment of if an AI special interest intangible harm occurred.", - "short_name": "Annotator’s AI special interest intangible harm assessment", - "weight": 50 - }, - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "long_string", - "field_number": "5.6", - "instant_facet": false, - "long_description": "If for 5.5 you select unclear or leave it blank, please provide a brief description of why.\n\nYou can also add notes if you want to provide justification for a level.", - "long_name": "If for 5.5 you select unclear or leave it blank, please provide a brief description of why.\n\nYou can also add notes if you want to provide justification for a level.", - "mongo_type": "string", - "permitted_values": [], - "placeholder": "Notes", - "public": true, - "required": false, - "short_description": "If for 5.5 you select unclear or leave it blank, please provide a brief description of why.\n\nYou can also add notes if you want to provide justification for a level.", - "short_name": "Notes (AI special interest intangible harm)", - "weight": 5 - }, - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "string", - "field_number": "6.1", - "instant_facet": false, - "long_description": "The year in which the incident occurred. If there are multiple harms or occurrences of the incident, list the earliest. If a precise date is unavailable, but the available sources provide a basis for estimating the year, estimate. Otherwise, leave blank.", - "long_name": "The year in which the incident first occurred.", - "mongo_type": "string", - "permitted_values": [], - "placeholder": "YYYY", - "public": true, - "required": false, - "short_description": "The year in which the incident occurred. If there are multiple harms or occurrences of the incident, list the earliest. If a precise date is unavailable, but the available sources provide a basis for estimating the year, estimate. Otherwise, leave blank.\n\nEnter in the format of YYYY", - "short_name": "Date of Incident Year", - "weight": 5 - }, - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "string", - "field_number": "6.2", - "instant_facet": false, - "long_description": "The month in which the incident occurred. If there are multiple harms or occurrences of the incident, list the earliest. If a precise date is unavailable, but the available sources provide a basis for estimating the month, estimate. Otherwise, leave blank.", - "long_name": "The month in which the incident first occurred.", - "mongo_type": "string", - "permitted_values": [], - "placeholder": "MM", - "public": true, - "required": false, - "short_description": "The month in which the incident occurred. If there are multiple harms or occurrences of the incident, list the earliest. If a precise date is unavailable, but the available sources provide a basis for estimating the month, estimate. Otherwise, leave blank.\n\nEnter in the format of MM", - "short_name": "Date of Incident Month", - "weight": 5 - }, - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "string", - "field_number": "6.3", - "instant_facet": true, - "long_description": "The day on which the incident occurred. If a precise date is unavailable, leave blank.\n\nEnter in the format of DD", - "long_name": "The day on which the first incident occurred.", - "mongo_type": "string", - "permitted_values": [], - "placeholder": "DD", - "public": true, - "required": false, - "short_description": "The day on which the incident occurred. If a precise date is unavailable, leave blank.\n\nEnter in the format of DD", - "short_name": "Date of Incident Day", - "weight": 5 - }, - { - "__typename": "TaxaField_list", - "default": null, - "display_type": "bool", - "field_number": "6.4", - "instant_facet": false, - "long_description": "“Yes” if the data was estimated. “No” otherwise.", - "long_name": "Is the date estimated?", - "mongo_type": "bool", - "permitted_values": [], - "placeholder": "", - "public": true, - "required": false, - "short_description": "“Yes” if the data was estimated. “No” otherwise.", - "short_name": "Estimated Date", - "weight": 5 - }, - { - "__typename": "TaxaField_list", - "default": "maybe", - "display_type": "enum", - "field_number": "6.5", - "instant_facet": true, - "long_description": "This happens very rarely but is possible. Examples include two chatbots having a conversation with each other, or two autonomous vehicles in a crash.", - "long_name": "Was the AI interacting with another AI?", - "mongo_type": "string", - "permitted_values": [ - "yes", - "no", - "maybe" - ], - "placeholder": "", - "public": true, - "required": false, - "short_description": "“Yes” if two or more independently operating AI systems were involved. “No” otherwise.", - "short_name": "Multiple AI Interaction", - "weight": 5 - }, - { - "__typename": "TaxaField_list", - "default": "maybe", - "display_type": "enum", - "field_number": "6.6", - "instant_facet": true, - "long_description": "This question is slightly different from the one in field 2.1.1. That question asks about there being interaction with physical objects–an ability to manipulate or change. A system can be embedded in a physical object and able to interact with the physical environment, e.g. a vacuum robot. A system can be embedded in a physical object and not interact with a physical environment, e.g. a camera system that only records images when the AI detects that dogs are present. AI systems that are accessed through API, web-browser, etc by using a mobile device or computer are not considered to be embedded in hardware systems. They are accessed through hardware.", - "long_name": "Is the AI embedded in a physical system or have a physical presence?", - "mongo_type": "string", - "permitted_values": [ - "yes", - "no", - "maybe" - ], - "placeholder": "", - "public": true, - "required": false, - "short_description": "“Yes” if the AI is embedded in a physical system. “No” if it is not. “Maybe” if it is unclear.", - "short_name": "Embedded", - "weight": 5 - }, - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "string", - "field_number": "6.7", - "instant_facet": false, - "long_description": "If the incident occurred at a specific known location, note the city. If there are multiple relevant locations, enter multiple city/state/country values.", - "long_name": "If the incident occurred at a specific known location, note the city.", - "mongo_type": "string", - "permitted_values": [], - "placeholder": "City", - "public": true, - "required": false, - "short_description": "If the incident occurred at a specific known location, note the city.", - "short_name": "Location City", - "weight": 5 - }, - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "string", - "field_number": "6.8", - "instant_facet": false, - "long_description": "If the incident occurred at a specific known location, note the state/province. If there are multiple relevant locations, enter multiple city/state/country values.", - "long_name": "If the incident occurred at a specific known location, note the state/province.", - "mongo_type": "string", - "permitted_values": [], - "placeholder": "State or Province", - "public": true, - "required": false, - "short_description": "If the incident occurred at a specific known location, note the state/province.", - "short_name": "Location State/Province (two letters)", - "weight": 5 - }, - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "string", - "field_number": "6.9", - "instant_facet": false, - "long_description": "Follow ISO 3166 for the 2-letter country codes.\n\nIf there are multiple relevant locations, enter multiple city/state/country values.", - "long_name": "If the incident occurred at a specific known location, note the country. ", - "mongo_type": "string", - "permitted_values": [], - "placeholder": "Country", - "public": true, - "required": false, - "short_description": "If the incident occurred at a specific known location, note the country. Follow ISO 3166 for the 2-letter country codes.", - "short_name": "Location Country (two letters)", - "weight": 5 - }, - { - "__typename": "TaxaField_list", - "default": "unclear", - "display_type": "enum", - "field_number": "6.10", - "instant_facet": true, - "long_description": "Use this reference to map countries to regions: https://www.dhs.gov/geographic-regions", - "long_name": "Location Region", - "mongo_type": "string", - "permitted_values": [ - "Global", - "Africa", - "Asia", - "Caribbean", - "Central America", - "Europe", - "North America", - "Oceania", - "South America", - "unclear" - ], - "placeholder": "", - "public": true, - "required": false, - "short_description": "Select the region of the world where the incident occurred. If it occurred in multiple, leave blank.", - "short_name": "Location Region", - "weight": 5 - }, - { - "__typename": "TaxaField_list", - "default": "unclear", - "display_type": "multi", - "field_number": "6.11", - "instant_facet": true, - "long_description": "Which critical infrastructure sectors were affected, if any?", - "long_name": "Which critical infrastructure sectors were affected, if any?", - "mongo_type": "string", - "permitted_values": [ - "chemical", - "commercial facilities", - "communications", - "critical manufacturing", - "dams", - "defense-industrial base", - "emergency services", - "energy", - "financial services", - "food and agriculture", - "government facilities", - "healthcare and public health", - "information technology", - "nuclear ", - "transportation", - "water and wastewater", - "Other", - "unclear" - ], - "placeholder": "", - "public": true, - "required": false, - "short_description": "Which critical infrastructure sectors were affected, if any?", - "short_name": "Infrastructure Sectors", - "weight": 5 - }, - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "list", - "field_number": "6.12", - "instant_facet": false, - "long_description": "A record of any abnormal or atypical operational conditions that occurred. This field is most often blank.", - "long_name": "A record of any abnormal or atypical operational conditions that occurred.", - "mongo_type": "array", - "permitted_values": [], - "placeholder": "e.g. raining; night; low visibility", - "public": true, - "required": false, - "short_description": "A record of any abnormal or atypical operational conditions that occurred.", - "short_name": "Operating Conditions", - "weight": 5 - }, - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "long_string", - "field_number": "6.13", - "instant_facet": false, - "long_description": "Input any notes that may help explain your answers.", - "long_name": "Notes (Environmental and Temporal Characteristics)", - "mongo_type": "string", - "permitted_values": [], - "placeholder": "Notes", - "public": true, - "required": false, - "short_description": "Input any notes that may help explain your answers.", - "short_name": "Notes (Environmental and Temporal Characteristics)", - "weight": 5 - }, - { - "__typename": "TaxaField_list", - "default": "[]", - "display_type": "object-list", - "field_number": "7", - "instant_facet": false, - "long_description": "Characterizing Entities and the Harm", - "long_name": "Characterizing Entities and the Harm", - "mongo_type": "array", - "permitted_values": [], - "placeholder": "", - "public": true, - "required": false, - "short_description": "Characterizing Entities and the Harm", - "short_name": "Entities", - "weight": 5 - }, - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "int", - "field_number": "8.1", - "instant_facet": true, - "long_description": "This field cannot be greater than zero if the harm is anything besides ‘Physical health/safety.’ ", - "long_name": "How many human lives were lost?", - "mongo_type": "int", - "permitted_values": [], - "placeholder": "", - "public": true, - "required": false, - "short_description": "Indicates the number of deaths reported", - "short_name": "Lives Lost", - "weight": 5 - }, - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "int", - "field_number": "8.2", - "instant_facet": false, - "long_description": "This field cannot be greater than zero if the harm is anything besides 'Physical health/safety'.\n\nAll reported injuries should count, regardless of their severity level. If a person lost their limb and another person scraped their elbow, both cases would be considered injuries. Do not include the number of deaths in this count.", - "long_name": "How many humans were injured?", - "mongo_type": "int", - "permitted_values": [], - "placeholder": "", - "public": true, - "required": false, - "short_description": "Indicate the number of injuries reported.", - "short_name": "Injuries", - "weight": 5 - }, - { - "__typename": "TaxaField_list", - "default": null, - "display_type": "bool", - "field_number": "8.3", - "instant_facet": false, - "long_description": "Indicates if the amount was estimated.", - "long_name": "Are any quantities estimated?", - "mongo_type": "bool", - "permitted_values": [], - "placeholder": "", - "public": true, - "required": false, - "short_description": "Indicates if the amount was estimated.", - "short_name": "Estimated Harm Quantities", - "weight": 5 - }, - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "long_string", - "field_number": "8.4", - "instant_facet": false, - "long_description": "Input any notes that may help explain your answers.", - "long_name": "Notes ( Tangible Harm Quantities Information)", - "mongo_type": "string", - "permitted_values": [], - "placeholder": "Notes", - "public": true, - "required": false, - "short_description": "Input any notes that may help explain your answers.", - "short_name": "Notes ( Tangible Harm Quantities Information)", - "weight": 5 - }, - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "long_string", - "field_number": "9.1", - "instant_facet": false, - "long_description": "Describe the AI system in as much detail as the reports will allow.\n\nA high level description of the AI system is sufficient, but if more technical details about the AI system are available, include them in the description as well.", - "long_name": "Description of the AI system involved", - "mongo_type": "string", - "permitted_values": [], - "placeholder": "Description of the AI system involved", - "public": true, - "required": false, - "short_description": "A description of the AI system (when possible)", - "short_name": "AI System Description", - "weight": 5 - }, - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "list", - "field_number": "9.2", - "instant_facet": false, - "long_description": "This is a freeform field that can have any value. There could be multiple entries for this field.\n\nCommon ones include\n\n- still images\n- video\n- text\n- speech\n- Personally Identifiable Information\n- structured data\n- other\n- unclear\n\nStill images are static images. Video images consist of moving images. Text and speech data are considered an important category of unstructured data. They consist of written and spoken words that are not in a tabular format. Personally identifiable information is data that can uniquely identify an individual and may contain sensitive information. Structured data is often in a tabular, machine readable format and can typically be used by an AI system without much preprocessing.\n\nAvoid using ‘unstructured data’ data in this field. Instead specify the type of unstructured data; text, images, audio files, etc. It is ok to use ‘structured data’ in this field.\n\nRecord what the media report explicitly states. If the report does not explicitly state an input modality but it is likely that a particular kind of input contributed to the harm or near harm, record that input. If you are still unsure, do not record anything.", - "long_name": "Description of data inputs to the AI system", - "mongo_type": "array", - "permitted_values": [], - "placeholder": "", - "public": true, - "required": false, - "short_description": "A list of the types of data inputs for the AI system.", - "short_name": "Data Inputs", - "weight": 5 - }, - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "multi", - "field_number": "9.3", - "instant_facet": true, - "long_description": "Indicate the sector in which the AI system is deployed\n\nThere could be multiple entries for this field.", - "long_name": "Indicates the sector in which the AI system is deployed", - "mongo_type": "array", - "permitted_values": [ - "agriculture, forestry and fishing", - "mining and quarrying", - "manufacturing", - "electricity, gas, steam and air conditioning supply", - "water supply", - "construction", - "wholesale and retail trade", - "transportation and storage", - "accommodation and food service activities", - "information and communication", - "financial and insurance activities", - "real estate activities", - "professional, scientific and technical activities", - "administrative and support service activities", - "public administration", - "defense", - "law enforcement", - "Education", - "human health and social work activities", - "Arts, entertainment and recreation", - "other service activities", - "activities of households as employers", - "activities of extraterritorial organizations and bodies", - "other", - "unclear" - ], - "placeholder": "", - "public": true, - "required": false, - "short_description": "Indicate the sector in which the AI system is deployed", - "short_name": "Sector of Deployment", - "weight": 50 - }, - { - "__typename": "TaxaField_list", - "default": "maybe", - "display_type": "enum", - "field_number": "9.4", - "instant_facet": true, - "long_description": "Indicate whether the AI system is deployed in the public sector. The public sector is the part of the economy that is controlled and operated by the government.", - "long_name": "Indicates whether the AI system is deployed in the public sector", - "mongo_type": "string", - "permitted_values": [ - "yes", - "no", - "maybe" - ], - "placeholder": "", - "public": true, - "required": false, - "short_description": "Indicate whether the AI system is deployed in the public sector", - "short_name": "Public Sector Deployment", - "weight": 50 - }, - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "enum", - "field_number": "9.5", - "instant_facet": true, - "long_description": "Autonomy1: The system operates independently without simultaneous human oversight, interaction, or intervention.\n\nAutonomy2: The system operates independently but with human oversight, where a human can observe and override the system’s decisions in real time.\n\nAutonomy3: The system does not independently make decisions but instead provides information to a human who actively chooses to proceed with the AI’s information.", - "long_name": "Autonomy Level", - "mongo_type": "string", - "permitted_values": [ - "Autonomy1", - "Autonomy2", - "Autonomy3", - "unclear" - ], - "placeholder": "", - "public": true, - "required": false, - "short_description": "Autonomy1: The system operates independently without simultaneous human oversight, interaction, or intervention.\n\nAutonomy2: The system operates independently but with human oversight, where a human can observe and override the system’s decisions in real time.\n\nAutonomy3: The system does not independently make decisions but instead provides information to a human who actively chooses to proceed with the AI’s information.", - "short_name": "Autonomy Level", - "weight": 50 - }, - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "long_string", - "field_number": "9.8", - "instant_facet": false, - "long_description": "Input any notes that may help explain your answers.", - "long_name": "Notes (Information about AI System)", - "mongo_type": "string", - "permitted_values": [], - "placeholder": "Notes", - "public": true, - "required": false, - "short_description": "Input any notes that may help explain your answers.", - "short_name": "Notes (Information about AI System)", - "weight": 5 - }, - { - "__typename": "TaxaField_list", - "default": "unclear", - "display_type": "enum", - "field_number": "10.1", - "instant_facet": true, - "long_description": "Indicates if the system was designed to do harm. If it was designed to perform harm, the field will indicate if the AI system did or did not create unintended harm–i.e. was the reported harm the harm that AI was expected to perform or a different unexpected harm? ", - "long_name": "Was the AI intentionally developed or deployed to perform the harm?", - "mongo_type": "string", - "permitted_values": [ - "Yes. Intentionally designed to perform harm and did create intended harm", - "Yes. Intentionally designed to perform harm but created an unintended harm (a different harm may have occurred)", - "No. Not intentionally designed to perform harm", - "unclear" - ], - "placeholder": "", - "public": true, - "required": false, - "short_description": "Was the AI intentionally developed or deployed to perform the harm?\n\nIf yes, did the AI’s behavior result in unintended or intended harm? ", - "short_name": "Intentional Harm", - "weight": 50 - }, - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "string", - "field_number": "10.2", - "instant_facet": false, - "long_description": "Describe the type of physical system that the AI was integrated into. ", - "long_name": "Into what type of physical system was the AI integrated, if any?", - "mongo_type": "string", - "permitted_values": [], - "placeholder": "Physical System Type (e.g. trash sorting robot)", - "public": true, - "required": false, - "short_description": "Describe the type of physical system that the AI was integrated into.", - "short_name": "Physical System Type", - "weight": 5 - }, - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "list", - "field_number": "10.3", - "instant_facet": false, - "long_description": "Describe the AI’s application.\n\nIt is likely that the annotator will not have enough information to complete this field. If this occurs, enter unclear.\n\nThis is a freeform field. Some possible entries are\n\n- unclear\n- human language technologies\n- computer vision\n- robotics\n- automation and/or optimization\n- other\n\nThe application area of an AI is the high level task that the AI is intended to perform. It does not describe the technical methods by which the AI performs the task. Considering what an AI’s technical methods enable it to do is another way of arriving at what an AI’s application is. \n\nIt is possible for multiple application areas to be involved. When possible pick the principle or domain area, but it is ok to select multiple areas.", - "long_name": "AI task or core application area", - "mongo_type": "array", - "permitted_values": [], - "placeholder": "", - "public": true, - "required": false, - "short_description": "Describe the AI’s application.", - "short_name": "AI Task", - "weight": 5 - }, - { - "__typename": "TaxaField_list", - "default": "unclear", - "display_type": "list", - "field_number": "10.4", - "instant_facet": false, - "long_description": "Describe the tools and methods that enable the AI’s application.\n\nIt is likely that the annotator will not have enough information to complete this field. If this occurs, enter unclear\n\nThis is a freeform field. Some possible entries are\n\n- unclear\n- reinforcement learning\n- neural networks\n- decision trees\n- bias mitigation\n- optimization\n- classifier\n- NLP/text analytics\n- continuous learning\n- unsupervised learning\n- supervised learning\n- clustering\n- prediction\n- rules\n- random forest\n\nAI tools and methods are the technical building blocks that enable the AI’s application.", - "long_name": "AI tools and methods", - "mongo_type": "array", - "permitted_values": [], - "placeholder": "", - "public": true, - "required": false, - "short_description": "Describe the tools and methods that enable the AI’s application.", - "short_name": "AI tools and methods", - "weight": 50 - }, - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "long_string", - "field_number": "10.5", - "instant_facet": false, - "long_description": "Input any notes that may help explain your answers.", - "long_name": "Notes (AI Functionality and Techniques)", - "mongo_type": "string", - "permitted_values": [], - "placeholder": "Notes", - "public": true, - "required": false, - "short_description": "Input any notes that may help explain your answers.", - "short_name": "Notes (AI Functionality and Techniques)", - "weight": 5 - } - ], - "namespace": "CSETv1", - "weight": 70 - }, - { - "__typename": "Taxa", - "description": "# What is the CSET Taxonomy?\n\nThe Center for Security and Emerging Technology (CSET) taxonomy is\na general taxonomy of AI incidents. There are a large number of \nclassified attributes, including ones pertaining to safety, fairness, \nindustry, geography, timing, and cost. All classifications within \nthe CSET taxonomy are first applied by one CSET annotator and \nreviewed by another CSET annotator before the classifications are \nfinalized. The combination of a rigorously defined coding set and \nthe completeness with which it has been applied make the CSET \ntaxonomy the AIID's gold standard for taxonomies. Nevertheless, \nthe CSET taxonomy is an ongoing effort and you are invited to \nreport any errors you may discover in its application.\n\n## How do I explore the taxonomy?\n\nAll taxonomies can be used to filter incident reports within the \n[Discover Application](/apps/discover). The taxonomy filters work\nsimilarly to how you filter products on an E-commerce website. \nUse the search field at the bottom of the “Classifications” tab to \nfind the taxonomy field you would like to filter with, then click \nthe desired value to apply the filter.\n\n# About CSET\n\nA policy research organization within Georgetown University’s \nWalsh School of Foreign Service, CSET produces data-driven \nresearch at the intersection of security and technology, \nproviding nonpartisan analysis to the policy community. CSET is \ncurrently focusing on the effects of progress in artificial \nintelligence (AI), advanced computing and biotechnology. CSET \nseeks to prepare a new generation of decision-makers to address \nthe challenges and opportunities of emerging technologies. [(Read \nmore)](https://cset.georgetown.edu/about-us/).", - "field_list": [ - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "int", - "field_number": "1.1", - "instant_facet": false, - "long_description": "The number of the incident in the AI Incident Database.", - "long_name": "The number of the incident in the AI Incident Database.", - "mongo_type": "int", - "permitted_values": null, - "placeholder": null, - "public": true, - "required": false, - "short_description": "The number of the incident in the AI Incident Database.", - "short_name": "Incident Number", - "weight": 5 - }, - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "string", - "field_number": "1.2", - "instant_facet": false, - "long_description": "An ID designating the individual who classified this incident according to the CSET taxonomy.", - "long_name": "Person responsible for the annotations", - "mongo_type": "string", - "permitted_values": [], - "placeholder": "Select name here", - "public": false, - "required": false, - "short_description": "This is the researcher that is responsible for applying the classifications of the CSET taxonomy.", - "short_name": "Annotator", - "weight": 5 - }, - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "enum", - "field_number": "1.3", - "instant_facet": false, - "long_description": "What is the quality assurance status of the CSET classifications for this incident?", - "long_name": "Where in the annotation process is this incident?", - "mongo_type": "string", - "permitted_values": [ - "1. Annotation in progress", - "2. Initial annotation complete", - "3. In peer review", - "4. Peer review complete", - "5. In quality control", - "6. Complete and final" - ], - "placeholder": "Select process status here", - "public": false, - "required": false, - "short_description": "What is the quality assurance status of the CSET classifications for this incident?", - "short_name": "Annotation Status", - "weight": 10 - }, - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "string", - "field_number": "1.4", - "instant_facet": false, - "long_description": "The CSET taxonomy assigns individual researchers to each incident as the primary parties responsible for classifying the incident according to the taxonomy. This is the person responsible for assuring the integrity of annotator's classifications.", - "long_name": "Person responsible for reviewing annotations", - "mongo_type": "string", - "permitted_values": [], - "placeholder": "Select name here", - "public": false, - "required": false, - "short_description": "This is the researcher that is responsible for ensuring the quality of the classifications applied to this incident.", - "short_name": "Peer Reviewer", - "weight": 20 - }, - { - "__typename": "TaxaField_list", - "default": "false", - "display_type": "bool", - "field_number": "1.5", - "instant_facet": false, - "long_description": "The peer review process sometimes uncovers issues with the classifications that have been applied by the annotator. This field serves as a flag when there is a need for additional thought and input on the classifications applied", - "long_name": "Was this incident randomly selected for additional quality control?", - "mongo_type": "bool", - "permitted_values": [], - "placeholder": "", - "public": false, - "required": false, - "short_description": "Has someone flagged a potential issue with this incident's classifications? Annotators should leave this field blank.", - "short_name": "Quality Control", - "weight": 15 - }, - { - "__typename": "TaxaField_list", - "default": "maybe", - "display_type": "enum", - "field_number": "2.1", - "instant_facet": true, - "long_description": "“Yes” if the AI system(s) is embedded in hardware that can interact with, affect, and change the physical objects (cars, robots, medical facilities, etc.). Mark “No” if the system cannot. This includes systems that inform, detect, predict, or recommend.", - "long_name": "Did the incident occur in a domain with physical objects ?", - "mongo_type": "string", - "permitted_values": [ - "yes", - "no", - "maybe" - ], - "placeholder": "", - "public": null, - "required": false, - "short_description": "Did the incident occur in a domain with physical objects ?", - "short_name": "Physical Objects", - "weight": 50 - }, - { - "__typename": "TaxaField_list", - "default": "maybe", - "display_type": "enum", - "field_number": "2.2", - "instant_facet": true, - "long_description": "“Yes” if the sector in which the AI was used is associated with entertainment. “No” if it was used in a different, clearly identifiable sector. “Maybe” if the sector of use could not be determined.", - "long_name": "Did the AI incident occur in the entertainment industry?", - "mongo_type": "string", - "permitted_values": [ - "yes", - "no", - "maybe" - ], - "placeholder": "", - "public": null, - "required": false, - "short_description": "Did the AI incident occur in the entertainment industry?", - "short_name": "Entertainment Industry", - "weight": 50 - }, - { - "__typename": "TaxaField_list", - "default": "maybe", - "display_type": "enum", - "field_number": "2.3", - "instant_facet": true, - "long_description": "“Yes” if the incident is about a report, test, or study of the data and does not discuss an instance of injury, damage, or loss. “Maybe” if it is unclear. Otherwise mark “No.”", - "long_name": "Was the incident about a report, test, or study of data instead of the AI itself?", - "mongo_type": "string", - "permitted_values": [ - "yes", - "no", - "maybe" - ], - "placeholder": "", - "public": null, - "required": false, - "short_description": "Was the incident about a report, test, or study of data instead of the AI itself?", - "short_name": "Report, Test, or Study of data", - "weight": 50 - }, - { - "__typename": "TaxaField_list", - "default": "maybe", - "display_type": "enum", - "field_number": "2.4", - "instant_facet": true, - "long_description": "“Yes” if the involved system was deployed or sold to users. “No” if it was not. “Maybe” if there is not enough information or if the use is unclear.", - "long_name": "Was the reported system (even if AI involvement is unknown) deployed or sold to users?", - "mongo_type": "string", - "permitted_values": [ - "yes", - "no", - "maybe" - ], - "placeholder": "", - "public": null, - "required": false, - "short_description": "Was the reported system (even if AI involvement is unknown) deployed or sold to users?", - "short_name": "Deployed", - "weight": 50 - }, - { - "__typename": "TaxaField_list", - "default": "maybe", - "display_type": "enum", - "field_number": "2.5", - "instant_facet": true, - "long_description": "“Yes” if it was a test/demonstration performed by developers, producers or journalists in controlled conditions. “No” if it was not a test/demonstration. “No” if the test/demonstration was done by a user. “No” if the test/demonstration was in operational or uncontrolled conditions. “Maybe” otherwise.", - "long_name": "Was this a test or demonstration of an AI system done by developers, producers or researchers (versus users) in controlled conditions?", - "mongo_type": "string", - "permitted_values": [ - "yes", - "no", - "maybe" - ], - "placeholder": "", - "public": null, - "required": false, - "short_description": "Was this a test or demonstration of an AI system done by developers, producers or researchers (versus users) in controlled conditions?", - "short_name": "Producer Test in Controlled Conditions", - "weight": 50 - }, - { - "__typename": "TaxaField_list", - "default": "maybe", - "display_type": "enum", - "field_number": "2.6", - "instant_facet": true, - "long_description": "“Yes” if it was a test/demonstration performed by developers, producers or journalists in controlled conditions. “No” if it was not a test/demonstration. “No” if the test/demonstration was done by a user. “No” if the test/demonstration was in controlled or non-operational conditions. “Maybe” otherwise.", - "long_name": "Was this a test or demonstration of an AI system done by developers, producers or researchers (versus users) in operational conditions?", - "mongo_type": "string", - "permitted_values": [ - "yes", - "no", - "maybe" - ], - "placeholder": "", - "public": null, - "required": false, - "short_description": "Was this a test or demonstration of an AI system done by developers, producers or researchers (versus users) in operational conditions?", - "short_name": "Producer Test in Operational Conditions", - "weight": 50 - }, - { - "__typename": "TaxaField_list", - "default": "maybe", - "display_type": "enum", - "field_number": "2.7", - "instant_facet": true, - "long_description": "“Yes” if it was a test/demonstration performed by users in controlled conditions. “No” if it was not a test/demonstration. “No” if the test/demonstration was done by developers, producers or researchers. “No” if the test/demonstration was in controlled or non-controlled conditions.“Maybe” otherwise.", - "long_name": "Was this a test or demonstration done by users in controlled conditions?", - "mongo_type": "string", - "permitted_values": [ - "yes", - "no", - "maybe" - ], - "placeholder": "", - "public": null, - "required": false, - "short_description": "Was this a test or demonstration done by users in controlled conditions?", - "short_name": "User Test in Controlled Conditions", - "weight": 50 - }, - { - "__typename": "TaxaField_list", - "default": "maybe", - "display_type": "enum", - "field_number": "2.8", - "instant_facet": true, - "long_description": "“Yes” if it was a test/demonstration performed by users in operational conditions. “No” if it was not a test/demonstration. “No” if the test/demonstration was done by developers, producers or researchers. “No” if the test/demonstration was in controlled or non-operational conditions.“Maybe” otherwise.", - "long_name": "Was this a test or demonstration done by users in operational conditions?", - "mongo_type": "string", - "permitted_values": [ - "yes", - "no", - "maybe" - ], - "placeholder": "", - "public": null, - "required": false, - "short_description": "Was this a test or demonstration done by users in operational conditions?", - "short_name": "User Test in Operational Conditions", - "weight": 50 - }, - { - "__typename": "TaxaField_list", - "default": "maybe", - "display_type": "enum", - "field_number": "2.9", - "instant_facet": false, - "long_description": "Using the answers to the 8 domain questions, assess if the incident occurred in a domain where harm could be expected to occur. If you are unclear, input “maybe.”", - "long_name": "Incident occurred in a domain where we could expect harm to occur?", - "mongo_type": "string", - "permitted_values": [ - "yes", - "no", - "maybe" - ], - "placeholder": "", - "public": null, - "required": false, - "short_description": "Incident occurred in a domain where we could likely expect harm to occur?", - "short_name": "Harm Domain", - "weight": 50 - }, - { - "__typename": "TaxaField_list", - "default": "unclear", - "display_type": "enum", - "field_number": "3.1", - "instant_facet": true, - "long_description": "An assessment of whether tangible harm, imminent tangible harm, or non-imminent tangible harm occurred. This assessment does not consider the context of the tangible harm, if an AI was involved, or if there is an identifiable, specific, and harmed entity. It is also not assessing if an intangible harm occurred. It is only asking if tangible harm occurred and what its imminency was.", - "long_name": "Did tangible harm (loss, damage or injury ) occur? ", - "mongo_type": "string", - "permitted_values": [ - "tangible harm definitively occurred", - "imminent risk of tangible harm (near miss) did occur", - "non-imminent risk of tangible harm (an issue) occurred", - "no tangible harm, near-miss, or issue", - "unclear" - ], - "placeholder": "", - "public": true, - "required": false, - "short_description": "Did tangible harm (loss, damage or injury ) occur? ", - "short_name": "Tangible Harm", - "weight": 50 - }, - { - "__typename": "TaxaField_list", - "default": "maybe", - "display_type": "enum", - "field_number": "3.2", - "instant_facet": true, - "long_description": "An assessment of whether or not an AI system was involved. It is sometimes difficult to judge between an AI and an automated system or expert rules system. In these cases select “maybe”", - "long_name": "Does the incident involve an AI system?", - "mongo_type": "string", - "permitted_values": [ - "yes", - "no", - "maybe" - ], - "placeholder": "", - "public": true, - "required": false, - "short_description": "Does the incident involve an AI system?", - "short_name": "AI System", - "weight": 50 - }, - { - "__typename": "TaxaField_list", - "default": "maybe", - "display_type": "enum", - "field_number": "3.3", - "instant_facet": true, - "long_description": "“Yes” if an AI was involved in harm, its behavior can be directly linked to the harm, and the harm may not have occurred if the AI acted differently. “Maybe” if the link is unclear. Otherwise, select “no.”", - "long_name": "Can an AI be directly and clearly linked to tangible harm?", - "mongo_type": "string", - "permitted_values": [ - "yes", - "no", - "maybe" - ], - "placeholder": "", - "public": true, - "required": false, - "short_description": "Can an AI be directly and clearly linked to tangible harm?", - "short_name": "Clear Link to AI", - "weight": 50 - }, - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "bool", - "field_number": "3.4", - "instant_facet": true, - "long_description": "“Yes” if it is theoretically possible to both specify and identify the entity. Having that information is not required. The information just needs to exist and be potentially discoverable. “No” if there are not any potentially identifiable specific entities or if the harmed entities are a class or subgroup that can only be characterized. ", - "long_name": "There is a potentially identifiable specific entity that experienced the harm", - "mongo_type": "bool", - "permitted_values": [], - "placeholder": "", - "public": true, - "required": false, - "short_description": "A potentially identifiable specific entity that experienced the harm can be characterized or identified.", - "short_name": "There is a potentially identifiable specific entity that experienced the harm", - "weight": 50 - }, - { - "__typename": "TaxaField_list", - "default": "unclear", - "display_type": "enum", - "field_number": "3.5", - "instant_facet": true, - "long_description": "An assessment of the AI tangible harm level, which takes into account the CSET definitions of AI tangible harm levels, along with the inputs for annotation fields about the AI, harm, chain of harm, and entity.", - "long_name": "Annotator's AI tangible harm level assessment", - "mongo_type": "string", - "permitted_values": [ - "AI tangible harm event", - "AI tangible harm near-miss", - "AI tangible harm issue", - "none", - "unclear" - ], - "placeholder": "", - "public": true, - "required": false, - "short_description": "An assessment of the AI tangible harm level, which takes into account the CSET definitions of AI tangible harm levels, along with the inputs for annotation fields about the AI, harm, chain of harm, and entity. ", - "short_name": "AI Harm Level", - "weight": 50 - }, - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "long_string", - "field_number": "3.6", - "instant_facet": false, - "long_description": "If for 3.5 you select unclear or leave it blank, please provide a brief description of why.\n\n You can also add notes if you want to provide justification for a level", - "long_name": "AI Tangible Harm Level Notes", - "mongo_type": "string", - "permitted_values": [], - "placeholder": "Notes about the AI tangible harm level assessment", - "public": true, - "required": false, - "short_description": "Notes about the AI tangible harm level assessment", - "short_name": "AI Tangible Harm Level Notes", - "weight": 5 - }, - { - "__typename": "TaxaField_list", - "default": "maybe", - "display_type": "enum", - "field_number": "4.1", - "instant_facet": true, - "long_description": "Did this impact people's access to critical or public services (health care, social services, voting, transportation, etc)?", - "long_name": "Did this impact people's access to critical or public services (health care, social services, voting, transportation, etc)?", - "mongo_type": "string", - "permitted_values": [ - "yes", - "no", - "maybe" - ], - "placeholder": "", - "public": true, - "required": false, - "short_description": "Indicates if people’s access to critical public services was impacted.", - "short_name": "Impact on Critical Services", - "weight": 50 - }, - { - "__typename": "TaxaField_list", - "default": "maybe", - "display_type": "enum", - "field_number": "4.2", - "instant_facet": true, - "long_description": "Indicate if a violation of human rights, civil rights, civil liberties, or democratic norms occurred.", - "long_name": "Was this a violation of human rights, civil liberties, civil rights, or democratic norms?", - "mongo_type": "string", - "permitted_values": [ - "yes", - "no", - "maybe" - ], - "placeholder": "", - "public": true, - "required": false, - "short_description": "Indicate if a violation of human rights, civil rights, civil liberties, or democratic norms occurred.", - "short_name": "Rights Violation", - "weight": 50 - }, - { - "__typename": "TaxaField_list", - "default": "maybe", - "display_type": "enum", - "field_number": "4.3", - "instant_facet": true, - "long_description": "Indicate if a minor was disproportionately targeted or affected", - "long_name": "Was a minor involved in the incident (disproportionally treated or specifically targeted/affected)", - "mongo_type": "string", - "permitted_values": [ - "yes", - "no", - "maybe" - ], - "placeholder": "", - "public": true, - "required": false, - "short_description": "Was a minor involved in the incident (disproportionally treated or specifically targeted/affected)", - "short_name": "Involving Minor", - "weight": 50 - }, - { - "__typename": "TaxaField_list", - "default": "maybe", - "display_type": "enum", - "field_number": "4.4", - "instant_facet": true, - "long_description": "Detrimental content can include deepfakes, identity misrepresentation, insults, threats of violence, eating disorder or self harm promotion, extremist content, misinformation, sexual abuse material, and scam emails. Detrimental content in itself is often not harmful, however, it can lead to or instigate injury, damage, or loss.", - "long_name": "Was detrimental content (misinformation, hate speech) involved?", - "mongo_type": "string", - "permitted_values": [ - "yes", - "no", - "maybe" - ], - "placeholder": "", - "public": true, - "required": false, - "short_description": "Was detrimental content (misinformation, hate speech) involved?", - "short_name": "Detrimental Content", - "weight": 50 - }, - { - "__typename": "TaxaField_list", - "default": "maybe", - "display_type": "enum", - "field_number": "4.5", - "instant_facet": true, - "long_description": "Protected characteristics include religion, commercial facilities, geography, age, sex, sexual orientation or gender identity, familial status (e.g., having or not having children) or pregnancy, disability, veteran status, genetic information, financial means, race or creed, Ideology, nation of origin, citizenship, and immigrant status.\n\nAt the federal level in the US, age is a protected characteristic for people over the age of 40. Minors are not considered a protected class. For this reason the CSET annotation taxonomy has a separate field to note if a minor was involved.\n\nOnly mark yes if there is clear evidence discrimination occurred. If there are conflicting accounts, mark unsure. Do not mark that discrimination occurred based on expectation alone.", - "long_name": "Was a group of people or an individual treated differently based upon a protected characteristic?", - "mongo_type": "string", - "permitted_values": [ - "yes", - "no", - "maybe" - ], - "placeholder": "", - "public": true, - "required": false, - "short_description": "Was a group of people treated differently based upon a protected characteristic (e.g. race, ethnicity, creed, immigrant status, color, religion, sex, national origin, age, disability, genetic information)?", - "short_name": "Protected Characteristic", - "weight": 50 - }, - { - "__typename": "TaxaField_list", - "default": "unclear", - "display_type": "multi", - "field_number": "4.6", - "instant_facet": true, - "long_description": "Multiple can occur.\n\nGenetic information refers to information about a person’s genetic tests or the genetic tests of their relatives. Genetic information can predict the manifestation of a disease or disorder.", - "long_name": "If harms were potentially unevenly distributed among people, on what basis?", - "mongo_type": "string", - "permitted_values": [ - "none", - "age", - "disability", - "familial status (e.g., having or not having children) or pregnancy", - "financial means", - "genetic information", - "geography", - "ideology", - "nation of origin, citizenship, immigrant status", - "race", - "religion", - "sex", - "sexual orientation or gender identity", - "veteran status", - "unclear", - "other" - ], - "placeholder": "", - "public": true, - "required": false, - "short_description": "Indicates how the harms were potentially distributed.", - "short_name": "Harm Distribution Basis", - "weight": 50 - }, - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "long_string", - "field_number": "4.7", - "instant_facet": false, - "long_description": "Input any notes that may help explain your answers.", - "long_name": "Input any notes that may help explain your answers.", - "mongo_type": "string", - "permitted_values": [], - "placeholder": "Notes", - "public": true, - "required": false, - "short_description": "Input any notes that may help explain your answers.", - "short_name": "Notes (special interest intangible harm)", - "weight": 5 - }, - { - "__typename": "TaxaField_list", - "default": "maybe", - "display_type": "enum", - "field_number": "5.1", - "instant_facet": false, - "long_description": "An assessment of whether a special interest intangible harm occurred. This assessment does not consider the context of the intangible harm, if an AI was involved, or if there is characterizable class or subgroup of harmed entities. It is also not assessing if an intangible harm occurred. It is only asking if a special interest intangible harm occurred.", - "long_name": "Was there a special interest intangible harm or risk of harm?", - "mongo_type": "string", - "permitted_values": [ - "yes", - "no", - "maybe" - ], - "placeholder": "", - "public": true, - "required": false, - "short_description": "An assessment of whether a special interest intangible harm occurred. This assessment does not consider the context of the intangible harm, if an AI was involved, or if there is characterizable class or subgroup of harmed entities. It is also not assessing if an intangible harm occurred. It is only asking if a special interest intangible harm occurred.", - "short_name": "Special Interest Intangible Harm", - "weight": 5 - }, - { - "__typename": "TaxaField_list", - "default": "maybe", - "display_type": "enum", - "field_number": "5.2", - "instant_facet": true, - "long_description": "An assessment of whether or not an AI system was involved. It is sometimes difficult to judge between an AI and an automated system or expert rules system. In these cases select “maybe”", - "long_name": "Does the incident involve an AI system?", - "mongo_type": "string", - "permitted_values": [ - "yes", - "no", - "maybe" - ], - "placeholder": "", - "public": true, - "required": false, - "short_description": "Does the incident involve an AI system?", - "short_name": "AI System", - "weight": 50 - }, - { - "__typename": "TaxaField_list", - "default": "maybe", - "display_type": "enum", - "field_number": "5.3", - "instant_facet": true, - "long_description": "“Yes” if an AI was involved in harm, its behavior can be directly linked to the harm, and the harm may not have occurred if the AI acted differently. “Maybe” if the link is unclear. Otherwise, select “no.”", - "long_name": "Can an AI be directly and clearly linked to the special interest intangible harm?", - "mongo_type": "string", - "permitted_values": [ - "yes", - "no", - "maybe" - ], - "placeholder": "", - "public": true, - "required": false, - "short_description": "Can an AI be directly and clearly linked to the special interest intangible harm?", - "short_name": "AI Linked to Special Interest Intangible Harm", - "weight": 50 - }, - { - "__typename": "TaxaField_list", - "default": null, - "display_type": "bool", - "field_number": "5.4", - "instant_facet": true, - "long_description": "A characterizable class or subgroup are descriptions of different populations of people. Often they are characteristics by which people qualify for special protection by a law, policy, or similar authority.\n\n Sometimes, groups may be characterized by their exposure to the incident via geographical proximity (e.g., ‘visitors to the park’) or participation in an activity (e.g.,‘Twitter users’).", - "long_name": "There is a characterizable class or subgroup of entities that experienced the harm", - "mongo_type": "bool", - "permitted_values": [], - "placeholder": "", - "public": true, - "required": false, - "short_description": "“Yes” if the harmed entity or entities can be characterized. “No” if there are not any characterizable entities.", - "short_name": "Harmed Class of Entities", - "weight": 50 - }, - { - "__typename": "TaxaField_list", - "default": "maybe", - "display_type": "enum", - "field_number": "5.5", - "instant_facet": true, - "long_description": "AI tangible harm is determined in a different field. The determination of a special interest intangible harm is not dependant upon the AI tangible harm level.", - "long_name": "The annotator’s assessment of if an AI special interest intangible harm occurred.", - "mongo_type": "string", - "permitted_values": [ - "yes", - "no", - "maybe" - ], - "placeholder": "", - "public": true, - "required": false, - "short_description": "The annotator’s assessment of if an AI special interest intangible harm occurred.", - "short_name": "Annotator’s AI special interest intangible harm assessment", - "weight": 50 - }, - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "long_string", - "field_number": "5.6", - "instant_facet": false, - "long_description": "If for 5.5 you select unclear or leave it blank, please provide a brief description of why.\n\nYou can also add notes if you want to provide justification for a level.", - "long_name": "If for 5.5 you select unclear or leave it blank, please provide a brief description of why.\n\nYou can also add notes if you want to provide justification for a level.", - "mongo_type": "string", - "permitted_values": [], - "placeholder": "Notes", - "public": true, - "required": false, - "short_description": "If for 5.5 you select unclear or leave it blank, please provide a brief description of why.\n\nYou can also add notes if you want to provide justification for a level.", - "short_name": "Notes (AI special interest intangible harm)", - "weight": 5 - }, - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "string", - "field_number": "6.1", - "instant_facet": false, - "long_description": "The year in which the incident occurred. If there are multiple harms or occurrences of the incident, list the earliest. If a precise date is unavailable, but the available sources provide a basis for estimating the year, estimate. Otherwise, leave blank.", - "long_name": "The year in which the incident first occurred.", - "mongo_type": "string", - "permitted_values": [], - "placeholder": "YYYY", - "public": true, - "required": false, - "short_description": "The year in which the incident occurred. If there are multiple harms or occurrences of the incident, list the earliest. If a precise date is unavailable, but the available sources provide a basis for estimating the year, estimate. Otherwise, leave blank.\n\nEnter in the format of YYYY", - "short_name": "Date of Incident Year", - "weight": 5 - }, - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "string", - "field_number": "6.2", - "instant_facet": false, - "long_description": "The month in which the incident occurred. If there are multiple harms or occurrences of the incident, list the earliest. If a precise date is unavailable, but the available sources provide a basis for estimating the month, estimate. Otherwise, leave blank.", - "long_name": "The month in which the incident first occurred.", - "mongo_type": "string", - "permitted_values": [], - "placeholder": "MM", - "public": true, - "required": false, - "short_description": "The month in which the incident occurred. If there are multiple harms or occurrences of the incident, list the earliest. If a precise date is unavailable, but the available sources provide a basis for estimating the month, estimate. Otherwise, leave blank.\n\nEnter in the format of MM", - "short_name": "Date of Incident Month", - "weight": 5 - }, - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "string", - "field_number": "6.3", - "instant_facet": true, - "long_description": "The day on which the incident occurred. If a precise date is unavailable, leave blank.\n\nEnter in the format of DD", - "long_name": "The day on which the first incident occurred.", - "mongo_type": "string", - "permitted_values": [], - "placeholder": "DD", - "public": true, - "required": false, - "short_description": "The day on which the incident occurred. If a precise date is unavailable, leave blank.\n\nEnter in the format of DD", - "short_name": "Date of Incident Day", - "weight": 5 - }, - { - "__typename": "TaxaField_list", - "default": null, - "display_type": "bool", - "field_number": "6.4", - "instant_facet": false, - "long_description": "“Yes” if the data was estimated. “No” otherwise.", - "long_name": "Is the date estimated?", - "mongo_type": "bool", - "permitted_values": [], - "placeholder": "", - "public": true, - "required": false, - "short_description": "“Yes” if the data was estimated. “No” otherwise.", - "short_name": "Estimated Date", - "weight": 5 - }, - { - "__typename": "TaxaField_list", - "default": "maybe", - "display_type": "enum", - "field_number": "6.5", - "instant_facet": true, - "long_description": "This happens very rarely but is possible. Examples include two chatbots having a conversation with each other, or two autonomous vehicles in a crash.", - "long_name": "Was the AI interacting with another AI?", - "mongo_type": "string", - "permitted_values": [ - "yes", - "no", - "maybe" - ], - "placeholder": "", - "public": true, - "required": false, - "short_description": "“Yes” if two or more independently operating AI systems were involved. “No” otherwise.", - "short_name": "Multiple AI Interaction", - "weight": 5 - }, - { - "__typename": "TaxaField_list", - "default": "maybe", - "display_type": "enum", - "field_number": "6.6", - "instant_facet": true, - "long_description": "This question is slightly different from the one in field 2.1.1. That question asks about there being interaction with physical objects–an ability to manipulate or change. A system can be embedded in a physical object and able to interact with the physical environment, e.g. a vacuum robot. A system can be embedded in a physical object and not interact with a physical environment, e.g. a camera system that only records images when the AI detects that dogs are present. AI systems that are accessed through API, web-browser, etc by using a mobile device or computer are not considered to be embedded in hardware systems. They are accessed through hardware.", - "long_name": "Is the AI embedded in a physical system or have a physical presence?", - "mongo_type": "string", - "permitted_values": [ - "yes", - "no", - "maybe" - ], - "placeholder": "", - "public": true, - "required": false, - "short_description": "“Yes” if the AI is embedded in a physical system. “No” if it is not. “Maybe” if it is unclear.", - "short_name": "Embedded", - "weight": 5 - }, - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "string", - "field_number": "6.7", - "instant_facet": false, - "long_description": "If the incident occurred at a specific known location, note the city. If there are multiple relevant locations, enter multiple city/state/country values.", - "long_name": "If the incident occurred at a specific known location, note the city.", - "mongo_type": "string", - "permitted_values": [], - "placeholder": "City", - "public": true, - "required": false, - "short_description": "If the incident occurred at a specific known location, note the city.", - "short_name": "Location City", - "weight": 5 - }, - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "string", - "field_number": "6.8", - "instant_facet": false, - "long_description": "If the incident occurred at a specific known location, note the state/province. If there are multiple relevant locations, enter multiple city/state/country values.", - "long_name": "If the incident occurred at a specific known location, note the state/province.", - "mongo_type": "string", - "permitted_values": [], - "placeholder": "State or Province", - "public": true, - "required": false, - "short_description": "If the incident occurred at a specific known location, note the state/province.", - "short_name": "Location State/Province (two letters)", - "weight": 5 - }, - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "string", - "field_number": "6.9", - "instant_facet": false, - "long_description": "Follow ISO 3166 for the 2-letter country codes.\n\nIf there are multiple relevant locations, enter multiple city/state/country values.", - "long_name": "If the incident occurred at a specific known location, note the country. ", - "mongo_type": "string", - "permitted_values": [], - "placeholder": "Country", - "public": true, - "required": false, - "short_description": "If the incident occurred at a specific known location, note the country. Follow ISO 3166 for the 2-letter country codes.", - "short_name": "Location Country (two letters)", - "weight": 5 - }, - { - "__typename": "TaxaField_list", - "default": "unclear", - "display_type": "enum", - "field_number": "6.10", - "instant_facet": true, - "long_description": "Use this reference to map countries to regions: https://www.dhs.gov/geographic-regions", - "long_name": "Location Region", - "mongo_type": "string", - "permitted_values": [ - "Global", - "Africa", - "Asia", - "Caribbean", - "Central America", - "Europe", - "North America", - "Oceania", - "South America", - "unclear" - ], - "placeholder": "", - "public": true, - "required": false, - "short_description": "Select the region of the world where the incident occurred. If it occurred in multiple, leave blank.", - "short_name": "Location Region", - "weight": 5 - }, - { - "__typename": "TaxaField_list", - "default": "unclear", - "display_type": "multi", - "field_number": "6.11", - "instant_facet": true, - "long_description": "Which critical infrastructure sectors were affected, if any?", - "long_name": "Which critical infrastructure sectors were affected, if any?", - "mongo_type": "string", - "permitted_values": [ - "chemical", - "commercial facilities", - "communications", - "critical manufacturing", - "dams", - "defense-industrial base", - "emergency services", - "energy", - "financial services", - "food and agriculture", - "government facilities", - "healthcare and public health", - "information technology", - "nuclear ", - "transportation", - "water and wastewater", - "Other", - "unclear" - ], - "placeholder": "", - "public": true, - "required": false, - "short_description": "Which critical infrastructure sectors were affected, if any?", - "short_name": "Infrastructure Sectors", - "weight": 5 - }, - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "list", - "field_number": "6.12", - "instant_facet": false, - "long_description": "A record of any abnormal or atypical operational conditions that occurred. This field is most often blank.", - "long_name": "A record of any abnormal or atypical operational conditions that occurred.", - "mongo_type": "array", - "permitted_values": [], - "placeholder": "e.g. raining; night; low visibility", - "public": true, - "required": false, - "short_description": "A record of any abnormal or atypical operational conditions that occurred.", - "short_name": "Operating Conditions", - "weight": 5 - }, - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "long_string", - "field_number": "6.13", - "instant_facet": false, - "long_description": "Input any notes that may help explain your answers.", - "long_name": "Notes (Environmental and Temporal Characteristics)", - "mongo_type": "string", - "permitted_values": [], - "placeholder": "Notes", - "public": true, - "required": false, - "short_description": "Input any notes that may help explain your answers.", - "short_name": "Notes (Environmental and Temporal Characteristics)", - "weight": 5 - }, - { - "__typename": "TaxaField_list", - "default": "[]", - "display_type": "object-list", - "field_number": "7", - "instant_facet": false, - "long_description": "Characterizing Entities and the Harm", - "long_name": "Characterizing Entities and the Harm", - "mongo_type": "array", - "permitted_values": [], - "placeholder": "", - "public": true, - "required": false, - "short_description": "Characterizing Entities and the Harm", - "short_name": "Entities", - "weight": 5 - }, - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "int", - "field_number": "8.1", - "instant_facet": true, - "long_description": "This field cannot be greater than zero if the harm is anything besides ‘Physical health/safety.’ ", - "long_name": "How many human lives were lost?", - "mongo_type": "int", - "permitted_values": [], - "placeholder": "", - "public": true, - "required": false, - "short_description": "Indicates the number of deaths reported", - "short_name": "Lives Lost", - "weight": 5 - }, - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "int", - "field_number": "8.2", - "instant_facet": false, - "long_description": "This field cannot be greater than zero if the harm is anything besides 'Physical health/safety'.\n\nAll reported injuries should count, regardless of their severity level. If a person lost their limb and another person scraped their elbow, both cases would be considered injuries. Do not include the number of deaths in this count.", - "long_name": "How many humans were injured?", - "mongo_type": "int", - "permitted_values": [], - "placeholder": "", - "public": true, - "required": false, - "short_description": "Indicate the number of injuries reported.", - "short_name": "Injuries", - "weight": 5 - }, - { - "__typename": "TaxaField_list", - "default": null, - "display_type": "bool", - "field_number": "8.3", - "instant_facet": false, - "long_description": "Indicates if the amount was estimated.", - "long_name": "Are any quantities estimated?", - "mongo_type": "bool", - "permitted_values": [], - "placeholder": "", - "public": true, - "required": false, - "short_description": "Indicates if the amount was estimated.", - "short_name": "Estimated Harm Quantities", - "weight": 5 - }, - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "long_string", - "field_number": "8.4", - "instant_facet": false, - "long_description": "Input any notes that may help explain your answers.", - "long_name": "Notes ( Tangible Harm Quantities Information)", - "mongo_type": "string", - "permitted_values": [], - "placeholder": "Notes", - "public": true, - "required": false, - "short_description": "Input any notes that may help explain your answers.", - "short_name": "Notes ( Tangible Harm Quantities Information)", - "weight": 5 - }, - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "long_string", - "field_number": "9.1", - "instant_facet": false, - "long_description": "Describe the AI system in as much detail as the reports will allow.\n\nA high level description of the AI system is sufficient, but if more technical details about the AI system are available, include them in the description as well.", - "long_name": "Description of the AI system involved", - "mongo_type": "string", - "permitted_values": [], - "placeholder": "Description of the AI system involved", - "public": true, - "required": false, - "short_description": "A description of the AI system (when possible)", - "short_name": "AI System Description", - "weight": 5 - }, - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "list", - "field_number": "9.2", - "instant_facet": false, - "long_description": "This is a freeform field that can have any value. There could be multiple entries for this field.\n\nCommon ones include\n\n- still images\n- video\n- text\n- speech\n- Personally Identifiable Information\n- structured data\n- other\n- unclear\n\nStill images are static images. Video images consist of moving images. Text and speech data are considered an important category of unstructured data. They consist of written and spoken words that are not in a tabular format. Personally identifiable information is data that can uniquely identify an individual and may contain sensitive information. Structured data is often in a tabular, machine readable format and can typically be used by an AI system without much preprocessing.\n\nAvoid using ‘unstructured data’ data in this field. Instead specify the type of unstructured data; text, images, audio files, etc. It is ok to use ‘structured data’ in this field.\n\nRecord what the media report explicitly states. If the report does not explicitly state an input modality but it is likely that a particular kind of input contributed to the harm or near harm, record that input. If you are still unsure, do not record anything.", - "long_name": "Description of data inputs to the AI system", - "mongo_type": "array", - "permitted_values": [], - "placeholder": "", - "public": true, - "required": false, - "short_description": "A list of the types of data inputs for the AI system.", - "short_name": "Data Inputs", - "weight": 5 - }, - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "multi", - "field_number": "9.3", - "instant_facet": true, - "long_description": "Indicate the sector in which the AI system is deployed\n\nThere could be multiple entries for this field.", - "long_name": "Indicates the sector in which the AI system is deployed", - "mongo_type": "array", - "permitted_values": [ - "agriculture, forestry and fishing", - "mining and quarrying", - "manufacturing", - "electricity, gas, steam and air conditioning supply", - "water supply", - "construction", - "wholesale and retail trade", - "transportation and storage", - "accommodation and food service activities", - "information and communication", - "financial and insurance activities", - "real estate activities", - "professional, scientific and technical activities", - "administrative and support service activities", - "public administration", - "defense", - "law enforcement", - "Education", - "human health and social work activities", - "Arts, entertainment and recreation", - "other service activities", - "activities of households as employers", - "activities of extraterritorial organizations and bodies", - "other", - "unclear" - ], - "placeholder": "", - "public": true, - "required": false, - "short_description": "Indicate the sector in which the AI system is deployed", - "short_name": "Sector of Deployment", - "weight": 50 - }, - { - "__typename": "TaxaField_list", - "default": "maybe", - "display_type": "enum", - "field_number": "9.4", - "instant_facet": true, - "long_description": "Indicate whether the AI system is deployed in the public sector. The public sector is the part of the economy that is controlled and operated by the government.", - "long_name": "Indicates whether the AI system is deployed in the public sector", - "mongo_type": "string", - "permitted_values": [ - "yes", - "no", - "maybe" - ], - "placeholder": "", - "public": true, - "required": false, - "short_description": "Indicate whether the AI system is deployed in the public sector", - "short_name": "Public Sector Deployment", - "weight": 50 - }, - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "enum", - "field_number": "9.5", - "instant_facet": true, - "long_description": "Autonomy1: The system operates independently without simultaneous human oversight, interaction, or intervention.\n\nAutonomy2: The system operates independently but with human oversight, where a human can observe and override the system’s decisions in real time.\n\nAutonomy3: The system does not independently make decisions but instead provides information to a human who actively chooses to proceed with the AI’s information.", - "long_name": "Autonomy Level", - "mongo_type": "string", - "permitted_values": [ - "Autonomy1", - "Autonomy2", - "Autonomy3", - "unclear" - ], - "placeholder": "", - "public": true, - "required": false, - "short_description": "Autonomy1: The system operates independently without simultaneous human oversight, interaction, or intervention.\n\nAutonomy2: The system operates independently but with human oversight, where a human can observe and override the system’s decisions in real time.\n\nAutonomy3: The system does not independently make decisions but instead provides information to a human who actively chooses to proceed with the AI’s information.", - "short_name": "Autonomy Level", - "weight": 50 - }, - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "long_string", - "field_number": "9.8", - "instant_facet": false, - "long_description": "Input any notes that may help explain your answers.", - "long_name": "Notes (Information about AI System)", - "mongo_type": "string", - "permitted_values": [], - "placeholder": "Notes", - "public": true, - "required": false, - "short_description": "Input any notes that may help explain your answers.", - "short_name": "Notes (Information about AI System)", - "weight": 5 - }, - { - "__typename": "TaxaField_list", - "default": "unclear", - "display_type": "enum", - "field_number": "10.1", - "instant_facet": true, - "long_description": "Indicates if the system was designed to do harm. If it was designed to perform harm, the field will indicate if the AI system did or did not create unintended harm–i.e. was the reported harm the harm that AI was expected to perform or a different unexpected harm? ", - "long_name": "Was the AI intentionally developed or deployed to perform the harm?", - "mongo_type": "string", - "permitted_values": [ - "Yes. Intentionally designed to perform harm and did create intended harm", - "Yes. Intentionally designed to perform harm but created an unintended harm (a different harm may have occurred)", - "No. Not intentionally designed to perform harm", - "unclear" - ], - "placeholder": "", - "public": true, - "required": false, - "short_description": "Was the AI intentionally developed or deployed to perform the harm?\n\nIf yes, did the AI’s behavior result in unintended or intended harm? ", - "short_name": "Intentional Harm", - "weight": 50 - }, - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "string", - "field_number": "10.2", - "instant_facet": false, - "long_description": "Describe the type of physical system that the AI was integrated into. ", - "long_name": "Into what type of physical system was the AI integrated, if any?", - "mongo_type": "string", - "permitted_values": [], - "placeholder": "Physical System Type (e.g. trash sorting robot)", - "public": true, - "required": false, - "short_description": "Describe the type of physical system that the AI was integrated into.", - "short_name": "Physical System Type", - "weight": 5 - }, - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "list", - "field_number": "10.3", - "instant_facet": false, - "long_description": "Describe the AI’s application.\n\nIt is likely that the annotator will not have enough information to complete this field. If this occurs, enter unclear.\n\nThis is a freeform field. Some possible entries are\n\n- unclear\n- human language technologies\n- computer vision\n- robotics\n- automation and/or optimization\n- other\n\nThe application area of an AI is the high level task that the AI is intended to perform. It does not describe the technical methods by which the AI performs the task. Considering what an AI’s technical methods enable it to do is another way of arriving at what an AI’s application is. \n\nIt is possible for multiple application areas to be involved. When possible pick the principle or domain area, but it is ok to select multiple areas.", - "long_name": "AI task or core application area", - "mongo_type": "array", - "permitted_values": [], - "placeholder": "", - "public": true, - "required": false, - "short_description": "Describe the AI’s application.", - "short_name": "AI Task", - "weight": 5 - }, - { - "__typename": "TaxaField_list", - "default": "unclear", - "display_type": "list", - "field_number": "10.4", - "instant_facet": false, - "long_description": "Describe the tools and methods that enable the AI’s application.\n\nIt is likely that the annotator will not have enough information to complete this field. If this occurs, enter unclear\n\nThis is a freeform field. Some possible entries are\n\n- unclear\n- reinforcement learning\n- neural networks\n- decision trees\n- bias mitigation\n- optimization\n- classifier\n- NLP/text analytics\n- continuous learning\n- unsupervised learning\n- supervised learning\n- clustering\n- prediction\n- rules\n- random forest\n\nAI tools and methods are the technical building blocks that enable the AI’s application.", - "long_name": "AI tools and methods", - "mongo_type": "array", - "permitted_values": [], - "placeholder": "", - "public": true, - "required": false, - "short_description": "Describe the tools and methods that enable the AI’s application.", - "short_name": "AI tools and methods", - "weight": 50 - }, - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "long_string", - "field_number": "10.5", - "instant_facet": false, - "long_description": "Input any notes that may help explain your answers.", - "long_name": "Notes (AI Functionality and Techniques)", - "mongo_type": "string", - "permitted_values": [], - "placeholder": "Notes", - "public": true, - "required": false, - "short_description": "Input any notes that may help explain your answers.", - "short_name": "Notes (AI Functionality and Techniques)", - "weight": 5 - } - ], - "namespace": "CSETv1_Annotator-1", - "weight": 70 - }, - { - "__typename": "Taxa", - "description": "# What is the CSET Taxonomy?\n\nThe Center for Security and Emerging Technology (CSET) taxonomy is\na general taxonomy of AI incidents. There are a large number of \nclassified attributes, including ones pertaining to safety, fairness, \nindustry, geography, timing, and cost. All classifications within \nthe CSET taxonomy are first applied by one CSET annotator and \nreviewed by another CSET annotator before the classifications are \nfinalized. The combination of a rigorously defined coding set and \nthe completeness with which it has been applied make the CSET \ntaxonomy the AIID's gold standard for taxonomies. Nevertheless, \nthe CSET taxonomy is an ongoing effort and you are invited to \nreport any errors you may discover in its application.\n\n## How do I explore the taxonomy?\n\nAll taxonomies can be used to filter incident reports within the \n[Discover Application](/apps/discover). The taxonomy filters work\nsimilarly to how you filter products on an E-commerce website. \nUse the search field at the bottom of the “Classifications” tab to \nfind the taxonomy field you would like to filter with, then click \nthe desired value to apply the filter.\n\n# About CSET\n\nA policy research organization within Georgetown University’s \nWalsh School of Foreign Service, CSET produces data-driven \nresearch at the intersection of security and technology, \nproviding nonpartisan analysis to the policy community. CSET is \ncurrently focusing on the effects of progress in artificial \nintelligence (AI), advanced computing and biotechnology. CSET \nseeks to prepare a new generation of decision-makers to address \nthe challenges and opportunities of emerging technologies. [(Read \nmore)](https://cset.georgetown.edu/about-us/).", - "field_list": [ - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "int", - "field_number": "1.1", - "instant_facet": false, - "long_description": "The number of the incident in the AI Incident Database.", - "long_name": "The number of the incident in the AI Incident Database.", - "mongo_type": "int", - "permitted_values": null, - "placeholder": null, - "public": true, - "required": false, - "short_description": "The number of the incident in the AI Incident Database.", - "short_name": "Incident Number", - "weight": 5 - }, - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "string", - "field_number": "1.2", - "instant_facet": false, - "long_description": "An ID designating the individual who classified this incident according to the CSET taxonomy.", - "long_name": "Person responsible for the annotations", - "mongo_type": "string", - "permitted_values": [], - "placeholder": "Select name here", - "public": false, - "required": false, - "short_description": "This is the researcher that is responsible for applying the classifications of the CSET taxonomy.", - "short_name": "Annotator", - "weight": 5 - }, - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "enum", - "field_number": "1.3", - "instant_facet": false, - "long_description": "What is the quality assurance status of the CSET classifications for this incident?", - "long_name": "Where in the annotation process is this incident?", - "mongo_type": "string", - "permitted_values": [ - "1. Annotation in progress", - "2. Initial annotation complete", - "3. In peer review", - "4. Peer review complete", - "5. In quality control", - "6. Complete and final" - ], - "placeholder": "Select process status here", - "public": false, - "required": false, - "short_description": "What is the quality assurance status of the CSET classifications for this incident?", - "short_name": "Annotation Status", - "weight": 10 - }, - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "string", - "field_number": "1.4", - "instant_facet": false, - "long_description": "The CSET taxonomy assigns individual researchers to each incident as the primary parties responsible for classifying the incident according to the taxonomy. This is the person responsible for assuring the integrity of annotator's classifications.", - "long_name": "Person responsible for reviewing annotations", - "mongo_type": "string", - "permitted_values": [], - "placeholder": "Select name here", - "public": false, - "required": false, - "short_description": "This is the researcher that is responsible for ensuring the quality of the classifications applied to this incident.", - "short_name": "Peer Reviewer", - "weight": 20 - }, - { - "__typename": "TaxaField_list", - "default": "false", - "display_type": "bool", - "field_number": "1.5", - "instant_facet": false, - "long_description": "The peer review process sometimes uncovers issues with the classifications that have been applied by the annotator. This field serves as a flag when there is a need for additional thought and input on the classifications applied", - "long_name": "Was this incident randomly selected for additional quality control?", - "mongo_type": "bool", - "permitted_values": [], - "placeholder": "", - "public": false, - "required": false, - "short_description": "Has someone flagged a potential issue with this incident's classifications? Annotators should leave this field blank.", - "short_name": "Quality Control", - "weight": 15 - }, - { - "__typename": "TaxaField_list", - "default": "maybe", - "display_type": "enum", - "field_number": "2.1", - "instant_facet": true, - "long_description": "“Yes” if the AI system(s) is embedded in hardware that can interact with, affect, and change the physical objects (cars, robots, medical facilities, etc.). Mark “No” if the system cannot. This includes systems that inform, detect, predict, or recommend.", - "long_name": "Did the incident occur in a domain with physical objects ?", - "mongo_type": "string", - "permitted_values": [ - "yes", - "no", - "maybe" - ], - "placeholder": "", - "public": null, - "required": false, - "short_description": "Did the incident occur in a domain with physical objects ?", - "short_name": "Physical Objects", - "weight": 50 - }, - { - "__typename": "TaxaField_list", - "default": "maybe", - "display_type": "enum", - "field_number": "2.2", - "instant_facet": true, - "long_description": "“Yes” if the sector in which the AI was used is associated with entertainment. “No” if it was used in a different, clearly identifiable sector. “Maybe” if the sector of use could not be determined.", - "long_name": "Did the AI incident occur in the entertainment industry?", - "mongo_type": "string", - "permitted_values": [ - "yes", - "no", - "maybe" - ], - "placeholder": "", - "public": null, - "required": false, - "short_description": "Did the AI incident occur in the entertainment industry?", - "short_name": "Entertainment Industry", - "weight": 50 - }, - { - "__typename": "TaxaField_list", - "default": "maybe", - "display_type": "enum", - "field_number": "2.3", - "instant_facet": true, - "long_description": "“Yes” if the incident is about a report, test, or study of the data and does not discuss an instance of injury, damage, or loss. “Maybe” if it is unclear. Otherwise mark “No.”", - "long_name": "Was the incident about a report, test, or study of data instead of the AI itself?", - "mongo_type": "string", - "permitted_values": [ - "yes", - "no", - "maybe" - ], - "placeholder": "", - "public": null, - "required": false, - "short_description": "Was the incident about a report, test, or study of data instead of the AI itself?", - "short_name": "Report, Test, or Study of data", - "weight": 50 - }, - { - "__typename": "TaxaField_list", - "default": "maybe", - "display_type": "enum", - "field_number": "2.4", - "instant_facet": true, - "long_description": "“Yes” if the involved system was deployed or sold to users. “No” if it was not. “Maybe” if there is not enough information or if the use is unclear.", - "long_name": "Was the reported system (even if AI involvement is unknown) deployed or sold to users?", - "mongo_type": "string", - "permitted_values": [ - "yes", - "no", - "maybe" - ], - "placeholder": "", - "public": null, - "required": false, - "short_description": "Was the reported system (even if AI involvement is unknown) deployed or sold to users?", - "short_name": "Deployed", - "weight": 50 - }, - { - "__typename": "TaxaField_list", - "default": "maybe", - "display_type": "enum", - "field_number": "2.5", - "instant_facet": true, - "long_description": "“Yes” if it was a test/demonstration performed by developers, producers or journalists in controlled conditions. “No” if it was not a test/demonstration. “No” if the test/demonstration was done by a user. “No” if the test/demonstration was in operational or uncontrolled conditions. “Maybe” otherwise.", - "long_name": "Was this a test or demonstration of an AI system done by developers, producers or researchers (versus users) in controlled conditions?", - "mongo_type": "string", - "permitted_values": [ - "yes", - "no", - "maybe" - ], - "placeholder": "", - "public": null, - "required": false, - "short_description": "Was this a test or demonstration of an AI system done by developers, producers or researchers (versus users) in controlled conditions?", - "short_name": "Producer Test in Controlled Conditions", - "weight": 50 - }, - { - "__typename": "TaxaField_list", - "default": "maybe", - "display_type": "enum", - "field_number": "2.6", - "instant_facet": true, - "long_description": "“Yes” if it was a test/demonstration performed by developers, producers or journalists in controlled conditions. “No” if it was not a test/demonstration. “No” if the test/demonstration was done by a user. “No” if the test/demonstration was in controlled or non-operational conditions. “Maybe” otherwise.", - "long_name": "Was this a test or demonstration of an AI system done by developers, producers or researchers (versus users) in operational conditions?", - "mongo_type": "string", - "permitted_values": [ - "yes", - "no", - "maybe" - ], - "placeholder": "", - "public": null, - "required": false, - "short_description": "Was this a test or demonstration of an AI system done by developers, producers or researchers (versus users) in operational conditions?", - "short_name": "Producer Test in Operational Conditions", - "weight": 50 - }, - { - "__typename": "TaxaField_list", - "default": "maybe", - "display_type": "enum", - "field_number": "2.7", - "instant_facet": true, - "long_description": "“Yes” if it was a test/demonstration performed by users in controlled conditions. “No” if it was not a test/demonstration. “No” if the test/demonstration was done by developers, producers or researchers. “No” if the test/demonstration was in controlled or non-controlled conditions.“Maybe” otherwise.", - "long_name": "Was this a test or demonstration done by users in controlled conditions?", - "mongo_type": "string", - "permitted_values": [ - "yes", - "no", - "maybe" - ], - "placeholder": "", - "public": null, - "required": false, - "short_description": "Was this a test or demonstration done by users in controlled conditions?", - "short_name": "User Test in Controlled Conditions", - "weight": 50 - }, - { - "__typename": "TaxaField_list", - "default": "maybe", - "display_type": "enum", - "field_number": "2.8", - "instant_facet": true, - "long_description": "“Yes” if it was a test/demonstration performed by users in operational conditions. “No” if it was not a test/demonstration. “No” if the test/demonstration was done by developers, producers or researchers. “No” if the test/demonstration was in controlled or non-operational conditions.“Maybe” otherwise.", - "long_name": "Was this a test or demonstration done by users in operational conditions?", - "mongo_type": "string", - "permitted_values": [ - "yes", - "no", - "maybe" - ], - "placeholder": "", - "public": null, - "required": false, - "short_description": "Was this a test or demonstration done by users in operational conditions?", - "short_name": "User Test in Operational Conditions", - "weight": 50 - }, - { - "__typename": "TaxaField_list", - "default": "maybe", - "display_type": "enum", - "field_number": "2.9", - "instant_facet": false, - "long_description": "Using the answers to the 8 domain questions, assess if the incident occurred in a domain where harm could be expected to occur. If you are unclear, input “maybe.”", - "long_name": "Incident occurred in a domain where we could expect harm to occur?", - "mongo_type": "string", - "permitted_values": [ - "yes", - "no", - "maybe" - ], - "placeholder": "", - "public": null, - "required": false, - "short_description": "Incident occurred in a domain where we could likely expect harm to occur?", - "short_name": "Harm Domain", - "weight": 50 - }, - { - "__typename": "TaxaField_list", - "default": "unclear", - "display_type": "enum", - "field_number": "3.1", - "instant_facet": true, - "long_description": "An assessment of whether tangible harm, imminent tangible harm, or non-imminent tangible harm occurred. This assessment does not consider the context of the tangible harm, if an AI was involved, or if there is an identifiable, specific, and harmed entity. It is also not assessing if an intangible harm occurred. It is only asking if tangible harm occurred and what its imminency was.", - "long_name": "Did tangible harm (loss, damage or injury ) occur? ", - "mongo_type": "string", - "permitted_values": [ - "tangible harm definitively occurred", - "imminent risk of tangible harm (near miss) did occur", - "non-imminent risk of tangible harm (an issue) occurred", - "no tangible harm, near-miss, or issue", - "unclear" - ], - "placeholder": "", - "public": true, - "required": false, - "short_description": "Did tangible harm (loss, damage or injury ) occur? ", - "short_name": "Tangible Harm", - "weight": 50 - }, - { - "__typename": "TaxaField_list", - "default": "maybe", - "display_type": "enum", - "field_number": "3.2", - "instant_facet": true, - "long_description": "An assessment of whether or not an AI system was involved. It is sometimes difficult to judge between an AI and an automated system or expert rules system. In these cases select “maybe”", - "long_name": "Does the incident involve an AI system?", - "mongo_type": "string", - "permitted_values": [ - "yes", - "no", - "maybe" - ], - "placeholder": "", - "public": true, - "required": false, - "short_description": "Does the incident involve an AI system?", - "short_name": "AI System", - "weight": 50 - }, - { - "__typename": "TaxaField_list", - "default": "maybe", - "display_type": "enum", - "field_number": "3.3", - "instant_facet": true, - "long_description": "“Yes” if an AI was involved in harm, its behavior can be directly linked to the harm, and the harm may not have occurred if the AI acted differently. “Maybe” if the link is unclear. Otherwise, select “no.”", - "long_name": "Can an AI be directly and clearly linked to tangible harm?", - "mongo_type": "string", - "permitted_values": [ - "yes", - "no", - "maybe" - ], - "placeholder": "", - "public": true, - "required": false, - "short_description": "Can an AI be directly and clearly linked to tangible harm?", - "short_name": "Clear Link to AI", - "weight": 50 - }, - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "bool", - "field_number": "3.4", - "instant_facet": true, - "long_description": "“Yes” if it is theoretically possible to both specify and identify the entity. Having that information is not required. The information just needs to exist and be potentially discoverable. “No” if there are not any potentially identifiable specific entities or if the harmed entities are a class or subgroup that can only be characterized. ", - "long_name": "There is a potentially identifiable specific entity that experienced the harm", - "mongo_type": "bool", - "permitted_values": [], - "placeholder": "", - "public": true, - "required": false, - "short_description": "A potentially identifiable specific entity that experienced the harm can be characterized or identified.", - "short_name": "There is a potentially identifiable specific entity that experienced the harm", - "weight": 50 - }, - { - "__typename": "TaxaField_list", - "default": "unclear", - "display_type": "enum", - "field_number": "3.5", - "instant_facet": true, - "long_description": "An assessment of the AI tangible harm level, which takes into account the CSET definitions of AI tangible harm levels, along with the inputs for annotation fields about the AI, harm, chain of harm, and entity.", - "long_name": "Annotator's AI tangible harm level assessment", - "mongo_type": "string", - "permitted_values": [ - "AI tangible harm event", - "AI tangible harm near-miss", - "AI tangible harm issue", - "none", - "unclear" - ], - "placeholder": "", - "public": true, - "required": false, - "short_description": "An assessment of the AI tangible harm level, which takes into account the CSET definitions of AI tangible harm levels, along with the inputs for annotation fields about the AI, harm, chain of harm, and entity. ", - "short_name": "AI Harm Level", - "weight": 50 - }, - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "long_string", - "field_number": "3.6", - "instant_facet": false, - "long_description": "If for 3.5 you select unclear or leave it blank, please provide a brief description of why.\n\n You can also add notes if you want to provide justification for a level", - "long_name": "AI Tangible Harm Level Notes", - "mongo_type": "string", - "permitted_values": [], - "placeholder": "Notes about the AI tangible harm level assessment", - "public": true, - "required": false, - "short_description": "Notes about the AI tangible harm level assessment", - "short_name": "AI Tangible Harm Level Notes", - "weight": 5 - }, - { - "__typename": "TaxaField_list", - "default": "maybe", - "display_type": "enum", - "field_number": "4.1", - "instant_facet": true, - "long_description": "Did this impact people's access to critical or public services (health care, social services, voting, transportation, etc)?", - "long_name": "Did this impact people's access to critical or public services (health care, social services, voting, transportation, etc)?", - "mongo_type": "string", - "permitted_values": [ - "yes", - "no", - "maybe" - ], - "placeholder": "", - "public": true, - "required": false, - "short_description": "Indicates if people’s access to critical public services was impacted.", - "short_name": "Impact on Critical Services", - "weight": 50 - }, - { - "__typename": "TaxaField_list", - "default": "maybe", - "display_type": "enum", - "field_number": "4.2", - "instant_facet": true, - "long_description": "Indicate if a violation of human rights, civil rights, civil liberties, or democratic norms occurred.", - "long_name": "Was this a violation of human rights, civil liberties, civil rights, or democratic norms?", - "mongo_type": "string", - "permitted_values": [ - "yes", - "no", - "maybe" - ], - "placeholder": "", - "public": true, - "required": false, - "short_description": "Indicate if a violation of human rights, civil rights, civil liberties, or democratic norms occurred.", - "short_name": "Rights Violation", - "weight": 50 - }, - { - "__typename": "TaxaField_list", - "default": "maybe", - "display_type": "enum", - "field_number": "4.3", - "instant_facet": true, - "long_description": "Indicate if a minor was disproportionately targeted or affected", - "long_name": "Was a minor involved in the incident (disproportionally treated or specifically targeted/affected)", - "mongo_type": "string", - "permitted_values": [ - "yes", - "no", - "maybe" - ], - "placeholder": "", - "public": true, - "required": false, - "short_description": "Was a minor involved in the incident (disproportionally treated or specifically targeted/affected)", - "short_name": "Involving Minor", - "weight": 50 - }, - { - "__typename": "TaxaField_list", - "default": "maybe", - "display_type": "enum", - "field_number": "4.4", - "instant_facet": true, - "long_description": "Detrimental content can include deepfakes, identity misrepresentation, insults, threats of violence, eating disorder or self harm promotion, extremist content, misinformation, sexual abuse material, and scam emails. Detrimental content in itself is often not harmful, however, it can lead to or instigate injury, damage, or loss.", - "long_name": "Was detrimental content (misinformation, hate speech) involved?", - "mongo_type": "string", - "permitted_values": [ - "yes", - "no", - "maybe" - ], - "placeholder": "", - "public": true, - "required": false, - "short_description": "Was detrimental content (misinformation, hate speech) involved?", - "short_name": "Detrimental Content", - "weight": 50 - }, - { - "__typename": "TaxaField_list", - "default": "maybe", - "display_type": "enum", - "field_number": "4.5", - "instant_facet": true, - "long_description": "Protected characteristics include religion, commercial facilities, geography, age, sex, sexual orientation or gender identity, familial status (e.g., having or not having children) or pregnancy, disability, veteran status, genetic information, financial means, race or creed, Ideology, nation of origin, citizenship, and immigrant status.\n\nAt the federal level in the US, age is a protected characteristic for people over the age of 40. Minors are not considered a protected class. For this reason the CSET annotation taxonomy has a separate field to note if a minor was involved.\n\nOnly mark yes if there is clear evidence discrimination occurred. If there are conflicting accounts, mark unsure. Do not mark that discrimination occurred based on expectation alone.", - "long_name": "Was a group of people or an individual treated differently based upon a protected characteristic?", - "mongo_type": "string", - "permitted_values": [ - "yes", - "no", - "maybe" - ], - "placeholder": "", - "public": true, - "required": false, - "short_description": "Was a group of people treated differently based upon a protected characteristic (e.g. race, ethnicity, creed, immigrant status, color, religion, sex, national origin, age, disability, genetic information)?", - "short_name": "Protected Characteristic", - "weight": 50 - }, - { - "__typename": "TaxaField_list", - "default": "unclear", - "display_type": "multi", - "field_number": "4.6", - "instant_facet": true, - "long_description": "Multiple can occur.\n\nGenetic information refers to information about a person’s genetic tests or the genetic tests of their relatives. Genetic information can predict the manifestation of a disease or disorder.", - "long_name": "If harms were potentially unevenly distributed among people, on what basis?", - "mongo_type": "string", - "permitted_values": [ - "none", - "age", - "disability", - "familial status (e.g., having or not having children) or pregnancy", - "financial means", - "genetic information", - "geography", - "ideology", - "nation of origin, citizenship, immigrant status", - "race", - "religion", - "sex", - "sexual orientation or gender identity", - "veteran status", - "unclear", - "other" - ], - "placeholder": "", - "public": true, - "required": false, - "short_description": "Indicates how the harms were potentially distributed.", - "short_name": "Harm Distribution Basis", - "weight": 50 - }, - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "long_string", - "field_number": "4.7", - "instant_facet": false, - "long_description": "Input any notes that may help explain your answers.", - "long_name": "Input any notes that may help explain your answers.", - "mongo_type": "string", - "permitted_values": [], - "placeholder": "Notes", - "public": true, - "required": false, - "short_description": "Input any notes that may help explain your answers.", - "short_name": "Notes (special interest intangible harm)", - "weight": 5 - }, - { - "__typename": "TaxaField_list", - "default": "maybe", - "display_type": "enum", - "field_number": "5.1", - "instant_facet": false, - "long_description": "An assessment of whether a special interest intangible harm occurred. This assessment does not consider the context of the intangible harm, if an AI was involved, or if there is characterizable class or subgroup of harmed entities. It is also not assessing if an intangible harm occurred. It is only asking if a special interest intangible harm occurred.", - "long_name": "Was there a special interest intangible harm or risk of harm?", - "mongo_type": "string", - "permitted_values": [ - "yes", - "no", - "maybe" - ], - "placeholder": "", - "public": true, - "required": false, - "short_description": "An assessment of whether a special interest intangible harm occurred. This assessment does not consider the context of the intangible harm, if an AI was involved, or if there is characterizable class or subgroup of harmed entities. It is also not assessing if an intangible harm occurred. It is only asking if a special interest intangible harm occurred.", - "short_name": "Special Interest Intangible Harm", - "weight": 5 - }, - { - "__typename": "TaxaField_list", - "default": "maybe", - "display_type": "enum", - "field_number": "5.2", - "instant_facet": true, - "long_description": "An assessment of whether or not an AI system was involved. It is sometimes difficult to judge between an AI and an automated system or expert rules system. In these cases select “maybe”", - "long_name": "Does the incident involve an AI system?", - "mongo_type": "string", - "permitted_values": [ - "yes", - "no", - "maybe" - ], - "placeholder": "", - "public": true, - "required": false, - "short_description": "Does the incident involve an AI system?", - "short_name": "AI System", - "weight": 50 - }, - { - "__typename": "TaxaField_list", - "default": "maybe", - "display_type": "enum", - "field_number": "5.3", - "instant_facet": true, - "long_description": "“Yes” if an AI was involved in harm, its behavior can be directly linked to the harm, and the harm may not have occurred if the AI acted differently. “Maybe” if the link is unclear. Otherwise, select “no.”", - "long_name": "Can an AI be directly and clearly linked to the special interest intangible harm?", - "mongo_type": "string", - "permitted_values": [ - "yes", - "no", - "maybe" - ], - "placeholder": "", - "public": true, - "required": false, - "short_description": "Can an AI be directly and clearly linked to the special interest intangible harm?", - "short_name": "AI Linked to Special Interest Intangible Harm", - "weight": 50 - }, - { - "__typename": "TaxaField_list", - "default": null, - "display_type": "bool", - "field_number": "5.4", - "instant_facet": true, - "long_description": "A characterizable class or subgroup are descriptions of different populations of people. Often they are characteristics by which people qualify for special protection by a law, policy, or similar authority.\n\n Sometimes, groups may be characterized by their exposure to the incident via geographical proximity (e.g., ‘visitors to the park’) or participation in an activity (e.g.,‘Twitter users’).", - "long_name": "There is a characterizable class or subgroup of entities that experienced the harm", - "mongo_type": "bool", - "permitted_values": [], - "placeholder": "", - "public": true, - "required": false, - "short_description": "“Yes” if the harmed entity or entities can be characterized. “No” if there are not any characterizable entities.", - "short_name": "Harmed Class of Entities", - "weight": 50 - }, - { - "__typename": "TaxaField_list", - "default": "maybe", - "display_type": "enum", - "field_number": "5.5", - "instant_facet": true, - "long_description": "AI tangible harm is determined in a different field. The determination of a special interest intangible harm is not dependant upon the AI tangible harm level.", - "long_name": "The annotator’s assessment of if an AI special interest intangible harm occurred.", - "mongo_type": "string", - "permitted_values": [ - "yes", - "no", - "maybe" - ], - "placeholder": "", - "public": true, - "required": false, - "short_description": "The annotator’s assessment of if an AI special interest intangible harm occurred.", - "short_name": "Annotator’s AI special interest intangible harm assessment", - "weight": 50 - }, - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "long_string", - "field_number": "5.6", - "instant_facet": false, - "long_description": "If for 5.5 you select unclear or leave it blank, please provide a brief description of why.\n\nYou can also add notes if you want to provide justification for a level.", - "long_name": "If for 5.5 you select unclear or leave it blank, please provide a brief description of why.\n\nYou can also add notes if you want to provide justification for a level.", - "mongo_type": "string", - "permitted_values": [], - "placeholder": "Notes", - "public": true, - "required": false, - "short_description": "If for 5.5 you select unclear or leave it blank, please provide a brief description of why.\n\nYou can also add notes if you want to provide justification for a level.", - "short_name": "Notes (AI special interest intangible harm)", - "weight": 5 - }, - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "string", - "field_number": "6.1", - "instant_facet": false, - "long_description": "The year in which the incident occurred. If there are multiple harms or occurrences of the incident, list the earliest. If a precise date is unavailable, but the available sources provide a basis for estimating the year, estimate. Otherwise, leave blank.", - "long_name": "The year in which the incident first occurred.", - "mongo_type": "string", - "permitted_values": [], - "placeholder": "YYYY", - "public": true, - "required": false, - "short_description": "The year in which the incident occurred. If there are multiple harms or occurrences of the incident, list the earliest. If a precise date is unavailable, but the available sources provide a basis for estimating the year, estimate. Otherwise, leave blank.\n\nEnter in the format of YYYY", - "short_name": "Date of Incident Year", - "weight": 5 - }, - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "string", - "field_number": "6.2", - "instant_facet": false, - "long_description": "The month in which the incident occurred. If there are multiple harms or occurrences of the incident, list the earliest. If a precise date is unavailable, but the available sources provide a basis for estimating the month, estimate. Otherwise, leave blank.", - "long_name": "The month in which the incident first occurred.", - "mongo_type": "string", - "permitted_values": [], - "placeholder": "MM", - "public": true, - "required": false, - "short_description": "The month in which the incident occurred. If there are multiple harms or occurrences of the incident, list the earliest. If a precise date is unavailable, but the available sources provide a basis for estimating the month, estimate. Otherwise, leave blank.\n\nEnter in the format of MM", - "short_name": "Date of Incident Month", - "weight": 5 - }, - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "string", - "field_number": "6.3", - "instant_facet": true, - "long_description": "The day on which the incident occurred. If a precise date is unavailable, leave blank.\n\nEnter in the format of DD", - "long_name": "The day on which the first incident occurred.", - "mongo_type": "string", - "permitted_values": [], - "placeholder": "DD", - "public": true, - "required": false, - "short_description": "The day on which the incident occurred. If a precise date is unavailable, leave blank.\n\nEnter in the format of DD", - "short_name": "Date of Incident Day", - "weight": 5 - }, - { - "__typename": "TaxaField_list", - "default": null, - "display_type": "bool", - "field_number": "6.4", - "instant_facet": false, - "long_description": "“Yes” if the data was estimated. “No” otherwise.", - "long_name": "Is the date estimated?", - "mongo_type": "bool", - "permitted_values": [], - "placeholder": "", - "public": true, - "required": false, - "short_description": "“Yes” if the data was estimated. “No” otherwise.", - "short_name": "Estimated Date", - "weight": 5 - }, - { - "__typename": "TaxaField_list", - "default": "maybe", - "display_type": "enum", - "field_number": "6.5", - "instant_facet": true, - "long_description": "This happens very rarely but is possible. Examples include two chatbots having a conversation with each other, or two autonomous vehicles in a crash.", - "long_name": "Was the AI interacting with another AI?", - "mongo_type": "string", - "permitted_values": [ - "yes", - "no", - "maybe" - ], - "placeholder": "", - "public": true, - "required": false, - "short_description": "“Yes” if two or more independently operating AI systems were involved. “No” otherwise.", - "short_name": "Multiple AI Interaction", - "weight": 5 - }, - { - "__typename": "TaxaField_list", - "default": "maybe", - "display_type": "enum", - "field_number": "6.6", - "instant_facet": true, - "long_description": "This question is slightly different from the one in field 2.1.1. That question asks about there being interaction with physical objects–an ability to manipulate or change. A system can be embedded in a physical object and able to interact with the physical environment, e.g. a vacuum robot. A system can be embedded in a physical object and not interact with a physical environment, e.g. a camera system that only records images when the AI detects that dogs are present. AI systems that are accessed through API, web-browser, etc by using a mobile device or computer are not considered to be embedded in hardware systems. They are accessed through hardware.", - "long_name": "Is the AI embedded in a physical system or have a physical presence?", - "mongo_type": "string", - "permitted_values": [ - "yes", - "no", - "maybe" - ], - "placeholder": "", - "public": true, - "required": false, - "short_description": "“Yes” if the AI is embedded in a physical system. “No” if it is not. “Maybe” if it is unclear.", - "short_name": "Embedded", - "weight": 5 - }, - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "string", - "field_number": "6.7", - "instant_facet": false, - "long_description": "If the incident occurred at a specific known location, note the city. If there are multiple relevant locations, enter multiple city/state/country values.", - "long_name": "If the incident occurred at a specific known location, note the city.", - "mongo_type": "string", - "permitted_values": [], - "placeholder": "City", - "public": true, - "required": false, - "short_description": "If the incident occurred at a specific known location, note the city.", - "short_name": "Location City", - "weight": 5 - }, - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "string", - "field_number": "6.8", - "instant_facet": false, - "long_description": "If the incident occurred at a specific known location, note the state/province. If there are multiple relevant locations, enter multiple city/state/country values.", - "long_name": "If the incident occurred at a specific known location, note the state/province.", - "mongo_type": "string", - "permitted_values": [], - "placeholder": "State or Province", - "public": true, - "required": false, - "short_description": "If the incident occurred at a specific known location, note the state/province.", - "short_name": "Location State/Province (two letters)", - "weight": 5 - }, - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "string", - "field_number": "6.9", - "instant_facet": false, - "long_description": "Follow ISO 3166 for the 2-letter country codes.\n\nIf there are multiple relevant locations, enter multiple city/state/country values.", - "long_name": "If the incident occurred at a specific known location, note the country. ", - "mongo_type": "string", - "permitted_values": [], - "placeholder": "Country", - "public": true, - "required": false, - "short_description": "If the incident occurred at a specific known location, note the country. Follow ISO 3166 for the 2-letter country codes.", - "short_name": "Location Country (two letters)", - "weight": 5 - }, - { - "__typename": "TaxaField_list", - "default": "unclear", - "display_type": "enum", - "field_number": "6.10", - "instant_facet": true, - "long_description": "Use this reference to map countries to regions: https://www.dhs.gov/geographic-regions", - "long_name": "Location Region", - "mongo_type": "string", - "permitted_values": [ - "Global", - "Africa", - "Asia", - "Caribbean", - "Central America", - "Europe", - "North America", - "Oceania", - "South America", - "unclear" - ], - "placeholder": "", - "public": true, - "required": false, - "short_description": "Select the region of the world where the incident occurred. If it occurred in multiple, leave blank.", - "short_name": "Location Region", - "weight": 5 - }, - { - "__typename": "TaxaField_list", - "default": "unclear", - "display_type": "multi", - "field_number": "6.11", - "instant_facet": true, - "long_description": "Which critical infrastructure sectors were affected, if any?", - "long_name": "Which critical infrastructure sectors were affected, if any?", - "mongo_type": "string", - "permitted_values": [ - "chemical", - "commercial facilities", - "communications", - "critical manufacturing", - "dams", - "defense-industrial base", - "emergency services", - "energy", - "financial services", - "food and agriculture", - "government facilities", - "healthcare and public health", - "information technology", - "nuclear ", - "transportation", - "water and wastewater", - "Other", - "unclear" - ], - "placeholder": "", - "public": true, - "required": false, - "short_description": "Which critical infrastructure sectors were affected, if any?", - "short_name": "Infrastructure Sectors", - "weight": 5 - }, - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "list", - "field_number": "6.12", - "instant_facet": false, - "long_description": "A record of any abnormal or atypical operational conditions that occurred. This field is most often blank.", - "long_name": "A record of any abnormal or atypical operational conditions that occurred.", - "mongo_type": "array", - "permitted_values": [], - "placeholder": "e.g. raining; night; low visibility", - "public": true, - "required": false, - "short_description": "A record of any abnormal or atypical operational conditions that occurred.", - "short_name": "Operating Conditions", - "weight": 5 - }, - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "long_string", - "field_number": "6.13", - "instant_facet": false, - "long_description": "Input any notes that may help explain your answers.", - "long_name": "Notes (Environmental and Temporal Characteristics)", - "mongo_type": "string", - "permitted_values": [], - "placeholder": "Notes", - "public": true, - "required": false, - "short_description": "Input any notes that may help explain your answers.", - "short_name": "Notes (Environmental and Temporal Characteristics)", - "weight": 5 - }, - { - "__typename": "TaxaField_list", - "default": "[]", - "display_type": "object-list", - "field_number": "7", - "instant_facet": false, - "long_description": "Characterizing Entities and the Harm", - "long_name": "Characterizing Entities and the Harm", - "mongo_type": "array", - "permitted_values": [], - "placeholder": "", - "public": true, - "required": false, - "short_description": "Characterizing Entities and the Harm", - "short_name": "Entities", - "weight": 5 - }, - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "int", - "field_number": "8.1", - "instant_facet": true, - "long_description": "This field cannot be greater than zero if the harm is anything besides ‘Physical health/safety.’ ", - "long_name": "How many human lives were lost?", - "mongo_type": "int", - "permitted_values": [], - "placeholder": "", - "public": true, - "required": false, - "short_description": "Indicates the number of deaths reported", - "short_name": "Lives Lost", - "weight": 5 - }, - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "int", - "field_number": "8.2", - "instant_facet": false, - "long_description": "This field cannot be greater than zero if the harm is anything besides 'Physical health/safety'.\n\nAll reported injuries should count, regardless of their severity level. If a person lost their limb and another person scraped their elbow, both cases would be considered injuries. Do not include the number of deaths in this count.", - "long_name": "How many humans were injured?", - "mongo_type": "int", - "permitted_values": [], - "placeholder": "", - "public": true, - "required": false, - "short_description": "Indicate the number of injuries reported.", - "short_name": "Injuries", - "weight": 5 - }, - { - "__typename": "TaxaField_list", - "default": null, - "display_type": "bool", - "field_number": "8.3", - "instant_facet": false, - "long_description": "Indicates if the amount was estimated.", - "long_name": "Are any quantities estimated?", - "mongo_type": "bool", - "permitted_values": [], - "placeholder": "", - "public": true, - "required": false, - "short_description": "Indicates if the amount was estimated.", - "short_name": "Estimated Harm Quantities", - "weight": 5 - }, - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "long_string", - "field_number": "8.4", - "instant_facet": false, - "long_description": "Input any notes that may help explain your answers.", - "long_name": "Notes ( Tangible Harm Quantities Information)", - "mongo_type": "string", - "permitted_values": [], - "placeholder": "Notes", - "public": true, - "required": false, - "short_description": "Input any notes that may help explain your answers.", - "short_name": "Notes ( Tangible Harm Quantities Information)", - "weight": 5 - }, - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "long_string", - "field_number": "9.1", - "instant_facet": false, - "long_description": "Describe the AI system in as much detail as the reports will allow.\n\nA high level description of the AI system is sufficient, but if more technical details about the AI system are available, include them in the description as well.", - "long_name": "Description of the AI system involved", - "mongo_type": "string", - "permitted_values": [], - "placeholder": "Description of the AI system involved", - "public": true, - "required": false, - "short_description": "A description of the AI system (when possible)", - "short_name": "AI System Description", - "weight": 5 - }, - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "list", - "field_number": "9.2", - "instant_facet": false, - "long_description": "This is a freeform field that can have any value. There could be multiple entries for this field.\n\nCommon ones include\n\n- still images\n- video\n- text\n- speech\n- Personally Identifiable Information\n- structured data\n- other\n- unclear\n\nStill images are static images. Video images consist of moving images. Text and speech data are considered an important category of unstructured data. They consist of written and spoken words that are not in a tabular format. Personally identifiable information is data that can uniquely identify an individual and may contain sensitive information. Structured data is often in a tabular, machine readable format and can typically be used by an AI system without much preprocessing.\n\nAvoid using ‘unstructured data’ data in this field. Instead specify the type of unstructured data; text, images, audio files, etc. It is ok to use ‘structured data’ in this field.\n\nRecord what the media report explicitly states. If the report does not explicitly state an input modality but it is likely that a particular kind of input contributed to the harm or near harm, record that input. If you are still unsure, do not record anything.", - "long_name": "Description of data inputs to the AI system", - "mongo_type": "array", - "permitted_values": [], - "placeholder": "", - "public": true, - "required": false, - "short_description": "A list of the types of data inputs for the AI system.", - "short_name": "Data Inputs", - "weight": 5 - }, - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "multi", - "field_number": "9.3", - "instant_facet": true, - "long_description": "Indicate the sector in which the AI system is deployed\n\nThere could be multiple entries for this field.", - "long_name": "Indicates the sector in which the AI system is deployed", - "mongo_type": "array", - "permitted_values": [ - "agriculture, forestry and fishing", - "mining and quarrying", - "manufacturing", - "electricity, gas, steam and air conditioning supply", - "water supply", - "construction", - "wholesale and retail trade", - "transportation and storage", - "accommodation and food service activities", - "information and communication", - "financial and insurance activities", - "real estate activities", - "professional, scientific and technical activities", - "administrative and support service activities", - "public administration", - "defense", - "law enforcement", - "Education", - "human health and social work activities", - "Arts, entertainment and recreation", - "other service activities", - "activities of households as employers", - "activities of extraterritorial organizations and bodies", - "other", - "unclear" - ], - "placeholder": "", - "public": true, - "required": false, - "short_description": "Indicate the sector in which the AI system is deployed", - "short_name": "Sector of Deployment", - "weight": 50 - }, - { - "__typename": "TaxaField_list", - "default": "maybe", - "display_type": "enum", - "field_number": "9.4", - "instant_facet": true, - "long_description": "Indicate whether the AI system is deployed in the public sector. The public sector is the part of the economy that is controlled and operated by the government.", - "long_name": "Indicates whether the AI system is deployed in the public sector", - "mongo_type": "string", - "permitted_values": [ - "yes", - "no", - "maybe" - ], - "placeholder": "", - "public": true, - "required": false, - "short_description": "Indicate whether the AI system is deployed in the public sector", - "short_name": "Public Sector Deployment", - "weight": 50 - }, - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "enum", - "field_number": "9.5", - "instant_facet": true, - "long_description": "Autonomy1: The system operates independently without simultaneous human oversight, interaction, or intervention.\n\nAutonomy2: The system operates independently but with human oversight, where a human can observe and override the system’s decisions in real time.\n\nAutonomy3: The system does not independently make decisions but instead provides information to a human who actively chooses to proceed with the AI’s information.", - "long_name": "Autonomy Level", - "mongo_type": "string", - "permitted_values": [ - "Autonomy1", - "Autonomy2", - "Autonomy3", - "unclear" - ], - "placeholder": "", - "public": true, - "required": false, - "short_description": "Autonomy1: The system operates independently without simultaneous human oversight, interaction, or intervention.\n\nAutonomy2: The system operates independently but with human oversight, where a human can observe and override the system’s decisions in real time.\n\nAutonomy3: The system does not independently make decisions but instead provides information to a human who actively chooses to proceed with the AI’s information.", - "short_name": "Autonomy Level", - "weight": 50 - }, - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "long_string", - "field_number": "9.8", - "instant_facet": false, - "long_description": "Input any notes that may help explain your answers.", - "long_name": "Notes (Information about AI System)", - "mongo_type": "string", - "permitted_values": [], - "placeholder": "Notes", - "public": true, - "required": false, - "short_description": "Input any notes that may help explain your answers.", - "short_name": "Notes (Information about AI System)", - "weight": 5 - }, - { - "__typename": "TaxaField_list", - "default": "unclear", - "display_type": "enum", - "field_number": "10.1", - "instant_facet": true, - "long_description": "Indicates if the system was designed to do harm. If it was designed to perform harm, the field will indicate if the AI system did or did not create unintended harm–i.e. was the reported harm the harm that AI was expected to perform or a different unexpected harm? ", - "long_name": "Was the AI intentionally developed or deployed to perform the harm?", - "mongo_type": "string", - "permitted_values": [ - "Yes. Intentionally designed to perform harm and did create intended harm", - "Yes. Intentionally designed to perform harm but created an unintended harm (a different harm may have occurred)", - "No. Not intentionally designed to perform harm", - "unclear" - ], - "placeholder": "", - "public": true, - "required": false, - "short_description": "Was the AI intentionally developed or deployed to perform the harm?\n\nIf yes, did the AI’s behavior result in unintended or intended harm? ", - "short_name": "Intentional Harm", - "weight": 50 - }, - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "string", - "field_number": "10.2", - "instant_facet": false, - "long_description": "Describe the type of physical system that the AI was integrated into. ", - "long_name": "Into what type of physical system was the AI integrated, if any?", - "mongo_type": "string", - "permitted_values": [], - "placeholder": "Physical System Type (e.g. trash sorting robot)", - "public": true, - "required": false, - "short_description": "Describe the type of physical system that the AI was integrated into.", - "short_name": "Physical System Type", - "weight": 5 - }, - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "list", - "field_number": "10.3", - "instant_facet": false, - "long_description": "Describe the AI’s application.\n\nIt is likely that the annotator will not have enough information to complete this field. If this occurs, enter unclear.\n\nThis is a freeform field. Some possible entries are\n\n- unclear\n- human language technologies\n- computer vision\n- robotics\n- automation and/or optimization\n- other\n\nThe application area of an AI is the high level task that the AI is intended to perform. It does not describe the technical methods by which the AI performs the task. Considering what an AI’s technical methods enable it to do is another way of arriving at what an AI’s application is. \n\nIt is possible for multiple application areas to be involved. When possible pick the principle or domain area, but it is ok to select multiple areas.", - "long_name": "AI task or core application area", - "mongo_type": "array", - "permitted_values": [], - "placeholder": "", - "public": true, - "required": false, - "short_description": "Describe the AI’s application.", - "short_name": "AI Task", - "weight": 5 - }, - { - "__typename": "TaxaField_list", - "default": "unclear", - "display_type": "list", - "field_number": "10.4", - "instant_facet": false, - "long_description": "Describe the tools and methods that enable the AI’s application.\n\nIt is likely that the annotator will not have enough information to complete this field. If this occurs, enter unclear\n\nThis is a freeform field. Some possible entries are\n\n- unclear\n- reinforcement learning\n- neural networks\n- decision trees\n- bias mitigation\n- optimization\n- classifier\n- NLP/text analytics\n- continuous learning\n- unsupervised learning\n- supervised learning\n- clustering\n- prediction\n- rules\n- random forest\n\nAI tools and methods are the technical building blocks that enable the AI’s application.", - "long_name": "AI tools and methods", - "mongo_type": "array", - "permitted_values": [], - "placeholder": "", - "public": true, - "required": false, - "short_description": "Describe the tools and methods that enable the AI’s application.", - "short_name": "AI tools and methods", - "weight": 50 - }, - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "long_string", - "field_number": "10.5", - "instant_facet": false, - "long_description": "Input any notes that may help explain your answers.", - "long_name": "Notes (AI Functionality and Techniques)", - "mongo_type": "string", - "permitted_values": [], - "placeholder": "Notes", - "public": true, - "required": false, - "short_description": "Input any notes that may help explain your answers.", - "short_name": "Notes (AI Functionality and Techniques)", - "weight": 5 - } - ], - "namespace": "CSETv1_Annotator-2", - "weight": 70 - }, - { - "__typename": "Taxa", - "description": "# What is the CSET Taxonomy?\n\nThe Center for Security and Emerging Technology (CSET) taxonomy is\na general taxonomy of AI incidents. There are a large number of \nclassified attributes, including ones pertaining to safety, fairness, \nindustry, geography, timing, and cost. All classifications within \nthe CSET taxonomy are first applied by one CSET annotator and \nreviewed by another CSET annotator before the classifications are \nfinalized. The combination of a rigorously defined coding set and \nthe completeness with which it has been applied make the CSET \ntaxonomy the AIID's gold standard for taxonomies. Nevertheless, \nthe CSET taxonomy is an ongoing effort and you are invited to \nreport any errors you may discover in its application.\n\n## How do I explore the taxonomy?\n\nAll taxonomies can be used to filter incident reports within the \n[Discover Application](/apps/discover). The taxonomy filters work\nsimilarly to how you filter products on an E-commerce website. \nUse the search field at the bottom of the “Classifications” tab to \nfind the taxonomy field you would like to filter with, then click \nthe desired value to apply the filter.\n\n# About CSET\n\nA policy research organization within Georgetown University’s \nWalsh School of Foreign Service, CSET produces data-driven \nresearch at the intersection of security and technology, \nproviding nonpartisan analysis to the policy community. CSET is \ncurrently focusing on the effects of progress in artificial \nintelligence (AI), advanced computing and biotechnology. CSET \nseeks to prepare a new generation of decision-makers to address \nthe challenges and opportunities of emerging technologies. [(Read \nmore)](https://cset.georgetown.edu/about-us/).", - "field_list": [ - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "int", - "field_number": "1.1", - "instant_facet": false, - "long_description": "The number of the incident in the AI Incident Database.", - "long_name": "The number of the incident in the AI Incident Database.", - "mongo_type": "int", - "permitted_values": null, - "placeholder": null, - "public": true, - "required": false, - "short_description": "The number of the incident in the AI Incident Database.", - "short_name": "Incident Number", - "weight": 5 - }, - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "string", - "field_number": "1.2", - "instant_facet": false, - "long_description": "An ID designating the individual who classified this incident according to the CSET taxonomy.", - "long_name": "Person responsible for the annotations", - "mongo_type": "string", - "permitted_values": [], - "placeholder": "Select name here", - "public": false, - "required": false, - "short_description": "This is the researcher that is responsible for applying the classifications of the CSET taxonomy.", - "short_name": "Annotator", - "weight": 5 - }, - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "enum", - "field_number": "1.3", - "instant_facet": false, - "long_description": "What is the quality assurance status of the CSET classifications for this incident?", - "long_name": "Where in the annotation process is this incident?", - "mongo_type": "string", - "permitted_values": [ - "1. Annotation in progress", - "2. Initial annotation complete", - "3. In peer review", - "4. Peer review complete", - "5. In quality control", - "6. Complete and final" - ], - "placeholder": "Select process status here", - "public": false, - "required": false, - "short_description": "What is the quality assurance status of the CSET classifications for this incident?", - "short_name": "Annotation Status", - "weight": 10 - }, - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "string", - "field_number": "1.4", - "instant_facet": false, - "long_description": "The CSET taxonomy assigns individual researchers to each incident as the primary parties responsible for classifying the incident according to the taxonomy. This is the person responsible for assuring the integrity of annotator's classifications.", - "long_name": "Person responsible for reviewing annotations", - "mongo_type": "string", - "permitted_values": [], - "placeholder": "Select name here", - "public": false, - "required": false, - "short_description": "This is the researcher that is responsible for ensuring the quality of the classifications applied to this incident.", - "short_name": "Peer Reviewer", - "weight": 20 - }, - { - "__typename": "TaxaField_list", - "default": "false", - "display_type": "bool", - "field_number": "1.5", - "instant_facet": false, - "long_description": "The peer review process sometimes uncovers issues with the classifications that have been applied by the annotator. This field serves as a flag when there is a need for additional thought and input on the classifications applied", - "long_name": "Was this incident randomly selected for additional quality control?", - "mongo_type": "bool", - "permitted_values": [], - "placeholder": "", - "public": false, - "required": false, - "short_description": "Has someone flagged a potential issue with this incident's classifications? Annotators should leave this field blank.", - "short_name": "Quality Control", - "weight": 15 - }, - { - "__typename": "TaxaField_list", - "default": "maybe", - "display_type": "enum", - "field_number": "2.1", - "instant_facet": true, - "long_description": "“Yes” if the AI system(s) is embedded in hardware that can interact with, affect, and change the physical objects (cars, robots, medical facilities, etc.). Mark “No” if the system cannot. This includes systems that inform, detect, predict, or recommend.", - "long_name": "Did the incident occur in a domain with physical objects ?", - "mongo_type": "string", - "permitted_values": [ - "yes", - "no", - "maybe" - ], - "placeholder": "", - "public": null, - "required": false, - "short_description": "Did the incident occur in a domain with physical objects ?", - "short_name": "Physical Objects", - "weight": 50 - }, - { - "__typename": "TaxaField_list", - "default": "maybe", - "display_type": "enum", - "field_number": "2.2", - "instant_facet": true, - "long_description": "“Yes” if the sector in which the AI was used is associated with entertainment. “No” if it was used in a different, clearly identifiable sector. “Maybe” if the sector of use could not be determined.", - "long_name": "Did the AI incident occur in the entertainment industry?", - "mongo_type": "string", - "permitted_values": [ - "yes", - "no", - "maybe" - ], - "placeholder": "", - "public": null, - "required": false, - "short_description": "Did the AI incident occur in the entertainment industry?", - "short_name": "Entertainment Industry", - "weight": 50 - }, - { - "__typename": "TaxaField_list", - "default": "maybe", - "display_type": "enum", - "field_number": "2.3", - "instant_facet": true, - "long_description": "“Yes” if the incident is about a report, test, or study of the data and does not discuss an instance of injury, damage, or loss. “Maybe” if it is unclear. Otherwise mark “No.”", - "long_name": "Was the incident about a report, test, or study of data instead of the AI itself?", - "mongo_type": "string", - "permitted_values": [ - "yes", - "no", - "maybe" - ], - "placeholder": "", - "public": null, - "required": false, - "short_description": "Was the incident about a report, test, or study of data instead of the AI itself?", - "short_name": "Report, Test, or Study of data", - "weight": 50 - }, - { - "__typename": "TaxaField_list", - "default": "maybe", - "display_type": "enum", - "field_number": "2.4", - "instant_facet": true, - "long_description": "“Yes” if the involved system was deployed or sold to users. “No” if it was not. “Maybe” if there is not enough information or if the use is unclear.", - "long_name": "Was the reported system (even if AI involvement is unknown) deployed or sold to users?", - "mongo_type": "string", - "permitted_values": [ - "yes", - "no", - "maybe" - ], - "placeholder": "", - "public": null, - "required": false, - "short_description": "Was the reported system (even if AI involvement is unknown) deployed or sold to users?", - "short_name": "Deployed", - "weight": 50 - }, - { - "__typename": "TaxaField_list", - "default": "maybe", - "display_type": "enum", - "field_number": "2.5", - "instant_facet": true, - "long_description": "“Yes” if it was a test/demonstration performed by developers, producers or journalists in controlled conditions. “No” if it was not a test/demonstration. “No” if the test/demonstration was done by a user. “No” if the test/demonstration was in operational or uncontrolled conditions. “Maybe” otherwise.", - "long_name": "Was this a test or demonstration of an AI system done by developers, producers or researchers (versus users) in controlled conditions?", - "mongo_type": "string", - "permitted_values": [ - "yes", - "no", - "maybe" - ], - "placeholder": "", - "public": null, - "required": false, - "short_description": "Was this a test or demonstration of an AI system done by developers, producers or researchers (versus users) in controlled conditions?", - "short_name": "Producer Test in Controlled Conditions", - "weight": 50 - }, - { - "__typename": "TaxaField_list", - "default": "maybe", - "display_type": "enum", - "field_number": "2.6", - "instant_facet": true, - "long_description": "“Yes” if it was a test/demonstration performed by developers, producers or journalists in controlled conditions. “No” if it was not a test/demonstration. “No” if the test/demonstration was done by a user. “No” if the test/demonstration was in controlled or non-operational conditions. “Maybe” otherwise.", - "long_name": "Was this a test or demonstration of an AI system done by developers, producers or researchers (versus users) in operational conditions?", - "mongo_type": "string", - "permitted_values": [ - "yes", - "no", - "maybe" - ], - "placeholder": "", - "public": null, - "required": false, - "short_description": "Was this a test or demonstration of an AI system done by developers, producers or researchers (versus users) in operational conditions?", - "short_name": "Producer Test in Operational Conditions", - "weight": 50 - }, - { - "__typename": "TaxaField_list", - "default": "maybe", - "display_type": "enum", - "field_number": "2.7", - "instant_facet": true, - "long_description": "“Yes” if it was a test/demonstration performed by users in controlled conditions. “No” if it was not a test/demonstration. “No” if the test/demonstration was done by developers, producers or researchers. “No” if the test/demonstration was in controlled or non-controlled conditions.“Maybe” otherwise.", - "long_name": "Was this a test or demonstration done by users in controlled conditions?", - "mongo_type": "string", - "permitted_values": [ - "yes", - "no", - "maybe" - ], - "placeholder": "", - "public": null, - "required": false, - "short_description": "Was this a test or demonstration done by users in controlled conditions?", - "short_name": "User Test in Controlled Conditions", - "weight": 50 - }, - { - "__typename": "TaxaField_list", - "default": "maybe", - "display_type": "enum", - "field_number": "2.8", - "instant_facet": true, - "long_description": "“Yes” if it was a test/demonstration performed by users in operational conditions. “No” if it was not a test/demonstration. “No” if the test/demonstration was done by developers, producers or researchers. “No” if the test/demonstration was in controlled or non-operational conditions.“Maybe” otherwise.", - "long_name": "Was this a test or demonstration done by users in operational conditions?", - "mongo_type": "string", - "permitted_values": [ - "yes", - "no", - "maybe" - ], - "placeholder": "", - "public": null, - "required": false, - "short_description": "Was this a test or demonstration done by users in operational conditions?", - "short_name": "User Test in Operational Conditions", - "weight": 50 - }, - { - "__typename": "TaxaField_list", - "default": "maybe", - "display_type": "enum", - "field_number": "2.9", - "instant_facet": false, - "long_description": "Using the answers to the 8 domain questions, assess if the incident occurred in a domain where harm could be expected to occur. If you are unclear, input “maybe.”", - "long_name": "Incident occurred in a domain where we could expect harm to occur?", - "mongo_type": "string", - "permitted_values": [ - "yes", - "no", - "maybe" - ], - "placeholder": "", - "public": null, - "required": false, - "short_description": "Incident occurred in a domain where we could likely expect harm to occur?", - "short_name": "Harm Domain", - "weight": 50 - }, - { - "__typename": "TaxaField_list", - "default": "unclear", - "display_type": "enum", - "field_number": "3.1", - "instant_facet": true, - "long_description": "An assessment of whether tangible harm, imminent tangible harm, or non-imminent tangible harm occurred. This assessment does not consider the context of the tangible harm, if an AI was involved, or if there is an identifiable, specific, and harmed entity. It is also not assessing if an intangible harm occurred. It is only asking if tangible harm occurred and what its imminency was.", - "long_name": "Did tangible harm (loss, damage or injury ) occur? ", - "mongo_type": "string", - "permitted_values": [ - "tangible harm definitively occurred", - "imminent risk of tangible harm (near miss) did occur", - "non-imminent risk of tangible harm (an issue) occurred", - "no tangible harm, near-miss, or issue", - "unclear" - ], - "placeholder": "", - "public": true, - "required": false, - "short_description": "Did tangible harm (loss, damage or injury ) occur? ", - "short_name": "Tangible Harm", - "weight": 50 - }, - { - "__typename": "TaxaField_list", - "default": "maybe", - "display_type": "enum", - "field_number": "3.2", - "instant_facet": true, - "long_description": "An assessment of whether or not an AI system was involved. It is sometimes difficult to judge between an AI and an automated system or expert rules system. In these cases select “maybe”", - "long_name": "Does the incident involve an AI system?", - "mongo_type": "string", - "permitted_values": [ - "yes", - "no", - "maybe" - ], - "placeholder": "", - "public": true, - "required": false, - "short_description": "Does the incident involve an AI system?", - "short_name": "AI System", - "weight": 50 - }, - { - "__typename": "TaxaField_list", - "default": "maybe", - "display_type": "enum", - "field_number": "3.3", - "instant_facet": true, - "long_description": "“Yes” if an AI was involved in harm, its behavior can be directly linked to the harm, and the harm may not have occurred if the AI acted differently. “Maybe” if the link is unclear. Otherwise, select “no.”", - "long_name": "Can an AI be directly and clearly linked to tangible harm?", - "mongo_type": "string", - "permitted_values": [ - "yes", - "no", - "maybe" - ], - "placeholder": "", - "public": true, - "required": false, - "short_description": "Can an AI be directly and clearly linked to tangible harm?", - "short_name": "Clear Link to AI", - "weight": 50 - }, - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "bool", - "field_number": "3.4", - "instant_facet": true, - "long_description": "“Yes” if it is theoretically possible to both specify and identify the entity. Having that information is not required. The information just needs to exist and be potentially discoverable. “No” if there are not any potentially identifiable specific entities or if the harmed entities are a class or subgroup that can only be characterized. ", - "long_name": "There is a potentially identifiable specific entity that experienced the harm", - "mongo_type": "bool", - "permitted_values": [], - "placeholder": "", - "public": true, - "required": false, - "short_description": "A potentially identifiable specific entity that experienced the harm can be characterized or identified.", - "short_name": "There is a potentially identifiable specific entity that experienced the harm", - "weight": 50 - }, - { - "__typename": "TaxaField_list", - "default": "unclear", - "display_type": "enum", - "field_number": "3.5", - "instant_facet": true, - "long_description": "An assessment of the AI tangible harm level, which takes into account the CSET definitions of AI tangible harm levels, along with the inputs for annotation fields about the AI, harm, chain of harm, and entity.", - "long_name": "Annotator's AI tangible harm level assessment", - "mongo_type": "string", - "permitted_values": [ - "AI tangible harm event", - "AI tangible harm near-miss", - "AI tangible harm issue", - "none", - "unclear" - ], - "placeholder": "", - "public": true, - "required": false, - "short_description": "An assessment of the AI tangible harm level, which takes into account the CSET definitions of AI tangible harm levels, along with the inputs for annotation fields about the AI, harm, chain of harm, and entity. ", - "short_name": "AI Harm Level", - "weight": 50 - }, - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "long_string", - "field_number": "3.6", - "instant_facet": false, - "long_description": "If for 3.5 you select unclear or leave it blank, please provide a brief description of why.\n\n You can also add notes if you want to provide justification for a level", - "long_name": "AI Tangible Harm Level Notes", - "mongo_type": "string", - "permitted_values": [], - "placeholder": "Notes about the AI tangible harm level assessment", - "public": true, - "required": false, - "short_description": "Notes about the AI tangible harm level assessment", - "short_name": "AI Tangible Harm Level Notes", - "weight": 5 - }, - { - "__typename": "TaxaField_list", - "default": "maybe", - "display_type": "enum", - "field_number": "4.1", - "instant_facet": true, - "long_description": "Did this impact people's access to critical or public services (health care, social services, voting, transportation, etc)?", - "long_name": "Did this impact people's access to critical or public services (health care, social services, voting, transportation, etc)?", - "mongo_type": "string", - "permitted_values": [ - "yes", - "no", - "maybe" - ], - "placeholder": "", - "public": true, - "required": false, - "short_description": "Indicates if people’s access to critical public services was impacted.", - "short_name": "Impact on Critical Services", - "weight": 50 - }, - { - "__typename": "TaxaField_list", - "default": "maybe", - "display_type": "enum", - "field_number": "4.2", - "instant_facet": true, - "long_description": "Indicate if a violation of human rights, civil rights, civil liberties, or democratic norms occurred.", - "long_name": "Was this a violation of human rights, civil liberties, civil rights, or democratic norms?", - "mongo_type": "string", - "permitted_values": [ - "yes", - "no", - "maybe" - ], - "placeholder": "", - "public": true, - "required": false, - "short_description": "Indicate if a violation of human rights, civil rights, civil liberties, or democratic norms occurred.", - "short_name": "Rights Violation", - "weight": 50 - }, - { - "__typename": "TaxaField_list", - "default": "maybe", - "display_type": "enum", - "field_number": "4.3", - "instant_facet": true, - "long_description": "Indicate if a minor was disproportionately targeted or affected", - "long_name": "Was a minor involved in the incident (disproportionally treated or specifically targeted/affected)", - "mongo_type": "string", - "permitted_values": [ - "yes", - "no", - "maybe" - ], - "placeholder": "", - "public": true, - "required": false, - "short_description": "Was a minor involved in the incident (disproportionally treated or specifically targeted/affected)", - "short_name": "Involving Minor", - "weight": 50 - }, - { - "__typename": "TaxaField_list", - "default": "maybe", - "display_type": "enum", - "field_number": "4.4", - "instant_facet": true, - "long_description": "Detrimental content can include deepfakes, identity misrepresentation, insults, threats of violence, eating disorder or self harm promotion, extremist content, misinformation, sexual abuse material, and scam emails. Detrimental content in itself is often not harmful, however, it can lead to or instigate injury, damage, or loss.", - "long_name": "Was detrimental content (misinformation, hate speech) involved?", - "mongo_type": "string", - "permitted_values": [ - "yes", - "no", - "maybe" - ], - "placeholder": "", - "public": true, - "required": false, - "short_description": "Was detrimental content (misinformation, hate speech) involved?", - "short_name": "Detrimental Content", - "weight": 50 - }, - { - "__typename": "TaxaField_list", - "default": "maybe", - "display_type": "enum", - "field_number": "4.5", - "instant_facet": true, - "long_description": "Protected characteristics include religion, commercial facilities, geography, age, sex, sexual orientation or gender identity, familial status (e.g., having or not having children) or pregnancy, disability, veteran status, genetic information, financial means, race or creed, Ideology, nation of origin, citizenship, and immigrant status.\n\nAt the federal level in the US, age is a protected characteristic for people over the age of 40. Minors are not considered a protected class. For this reason the CSET annotation taxonomy has a separate field to note if a minor was involved.\n\nOnly mark yes if there is clear evidence discrimination occurred. If there are conflicting accounts, mark unsure. Do not mark that discrimination occurred based on expectation alone.", - "long_name": "Was a group of people or an individual treated differently based upon a protected characteristic?", - "mongo_type": "string", - "permitted_values": [ - "yes", - "no", - "maybe" - ], - "placeholder": "", - "public": true, - "required": false, - "short_description": "Was a group of people treated differently based upon a protected characteristic (e.g. race, ethnicity, creed, immigrant status, color, religion, sex, national origin, age, disability, genetic information)?", - "short_name": "Protected Characteristic", - "weight": 50 - }, - { - "__typename": "TaxaField_list", - "default": "unclear", - "display_type": "multi", - "field_number": "4.6", - "instant_facet": true, - "long_description": "Multiple can occur.\n\nGenetic information refers to information about a person’s genetic tests or the genetic tests of their relatives. Genetic information can predict the manifestation of a disease or disorder.", - "long_name": "If harms were potentially unevenly distributed among people, on what basis?", - "mongo_type": "string", - "permitted_values": [ - "none", - "age", - "disability", - "familial status (e.g., having or not having children) or pregnancy", - "financial means", - "genetic information", - "geography", - "ideology", - "nation of origin, citizenship, immigrant status", - "race", - "religion", - "sex", - "sexual orientation or gender identity", - "veteran status", - "unclear", - "other" - ], - "placeholder": "", - "public": true, - "required": false, - "short_description": "Indicates how the harms were potentially distributed.", - "short_name": "Harm Distribution Basis", - "weight": 50 - }, - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "long_string", - "field_number": "4.7", - "instant_facet": false, - "long_description": "Input any notes that may help explain your answers.", - "long_name": "Input any notes that may help explain your answers.", - "mongo_type": "string", - "permitted_values": [], - "placeholder": "Notes", - "public": true, - "required": false, - "short_description": "Input any notes that may help explain your answers.", - "short_name": "Notes (special interest intangible harm)", - "weight": 5 - }, - { - "__typename": "TaxaField_list", - "default": "maybe", - "display_type": "enum", - "field_number": "5.1", - "instant_facet": false, - "long_description": "An assessment of whether a special interest intangible harm occurred. This assessment does not consider the context of the intangible harm, if an AI was involved, or if there is characterizable class or subgroup of harmed entities. It is also not assessing if an intangible harm occurred. It is only asking if a special interest intangible harm occurred.", - "long_name": "Was there a special interest intangible harm or risk of harm?", - "mongo_type": "string", - "permitted_values": [ - "yes", - "no", - "maybe" - ], - "placeholder": "", - "public": true, - "required": false, - "short_description": "An assessment of whether a special interest intangible harm occurred. This assessment does not consider the context of the intangible harm, if an AI was involved, or if there is characterizable class or subgroup of harmed entities. It is also not assessing if an intangible harm occurred. It is only asking if a special interest intangible harm occurred.", - "short_name": "Special Interest Intangible Harm", - "weight": 5 - }, - { - "__typename": "TaxaField_list", - "default": "maybe", - "display_type": "enum", - "field_number": "5.2", - "instant_facet": true, - "long_description": "An assessment of whether or not an AI system was involved. It is sometimes difficult to judge between an AI and an automated system or expert rules system. In these cases select “maybe”", - "long_name": "Does the incident involve an AI system?", - "mongo_type": "string", - "permitted_values": [ - "yes", - "no", - "maybe" - ], - "placeholder": "", - "public": true, - "required": false, - "short_description": "Does the incident involve an AI system?", - "short_name": "AI System", - "weight": 50 - }, - { - "__typename": "TaxaField_list", - "default": "maybe", - "display_type": "enum", - "field_number": "5.3", - "instant_facet": true, - "long_description": "“Yes” if an AI was involved in harm, its behavior can be directly linked to the harm, and the harm may not have occurred if the AI acted differently. “Maybe” if the link is unclear. Otherwise, select “no.”", - "long_name": "Can an AI be directly and clearly linked to the special interest intangible harm?", - "mongo_type": "string", - "permitted_values": [ - "yes", - "no", - "maybe" - ], - "placeholder": "", - "public": true, - "required": false, - "short_description": "Can an AI be directly and clearly linked to the special interest intangible harm?", - "short_name": "AI Linked to Special Interest Intangible Harm", - "weight": 50 - }, - { - "__typename": "TaxaField_list", - "default": null, - "display_type": "bool", - "field_number": "5.4", - "instant_facet": true, - "long_description": "A characterizable class or subgroup are descriptions of different populations of people. Often they are characteristics by which people qualify for special protection by a law, policy, or similar authority.\n\n Sometimes, groups may be characterized by their exposure to the incident via geographical proximity (e.g., ‘visitors to the park’) or participation in an activity (e.g.,‘Twitter users’).", - "long_name": "There is a characterizable class or subgroup of entities that experienced the harm", - "mongo_type": "bool", - "permitted_values": [], - "placeholder": "", - "public": true, - "required": false, - "short_description": "“Yes” if the harmed entity or entities can be characterized. “No” if there are not any characterizable entities.", - "short_name": "Harmed Class of Entities", - "weight": 50 - }, - { - "__typename": "TaxaField_list", - "default": "maybe", - "display_type": "enum", - "field_number": "5.5", - "instant_facet": true, - "long_description": "AI tangible harm is determined in a different field. The determination of a special interest intangible harm is not dependant upon the AI tangible harm level.", - "long_name": "The annotator’s assessment of if an AI special interest intangible harm occurred.", - "mongo_type": "string", - "permitted_values": [ - "yes", - "no", - "maybe" - ], - "placeholder": "", - "public": true, - "required": false, - "short_description": "The annotator’s assessment of if an AI special interest intangible harm occurred.", - "short_name": "Annotator’s AI special interest intangible harm assessment", - "weight": 50 - }, - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "long_string", - "field_number": "5.6", - "instant_facet": false, - "long_description": "If for 5.5 you select unclear or leave it blank, please provide a brief description of why.\n\nYou can also add notes if you want to provide justification for a level.", - "long_name": "If for 5.5 you select unclear or leave it blank, please provide a brief description of why.\n\nYou can also add notes if you want to provide justification for a level.", - "mongo_type": "string", - "permitted_values": [], - "placeholder": "Notes", - "public": true, - "required": false, - "short_description": "If for 5.5 you select unclear or leave it blank, please provide a brief description of why.\n\nYou can also add notes if you want to provide justification for a level.", - "short_name": "Notes (AI special interest intangible harm)", - "weight": 5 - }, - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "string", - "field_number": "6.1", - "instant_facet": false, - "long_description": "The year in which the incident occurred. If there are multiple harms or occurrences of the incident, list the earliest. If a precise date is unavailable, but the available sources provide a basis for estimating the year, estimate. Otherwise, leave blank.", - "long_name": "The year in which the incident first occurred.", - "mongo_type": "string", - "permitted_values": [], - "placeholder": "YYYY", - "public": true, - "required": false, - "short_description": "The year in which the incident occurred. If there are multiple harms or occurrences of the incident, list the earliest. If a precise date is unavailable, but the available sources provide a basis for estimating the year, estimate. Otherwise, leave blank.\n\nEnter in the format of YYYY", - "short_name": "Date of Incident Year", - "weight": 5 - }, - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "string", - "field_number": "6.2", - "instant_facet": false, - "long_description": "The month in which the incident occurred. If there are multiple harms or occurrences of the incident, list the earliest. If a precise date is unavailable, but the available sources provide a basis for estimating the month, estimate. Otherwise, leave blank.", - "long_name": "The month in which the incident first occurred.", - "mongo_type": "string", - "permitted_values": [], - "placeholder": "MM", - "public": true, - "required": false, - "short_description": "The month in which the incident occurred. If there are multiple harms or occurrences of the incident, list the earliest. If a precise date is unavailable, but the available sources provide a basis for estimating the month, estimate. Otherwise, leave blank.\n\nEnter in the format of MM", - "short_name": "Date of Incident Month", - "weight": 5 - }, - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "string", - "field_number": "6.3", - "instant_facet": true, - "long_description": "The day on which the incident occurred. If a precise date is unavailable, leave blank.\n\nEnter in the format of DD", - "long_name": "The day on which the first incident occurred.", - "mongo_type": "string", - "permitted_values": [], - "placeholder": "DD", - "public": true, - "required": false, - "short_description": "The day on which the incident occurred. If a precise date is unavailable, leave blank.\n\nEnter in the format of DD", - "short_name": "Date of Incident Day", - "weight": 5 - }, - { - "__typename": "TaxaField_list", - "default": null, - "display_type": "bool", - "field_number": "6.4", - "instant_facet": false, - "long_description": "“Yes” if the data was estimated. “No” otherwise.", - "long_name": "Is the date estimated?", - "mongo_type": "bool", - "permitted_values": [], - "placeholder": "", - "public": true, - "required": false, - "short_description": "“Yes” if the data was estimated. “No” otherwise.", - "short_name": "Estimated Date", - "weight": 5 - }, - { - "__typename": "TaxaField_list", - "default": "maybe", - "display_type": "enum", - "field_number": "6.5", - "instant_facet": true, - "long_description": "This happens very rarely but is possible. Examples include two chatbots having a conversation with each other, or two autonomous vehicles in a crash.", - "long_name": "Was the AI interacting with another AI?", - "mongo_type": "string", - "permitted_values": [ - "yes", - "no", - "maybe" - ], - "placeholder": "", - "public": true, - "required": false, - "short_description": "“Yes” if two or more independently operating AI systems were involved. “No” otherwise.", - "short_name": "Multiple AI Interaction", - "weight": 5 - }, - { - "__typename": "TaxaField_list", - "default": "maybe", - "display_type": "enum", - "field_number": "6.6", - "instant_facet": true, - "long_description": "This question is slightly different from the one in field 2.1.1. That question asks about there being interaction with physical objects–an ability to manipulate or change. A system can be embedded in a physical object and able to interact with the physical environment, e.g. a vacuum robot. A system can be embedded in a physical object and not interact with a physical environment, e.g. a camera system that only records images when the AI detects that dogs are present. AI systems that are accessed through API, web-browser, etc by using a mobile device or computer are not considered to be embedded in hardware systems. They are accessed through hardware.", - "long_name": "Is the AI embedded in a physical system or have a physical presence?", - "mongo_type": "string", - "permitted_values": [ - "yes", - "no", - "maybe" - ], - "placeholder": "", - "public": true, - "required": false, - "short_description": "“Yes” if the AI is embedded in a physical system. “No” if it is not. “Maybe” if it is unclear.", - "short_name": "Embedded", - "weight": 5 - }, - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "string", - "field_number": "6.7", - "instant_facet": false, - "long_description": "If the incident occurred at a specific known location, note the city. If there are multiple relevant locations, enter multiple city/state/country values.", - "long_name": "If the incident occurred at a specific known location, note the city.", - "mongo_type": "string", - "permitted_values": [], - "placeholder": "City", - "public": true, - "required": false, - "short_description": "If the incident occurred at a specific known location, note the city.", - "short_name": "Location City", - "weight": 5 - }, - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "string", - "field_number": "6.8", - "instant_facet": false, - "long_description": "If the incident occurred at a specific known location, note the state/province. If there are multiple relevant locations, enter multiple city/state/country values.", - "long_name": "If the incident occurred at a specific known location, note the state/province.", - "mongo_type": "string", - "permitted_values": [], - "placeholder": "State or Province", - "public": true, - "required": false, - "short_description": "If the incident occurred at a specific known location, note the state/province.", - "short_name": "Location State/Province (two letters)", - "weight": 5 - }, - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "string", - "field_number": "6.9", - "instant_facet": false, - "long_description": "Follow ISO 3166 for the 2-letter country codes.\n\nIf there are multiple relevant locations, enter multiple city/state/country values.", - "long_name": "If the incident occurred at a specific known location, note the country. ", - "mongo_type": "string", - "permitted_values": [], - "placeholder": "Country", - "public": true, - "required": false, - "short_description": "If the incident occurred at a specific known location, note the country. Follow ISO 3166 for the 2-letter country codes.", - "short_name": "Location Country (two letters)", - "weight": 5 - }, - { - "__typename": "TaxaField_list", - "default": "unclear", - "display_type": "enum", - "field_number": "6.10", - "instant_facet": true, - "long_description": "Use this reference to map countries to regions: https://www.dhs.gov/geographic-regions", - "long_name": "Location Region", - "mongo_type": "string", - "permitted_values": [ - "Global", - "Africa", - "Asia", - "Caribbean", - "Central America", - "Europe", - "North America", - "Oceania", - "South America", - "unclear" - ], - "placeholder": "", - "public": true, - "required": false, - "short_description": "Select the region of the world where the incident occurred. If it occurred in multiple, leave blank.", - "short_name": "Location Region", - "weight": 5 - }, - { - "__typename": "TaxaField_list", - "default": "unclear", - "display_type": "multi", - "field_number": "6.11", - "instant_facet": true, - "long_description": "Which critical infrastructure sectors were affected, if any?", - "long_name": "Which critical infrastructure sectors were affected, if any?", - "mongo_type": "string", - "permitted_values": [ - "chemical", - "commercial facilities", - "communications", - "critical manufacturing", - "dams", - "defense-industrial base", - "emergency services", - "energy", - "financial services", - "food and agriculture", - "government facilities", - "healthcare and public health", - "information technology", - "nuclear ", - "transportation", - "water and wastewater", - "Other", - "unclear" - ], - "placeholder": "", - "public": true, - "required": false, - "short_description": "Which critical infrastructure sectors were affected, if any?", - "short_name": "Infrastructure Sectors", - "weight": 5 - }, - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "list", - "field_number": "6.12", - "instant_facet": false, - "long_description": "A record of any abnormal or atypical operational conditions that occurred. This field is most often blank.", - "long_name": "A record of any abnormal or atypical operational conditions that occurred.", - "mongo_type": "array", - "permitted_values": [], - "placeholder": "e.g. raining; night; low visibility", - "public": true, - "required": false, - "short_description": "A record of any abnormal or atypical operational conditions that occurred.", - "short_name": "Operating Conditions", - "weight": 5 - }, - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "long_string", - "field_number": "6.13", - "instant_facet": false, - "long_description": "Input any notes that may help explain your answers.", - "long_name": "Notes (Environmental and Temporal Characteristics)", - "mongo_type": "string", - "permitted_values": [], - "placeholder": "Notes", - "public": true, - "required": false, - "short_description": "Input any notes that may help explain your answers.", - "short_name": "Notes (Environmental and Temporal Characteristics)", - "weight": 5 - }, - { - "__typename": "TaxaField_list", - "default": "[]", - "display_type": "object-list", - "field_number": "7", - "instant_facet": false, - "long_description": "Characterizing Entities and the Harm", - "long_name": "Characterizing Entities and the Harm", - "mongo_type": "array", - "permitted_values": [], - "placeholder": "", - "public": true, - "required": false, - "short_description": "Characterizing Entities and the Harm", - "short_name": "Entities", - "weight": 5 - }, - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "int", - "field_number": "8.1", - "instant_facet": true, - "long_description": "This field cannot be greater than zero if the harm is anything besides ‘Physical health/safety.’ ", - "long_name": "How many human lives were lost?", - "mongo_type": "int", - "permitted_values": [], - "placeholder": "", - "public": true, - "required": false, - "short_description": "Indicates the number of deaths reported", - "short_name": "Lives Lost", - "weight": 5 - }, - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "int", - "field_number": "8.2", - "instant_facet": false, - "long_description": "This field cannot be greater than zero if the harm is anything besides 'Physical health/safety'.\n\nAll reported injuries should count, regardless of their severity level. If a person lost their limb and another person scraped their elbow, both cases would be considered injuries. Do not include the number of deaths in this count.", - "long_name": "How many humans were injured?", - "mongo_type": "int", - "permitted_values": [], - "placeholder": "", - "public": true, - "required": false, - "short_description": "Indicate the number of injuries reported.", - "short_name": "Injuries", - "weight": 5 - }, - { - "__typename": "TaxaField_list", - "default": null, - "display_type": "bool", - "field_number": "8.3", - "instant_facet": false, - "long_description": "Indicates if the amount was estimated.", - "long_name": "Are any quantities estimated?", - "mongo_type": "bool", - "permitted_values": [], - "placeholder": "", - "public": true, - "required": false, - "short_description": "Indicates if the amount was estimated.", - "short_name": "Estimated Harm Quantities", - "weight": 5 - }, - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "long_string", - "field_number": "8.4", - "instant_facet": false, - "long_description": "Input any notes that may help explain your answers.", - "long_name": "Notes ( Tangible Harm Quantities Information)", - "mongo_type": "string", - "permitted_values": [], - "placeholder": "Notes", - "public": true, - "required": false, - "short_description": "Input any notes that may help explain your answers.", - "short_name": "Notes ( Tangible Harm Quantities Information)", - "weight": 5 - }, - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "long_string", - "field_number": "9.1", - "instant_facet": false, - "long_description": "Describe the AI system in as much detail as the reports will allow.\n\nA high level description of the AI system is sufficient, but if more technical details about the AI system are available, include them in the description as well.", - "long_name": "Description of the AI system involved", - "mongo_type": "string", - "permitted_values": [], - "placeholder": "Description of the AI system involved", - "public": true, - "required": false, - "short_description": "A description of the AI system (when possible)", - "short_name": "AI System Description", - "weight": 5 - }, - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "list", - "field_number": "9.2", - "instant_facet": false, - "long_description": "This is a freeform field that can have any value. There could be multiple entries for this field.\n\nCommon ones include\n\n- still images\n- video\n- text\n- speech\n- Personally Identifiable Information\n- structured data\n- other\n- unclear\n\nStill images are static images. Video images consist of moving images. Text and speech data are considered an important category of unstructured data. They consist of written and spoken words that are not in a tabular format. Personally identifiable information is data that can uniquely identify an individual and may contain sensitive information. Structured data is often in a tabular, machine readable format and can typically be used by an AI system without much preprocessing.\n\nAvoid using ‘unstructured data’ data in this field. Instead specify the type of unstructured data; text, images, audio files, etc. It is ok to use ‘structured data’ in this field.\n\nRecord what the media report explicitly states. If the report does not explicitly state an input modality but it is likely that a particular kind of input contributed to the harm or near harm, record that input. If you are still unsure, do not record anything.", - "long_name": "Description of data inputs to the AI system", - "mongo_type": "array", - "permitted_values": [], - "placeholder": "", - "public": true, - "required": false, - "short_description": "A list of the types of data inputs for the AI system.", - "short_name": "Data Inputs", - "weight": 5 - }, - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "multi", - "field_number": "9.3", - "instant_facet": true, - "long_description": "Indicate the sector in which the AI system is deployed\n\nThere could be multiple entries for this field.", - "long_name": "Indicates the sector in which the AI system is deployed", - "mongo_type": "array", - "permitted_values": [ - "agriculture, forestry and fishing", - "mining and quarrying", - "manufacturing", - "electricity, gas, steam and air conditioning supply", - "water supply", - "construction", - "wholesale and retail trade", - "transportation and storage", - "accommodation and food service activities", - "information and communication", - "financial and insurance activities", - "real estate activities", - "professional, scientific and technical activities", - "administrative and support service activities", - "public administration", - "defense", - "law enforcement", - "Education", - "human health and social work activities", - "Arts, entertainment and recreation", - "other service activities", - "activities of households as employers", - "activities of extraterritorial organizations and bodies", - "other", - "unclear" - ], - "placeholder": "", - "public": true, - "required": false, - "short_description": "Indicate the sector in which the AI system is deployed", - "short_name": "Sector of Deployment", - "weight": 50 - }, - { - "__typename": "TaxaField_list", - "default": "maybe", - "display_type": "enum", - "field_number": "9.4", - "instant_facet": true, - "long_description": "Indicate whether the AI system is deployed in the public sector. The public sector is the part of the economy that is controlled and operated by the government.", - "long_name": "Indicates whether the AI system is deployed in the public sector", - "mongo_type": "string", - "permitted_values": [ - "yes", - "no", - "maybe" - ], - "placeholder": "", - "public": true, - "required": false, - "short_description": "Indicate whether the AI system is deployed in the public sector", - "short_name": "Public Sector Deployment", - "weight": 50 - }, - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "enum", - "field_number": "9.5", - "instant_facet": true, - "long_description": "Autonomy1: The system operates independently without simultaneous human oversight, interaction, or intervention.\n\nAutonomy2: The system operates independently but with human oversight, where a human can observe and override the system’s decisions in real time.\n\nAutonomy3: The system does not independently make decisions but instead provides information to a human who actively chooses to proceed with the AI’s information.", - "long_name": "Autonomy Level", - "mongo_type": "string", - "permitted_values": [ - "Autonomy1", - "Autonomy2", - "Autonomy3", - "unclear" - ], - "placeholder": "", - "public": true, - "required": false, - "short_description": "Autonomy1: The system operates independently without simultaneous human oversight, interaction, or intervention.\n\nAutonomy2: The system operates independently but with human oversight, where a human can observe and override the system’s decisions in real time.\n\nAutonomy3: The system does not independently make decisions but instead provides information to a human who actively chooses to proceed with the AI’s information.", - "short_name": "Autonomy Level", - "weight": 50 - }, - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "long_string", - "field_number": "9.8", - "instant_facet": false, - "long_description": "Input any notes that may help explain your answers.", - "long_name": "Notes (Information about AI System)", - "mongo_type": "string", - "permitted_values": [], - "placeholder": "Notes", - "public": true, - "required": false, - "short_description": "Input any notes that may help explain your answers.", - "short_name": "Notes (Information about AI System)", - "weight": 5 - }, - { - "__typename": "TaxaField_list", - "default": "unclear", - "display_type": "enum", - "field_number": "10.1", - "instant_facet": true, - "long_description": "Indicates if the system was designed to do harm. If it was designed to perform harm, the field will indicate if the AI system did or did not create unintended harm–i.e. was the reported harm the harm that AI was expected to perform or a different unexpected harm? ", - "long_name": "Was the AI intentionally developed or deployed to perform the harm?", - "mongo_type": "string", - "permitted_values": [ - "Yes. Intentionally designed to perform harm and did create intended harm", - "Yes. Intentionally designed to perform harm but created an unintended harm (a different harm may have occurred)", - "No. Not intentionally designed to perform harm", - "unclear" - ], - "placeholder": "", - "public": true, - "required": false, - "short_description": "Was the AI intentionally developed or deployed to perform the harm?\n\nIf yes, did the AI’s behavior result in unintended or intended harm? ", - "short_name": "Intentional Harm", - "weight": 50 - }, - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "string", - "field_number": "10.2", - "instant_facet": false, - "long_description": "Describe the type of physical system that the AI was integrated into. ", - "long_name": "Into what type of physical system was the AI integrated, if any?", - "mongo_type": "string", - "permitted_values": [], - "placeholder": "Physical System Type (e.g. trash sorting robot)", - "public": true, - "required": false, - "short_description": "Describe the type of physical system that the AI was integrated into.", - "short_name": "Physical System Type", - "weight": 5 - }, - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "list", - "field_number": "10.3", - "instant_facet": false, - "long_description": "Describe the AI’s application.\n\nIt is likely that the annotator will not have enough information to complete this field. If this occurs, enter unclear.\n\nThis is a freeform field. Some possible entries are\n\n- unclear\n- human language technologies\n- computer vision\n- robotics\n- automation and/or optimization\n- other\n\nThe application area of an AI is the high level task that the AI is intended to perform. It does not describe the technical methods by which the AI performs the task. Considering what an AI’s technical methods enable it to do is another way of arriving at what an AI’s application is. \n\nIt is possible for multiple application areas to be involved. When possible pick the principle or domain area, but it is ok to select multiple areas.", - "long_name": "AI task or core application area", - "mongo_type": "array", - "permitted_values": [], - "placeholder": "", - "public": true, - "required": false, - "short_description": "Describe the AI’s application.", - "short_name": "AI Task", - "weight": 5 - }, - { - "__typename": "TaxaField_list", - "default": "unclear", - "display_type": "list", - "field_number": "10.4", - "instant_facet": false, - "long_description": "Describe the tools and methods that enable the AI’s application.\n\nIt is likely that the annotator will not have enough information to complete this field. If this occurs, enter unclear\n\nThis is a freeform field. Some possible entries are\n\n- unclear\n- reinforcement learning\n- neural networks\n- decision trees\n- bias mitigation\n- optimization\n- classifier\n- NLP/text analytics\n- continuous learning\n- unsupervised learning\n- supervised learning\n- clustering\n- prediction\n- rules\n- random forest\n\nAI tools and methods are the technical building blocks that enable the AI’s application.", - "long_name": "AI tools and methods", - "mongo_type": "array", - "permitted_values": [], - "placeholder": "", - "public": true, - "required": false, - "short_description": "Describe the tools and methods that enable the AI’s application.", - "short_name": "AI tools and methods", - "weight": 50 - }, - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "long_string", - "field_number": "10.5", - "instant_facet": false, - "long_description": "Input any notes that may help explain your answers.", - "long_name": "Notes (AI Functionality and Techniques)", - "mongo_type": "string", - "permitted_values": [], - "placeholder": "Notes", - "public": true, - "required": false, - "short_description": "Input any notes that may help explain your answers.", - "short_name": "Notes (AI Functionality and Techniques)", - "weight": 5 - } - ], - "namespace": "CSETv1_Annotator-3", - "weight": 70 - }, - { - "__typename": "Taxa", - "description": "## What is the GMF Taxonomy?\n\nThe Goals, Methods, and Failures (GMF) taxonomy is a failure \ncause analysis taxonomy interrelating the goals of the system \ndeployment, the system's methods, and their likely failings. \nDetails on the process are available in the recent work published \nfor [SafeAI paper](https://arxiv.org/abs/2211.07280).\n\n## How do I explore the taxonomy?\n\nAll taxonomies can be used to filter incident reports within the \nDiscover Application. The taxonomy filters work similarly to how \nyou filter products on an E-commerce website. Use the search \nfield at the bottom of the “Classifications” tab to find the \ntaxonomy field you would like to filter with, then click the \ndesired value to apply the filter.\n\n## About the Responsible AI Collaborative\n\nThe AI Incident Database is a collaborative project of many \npeople and organizations. Details on the people and organizations \ncontributing to this particular taxonomy will appear here, while \nyou can learn more about the Collab itself on the incident \ndatabase [home](https://incidentdatabase.ai/) and \n[about](https://incidentdatabase.ai/about/) pages.\n\nThe maintainers of this taxonomy include,\n\n* [Nikiforos Pittaras](https://www.linkedin.com/in/nikiforos-pittaras/)\n* [Sean McGregor](https://www.linkedin.com/in/seanbmcgregor/)", - "field_list": [ - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "list", - "field_number": "1.1.1", - "instant_facet": true, - "long_description": "An AI Goal which is almost certainly pursued by the AI system referenced in the incident.", - "long_name": "Known AI Goal", - "mongo_type": "array", - "permitted_values": [], - "placeholder": "", - "public": true, - "required": false, - "short_description": "An AI Goal which is almost certainly pursued by the AI system referenced in the incident.", - "short_name": "Known AI Goal", - "weight": 50 - }, - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "object-list", - "field_number": "1.1.2", - "instant_facet": false, - "long_description": "One or more snippets that justify the classification.", - "long_name": "Known AI Goal Snippets", - "mongo_type": "array", - "permitted_values": [], - "placeholder": "", - "public": true, - "required": false, - "short_description": "One or more snippets that justify the classification.", - "short_name": "Known AI Goal Snippets", - "weight": 40 - }, - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "long_string", - "field_number": "1.1.3", - "instant_facet": false, - "long_description": "Free text with comments justifying the chosen classification (e.g. based on information on selected snippets and technical analysis), if needed.", - "long_name": "Known AI Goal Classification Discussion", - "mongo_type": "string", - "permitted_values": [], - "placeholder": "", - "public": true, - "required": false, - "short_description": "Free text with comments justifying the chosen classification (e.g. based on information on selected snippets and technical analysis), if needed.", - "short_name": "Known AI Goal Classification Discussion", - "weight": 40 - }, - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "list", - "field_number": "1.2.1", - "instant_facet": true, - "long_description": "An AI Goal which is probably pursued by the AI system referenced in the incident.", - "long_name": "Potential AI Goal", - "mongo_type": "array", - "permitted_values": [], - "placeholder": "", - "public": true, - "required": false, - "short_description": "An AI Goal which is probably pursued by the AI system referenced in the incident.", - "short_name": "Potential AI Goal", - "weight": 50 - }, - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "object-list", - "field_number": "1.2.2", - "instant_facet": false, - "long_description": "One or more snippets that justify the classification.", - "long_name": "Potential AI Goal Snippets", - "mongo_type": "array", - "permitted_values": [], - "placeholder": "", - "public": true, - "required": false, - "short_description": "One or more snippets that justify the classification.", - "short_name": "Potential AI Goal Snippets", - "weight": 40 - }, - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "long_string", - "field_number": "1.2.3", - "instant_facet": false, - "long_description": "Free text with comments justifying the chosen classification (e.g. based on information on selected snippets and technical analysis), if needed.", - "long_name": "Potential AI Goal Classification Discussion", - "mongo_type": "string", - "permitted_values": [], - "placeholder": "", - "public": true, - "required": false, - "short_description": "Free text with comments justifying the chosen classification (e.g. based on information on selected snippets and technical analysis), if needed.", - "short_name": "Potential AI Goal Classification Discussion", - "weight": 40 - }, - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "list", - "field_number": "2.1.1", - "instant_facet": true, - "long_description": "An AI Technology which is almost certainly a part of the implementation of the AI system referenced in the incident.", - "long_name": "Known AI Technology", - "mongo_type": "array", - "permitted_values": [], - "placeholder": "", - "public": true, - "required": false, - "short_description": "An AI Technology which is almost certainly a part of the implementation of the AI system referenced in the incident.", - "short_name": "Known AI Technology", - "weight": 50 - }, - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "object-list", - "field_number": "2.1.2", - "instant_facet": false, - "long_description": "One or more snippets that justify the classification.", - "long_name": "Known AI Technology Snippets", - "mongo_type": "array", - "permitted_values": [], - "placeholder": "", - "public": true, - "required": false, - "short_description": "One or more snippets that justify the classification.", - "short_name": "Known AI Technology Snippets", - "weight": 40 - }, - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "long_string", - "field_number": "2.1.3", - "instant_facet": false, - "long_description": "Free text with comments justifying the chosen classification (e.g. based on information on selected snippets and technical analysis), if needed.", - "long_name": "Known AI Technology Classification Discussion", - "mongo_type": "string", - "permitted_values": [], - "placeholder": "", - "public": true, - "required": false, - "short_description": "Free text with comments justifying the chosen classification (e.g. based on information on selected snippets and technical analysis), if needed.", - "short_name": "Known AI Technology Classification Discussion", - "weight": 40 - }, - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "list", - "field_number": "2.2.1", - "instant_facet": true, - "long_description": "An AI Method / Technology which probably is a part of the implementation of the AI system referenced in the incident.", - "long_name": "Potential AI Technology", - "mongo_type": "array", - "permitted_values": [], - "placeholder": "", - "public": true, - "required": false, - "short_description": "An AI Method / Technology which probably is a part of the implementation of the AI system referenced in the incident.", - "short_name": "Potential AI Technology", - "weight": 50 - }, - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "object-list", - "field_number": "2.2.2", - "instant_facet": false, - "long_description": "One or more snippets that justify the classification.", - "long_name": "Potential AI Technology Snippets", - "mongo_type": "array", - "permitted_values": [], - "placeholder": "", - "public": true, - "required": false, - "short_description": "One or more snippets that justify the classification.", - "short_name": "Potential AI Technology Snippets", - "weight": 40 - }, - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "long_string", - "field_number": "2.2.3", - "instant_facet": false, - "long_description": "Free text with comments justifying the chosen classification (e.g. based on information on selected snippets and technical analysis), if needed.", - "long_name": "Potential AI Technology Classification Discussion", - "mongo_type": "string", - "permitted_values": [], - "placeholder": "", - "public": true, - "required": false, - "short_description": "Free text with comments justifying the chosen classification (e.g. based on information on selected snippets and technical analysis), if needed.", - "short_name": "Potential AI Technology Classification Discussion", - "weight": 40 - }, - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "list", - "field_number": "3.1.1", - "instant_facet": true, - "long_description": "An AI Technical Failure which almost certainly contributes to the AI system failure referenced in the incident.", - "long_name": "Known AI Technical Failure", - "mongo_type": "array", - "permitted_values": [], - "placeholder": "", - "public": true, - "required": false, - "short_description": "An AI Technical Failure which almost certainly contributes to the AI system failure referenced in the incident.", - "short_name": "Known AI Technical Failure", - "weight": 50 - }, - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "object-list", - "field_number": "3.1.2", - "instant_facet": false, - "long_description": "One or more snippets that justify the classification.", - "long_name": "Snippets", - "mongo_type": "array", - "permitted_values": [], - "placeholder": "", - "public": true, - "required": false, - "short_description": "One or more snippets that justify the classification.", - "short_name": "Known AI Technical Failure Snippets", - "weight": 40 - }, - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "long_string", - "field_number": "3.1.3", - "instant_facet": false, - "long_description": "Free text with comments justifying the chosen classification (e.g. based on information on selected snippets and technical analysis), if needed.", - "long_name": "Known AI Technical Failure Classification Discussion", - "mongo_type": "string", - "permitted_values": [], - "placeholder": "", - "public": true, - "required": false, - "short_description": "Free text with comments justifying the chosen classification (e.g. based on information on selected snippets and technical analysis), if needed.", - "short_name": "Known AI Technical Failure Classification Discussion", - "weight": 40 - }, - { - "__typename": "TaxaField_list", - "default": "", - "display_type": "list", - "field_number": "3.2.1", - "instant_facet": true, - "long_description": "An AI Technical Failure which probably contributes to the AI system failure referenced in the incident.", - "long_name": "Potential AI Technical Failure", - "mongo_type": "array", - "permitted_values": [], - "placeholder": "", - "public": true, - 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"long_name": "Potential AI Technical Failure Classification Discussion", - "mongo_type": "string", - "permitted_values": [], - "placeholder": "", - "public": true, - "required": false, - "short_description": "Free text with comments justifying the chosen classification (e.g. based on information on selected snippets and technical analysis), if needed.", - "short_name": "Potential AI Technical Failure Classification Discussion", - "weight": 40 - } - ], - "namespace": "GMF", - "weight": 70 - } - ] - } -} diff --git a/site/gatsby-site/cypress/fixtures/candidates/newsArticles.json b/site/gatsby-site/cypress/fixtures/candidates/newsArticles.json deleted file mode 100644 index c1eae80312..0000000000 --- a/site/gatsby-site/cypress/fixtures/candidates/newsArticles.json +++ /dev/null @@ -1,57 +0,0 @@ -{ - "data": { - "candidates": [ - { - "__typename": "Candidate", - "date_published": "2022-09-22", - "dismissed": null, - "matching_keywords": ["AI"], - "similarity": 0.9999999999999999, - "text": "![An excerpt from Zarya of the Dawn, which received a US copyright\nregistration.](https://cdn.arstechnica.net/wp-\ncontent/uploads/2022/09/zarya_hero-800x448.jpg)\n\n[Enlarge](https://cdn.arstechnica.net/wp-\ncontent/uploads/2022/09/zarya_hero.jpg) /\n\nAn excerpt from the AI-assisted comic book\n\n_Zarya of the Dawn_\n\n, which received a US copyright registration.\n\nIn what might be a first, a New York-based artist named Kris Kashtanova has\nreceived US copyright registration on their graphic novel that features AI-\ngenerated artwork created by [latent\ndiffusion](https://www.louisbouchard.ai/latent-diffusion-models/) AI,\naccording to [their Instagram feed](https://www.instagram.com/p/CivS3iiPigt/)\nand confirmed through a public records search by Ars Technica.\n\nThe registration, effective September 15, applies to a comic book called\n[_Zarya of the Dawn_](https://aicomicbooks.com/). Kashtanova created the\nartwork for _Zarya_ using Midjourney, a commercial image synthesis service. In\ntheir post announcing the news from Tuesday, Kashtanova wrote:\n\n\u003e I got Copyright from the Copyright Office of the USA on my Ai-generated\n\u003e graphic novel. I was open how it was made and put Midjourney on the cover\n\u003e page. It wasn’t altered in any other way. Just the way you saw it here.\n\u003e\n\u003e I tried to make a case that we do own copyright when we make something using\n\u003e AI. I registered it as visual arts work. My certificate is in the mail and I\n\u003e got the number and a confirmation today that it was approved.\n\u003e\n\u003e My friend lawyer gave me this idea and I decided to make a precedent.\n\nGoing by their announcement, Kashtanova approached the registration by saying\nthe artwork was AI-assisted and not created entirely by the AI. Kashtanova\nwrote the comic book story, created the layout, and made artistic choices to\npiece the images together.\n\nIt's likely that artists have registered works created by machine or\nalgorithms before because the history of generative art [extends back to the\n1960s](https://en.wikipedia.org/wiki/Generative_art#History). But this is the\nfirst time we know of that an artist has registered a copyright for art\ncreated by the recent round of image synthesis models powered by latent\ndiffusion, which has been a [contentious\nsubject](https://arstechnica.com/information-technology/2022/09/artists-begin-\nselling-ai-generated-artwork-on-stock-photography-websites/) among artists.\n\nSpeculation about whether AI artwork can be copyrighted has been the subject\nof many articles over the past few months, and just yesterday, [we wrote\nabout](https://arstechnica.com/information-technology/2022/09/fearing-\ncopyright-issues-getty-images-bans-ai-generated-artwork/) Getty Images banning\nAI-generated artwork on its site over unresolved issues about copyright and\nethics issues.\n\nDespite popular misconception (explained in the Getty piece), the US Copyright\nOffice has not ruled against copyright on AI artworks. Instead, it [ruled\nout](https://www.theverge.com/2022/2/21/22944335/us-copyright-office-reject-\nai-generated-art-recent-entrance-to-paradise) copyright registered to an AI as\nthe author instead of a human.\n\n_Zarya of the Dawn_ , which features a main character with an uncanny\nresemblance to the actress [Zendaya](https://en.wikipedia.org/wiki/Zendaya),\nis available for free through the AI Comic Books website. AI artists often use\ncelebrity names in their prompts to achieve consistency between images, since\nthere are many celebrity photographs in the data set used to train Midjourney.\n\n", - "title": "Artist receives first known US copyright registration for latent diffusion AI art | Ars Technica", - "url": "https://arstechnica.com/?p=1883867" - }, - { - "__typename": "Candidate", - "date_published": "2022-09-22", - "dismissed": null, - "matching_keywords": ["AI", "neural net"], - "matching_harm_keywords": ["harm"], - "similarity": 0.9999999999999999, - "text": "![A selection of Stable Diffusion images with a strike-out through\nthem.](https://cdn.arstechnica.net/wp-\ncontent/uploads/2022/09/ai_image_ban_1-800x448.jpg)\n\n[Enlarge](https://cdn.arstechnica.net/wp-\ncontent/uploads/2022/09/ai_image_ban_1.jpg) /\n\nA selection of Stable Diffusion images with a strikeout through them.\n\nArs Technica\n\nGetty Images has banned the sale of AI generative artwork created using image\nsynthesis models such as Stable Diffusion, DALL-E 2, and Midjourney through\nits service, [The Verge\nreports](https://www.theverge.com/2022/9/21/23364696/getty-images-ai-ban-\ngenerated-artwork-illustration-copyright).\n\nTo clarify the new policy, The Verge spoke with Getty Images CEO Craig Peters.\n\"There are real concerns with respect to the copyright of outputs from these\nmodels and unaddressed rights issues with respect to the imagery, the image\nmetadata and those individuals contained within the imagery,\" Peters told the\npublication.\n\nGetty Images is a large repository of stock and archival photographs and\nillustrations, often used by publications (such as Ars Technica) to illustrate\narticles after paying a license fee.\n\nGetty's move [follows](https://arstechnica.com/information-\ntechnology/2022/09/flooded-with-ai-generated-images-some-art-communities-ban-\nthem-completely/) image synthesis bans by smaller art community sites earlier\nthis month, which found their sites flooded with AI-generated work that\nthreatened to overwhelm artwork created without the use of those tools. Getty\nImages competitor Shutterstock [allows](https://arstechnica.com/information-\ntechnology/2022/09/artists-begin-selling-ai-generated-artwork-on-stock-\nphotography-websites/) AI-generated artwork on its site (and although Vice\n[recently reported](https://www.vice.com/en/article/v7vzpj/shutterstock-is-\nremoving-ai-generated-images) the site was removing AI artwork, we still see\nthe same amount as before—and Shutterstock's content submission terms have not\nchanged).\n\n[![A notice from Getty Images and iStock about a ban on \"AI generated\ncontent.\"](https://cdn.arstechnica.net/wp-\ncontent/uploads/2022/09/getty_images_notice-1-640x479.jpg)](https://cdn.arstechnica.net/wp-\ncontent/uploads/2022/09/getty_images_notice-1.jpg)\n\n[Enlarge](https://cdn.arstechnica.net/wp-\ncontent/uploads/2022/09/getty_images_notice-1.jpg) /\n\nA notice from Getty Images and iStock about a ban on \"AI generated content.\"\n\nGetty Images\n\nThe ability to copyright AI-generated artwork has not been tested in court,\nand the ethics of using artists' work without consent (including artwork\n[found](https://waxy.org/2022/08/exploring-12-million-of-the-images-used-to-\ntrain-stable-diffusions-image-generator/) on Getty Images) to train neural\nnetworks that can create almost human-level artwork is still [an open\nquestion](https://arstechnica.com/information-technology/2022/09/have-ai-\nimage-generators-assimilated-your-art-new-tool-lets-you-check/) being debated\nonline. To protect the company's brand and its customers, Getty decided to\navoid the issue altogether with its ban. That said, Ars Technica searched the\nGetty Images library and found AI-generated artwork.\n\n## Can AI artwork be copyrighted?\n\nWhile the creators of popular AI image synthesis models insist their products\ncreate work protected by copyright, the issue of copyright over AI-generated\nimages has not yet been fully resolved. It's worth pointing out that an\n[often-cited article](https://www.smithsonianmag.com/smart-news/us-copyright-\noffice-rules-ai-art-cant-be-copyrighted-180979808/) in the Smithsonian titled\n\"US Copyright Office Rules AI Art Can't Be Copyrighted\" has an erroneous title\nand is often misunderstood. In that case, a researcher attempted to register\nan AI algorithm as the non-human owner of a copyright, which the Copyright\nOffice denied. The copyright owner must be human (or a group of humans, in the\ncase of a corporation).\n\nCurrently, AI image synthesis firms operate under the assumption that the\ncopyright for AI artwork can be registered to a human or corporation, just as\nit is with the output of any other artistic tool. There is some strong\nprecedent to this, and in the Copyright Office's [2022\ndecision](https://www.copyright.gov/rulings-filings/review-\nboard/docs/a-recent-entrance-to-paradise.pdf) rejecting the registry of\ncopyright to an AI (as mentioned above), it referenced a landmark 1884 legal\ncase that affirmed the copyright status of photographs.\n\nEarly in the camera's history, the defendant in the case ( _[Burrow-Giles\nLithographic Co. v. Sarony](https://en.wikipedia.org/wiki/Burrow-\nGiles_Lithographic_Co._v._Sarony))_ claimed that photographs could not be\ncopyrighted because a photo is \"a reproduction on paper of the exact features\nof some natural object or of some person.\" In effect, they argued that a photo\nis the work of a machine and not a creative expression. Instead, the court\nruled that photos can be copyrighted because they are \"representatives of\noriginal intellectual conceptions of [an] author.\"\n\nPeople familiar with the AI generative art process as it now stands, at least\nregarding text-to-image generators, will recognize that their image synthesis\noutputs are \"representatives of original intellectual conceptions of [an]\nauthor\" as well. Despite misconceptions to the contrary, creative input and\nguidance of a human are still necessary to create image synthesis work, no\nmatter how small the contribution. Even the selection of the tool and the\ndecision to execute it is a creative act.\n\nUnder US copyright law, pressing the shutter button of a camera randomly\npointed at a wall [still assigns\ncopyright](https://www.copyright.gov/help/faq/faq-general.html) to the human\nwho took the picture, and yet the human creative input in an image synthesis\nartwork can be much more extensive. So it would make sense that the person who\ninitiated the AI-generated work holds the copyright to the image unless\notherwise restrained by license or terms of use.\n\nAll that said, the question of copyright over AI artwork has yet to be legally\nresolved one way or the other in the United States. Stay tuned for further\ndevelopments.\n\n", - "title": "Fearing copyright issues, Getty Images bans AI-generated artwork | Ars Technica", - "url": "https://arstechnica.com/?p=1883513" - }, - { - "__typename": "Candidate", - "date_published": "2022-09-19", - "dismissed": null, - "matching_keywords": ["AI"], - "similarity": 0.0, - "text": "![Negative photo image of a western blot test\nresult.](https://cdn.arstechnica.net/wp-\ncontent/uploads/2022/09/western_blot_3-800x448.jpg)\n\n[Enlarge](https://cdn.arstechnica.net/wp-\ncontent/uploads/2022/09/western_blot_3.jpg) /\n\nColorized photo image of a Western blot test result.\n\nScientific publishers such as the American Association for Cancer Research\n(AACR) and Taylor \u0026 Francis have begun attempting to detect fraud in academic\npaper submissions with an AI image-checking program called Proofig,\n[reports](https://www.theregister.com/2022/09/12/academic_publishers_are_using_ai)\nThe Register. Proofig, a product of an Israeli firm of the same name, aims to\nhelp use \"artificial intelligence, computer vision and image processing to\nreview image integrity in scientific publications,\"\n[according](https://www.proofig.com/) to the company's website.\n\nDuring a trial that ran from January 2021 to May 2022, AACR used Proofig to\nscreen 1,367 papers accepted for publication, according to The Register. Of\nthose, 208 papers required author contact to clear up issues such as mistaken\nduplications, and four papers were withdrawn.\n\nIn particular, many journals need help detecting image duplication fraud in\n[Western blots](https://en.wikipedia.org/wiki/Western_blot), which are a\nspecific style of protein-detection imagery consisting of line segments of\nvarious widths. Subtle differences in a blot's appearance can translate to\ndramatically different conclusions about test results, and [many cases of\nacademic fraud](https://www.science.org/content/blog-post/down-western-blot)\nhave seen unscrupulous researchers duplicate, crop, stretch, and rotate\nWestern blots to make it appear like they have more (or different) data than\nthey really do. Detecting duplicate images can be tedious work for human eyes,\nwhich is why some firms like Proofig and [ImageTwin](https://imagetwin.ai/),\nan Austrian firm, are attempting to automate the process.\n\nBut both Proofig's and ImageTwin's solutions currently have significant\nlimitations, according to The Register. First, human expertise is still\nnecessary to interpret detection results and reduce false positives. Second,\nProofig is currently expensive due to its computationally intensive process,\ncosting $99 to analyze 120 images for an individual (the journals have\nnegotiated cheaper rates). Currently, both high cost and the requirement for\nmanual oversight is keeping the journals from analyzing every paper at the\nsubmission stage. Instead, they have been reserving its use for later in the\npublication process.\n\nAcademic fraud, while uncommon, can still have a devastating effect on a\npublication's reputation. Between the [massive\nvolume](https://www.nature.com/articles/nj7612-457a) of academic papers being\npublished today and recent revelations about image fraud in [widely cited\nAlzheimer's research](https://www.science.org/content/article/potential-\nfabrication-research-images-threatens-key-theory-alzheimers-disease), the\nfield does seem ripe for computer vision tools that can assist humans with\nfraud detection. Their overall effectiveness—and how widely they become\nadopted—is still a developing story.\n\n", - "title": "AI software helps bust image fraud in academic papers | Ars Technica", - "url": "https://arstechnica.com/?p=1882596" - }, - { - "__typename": "Candidate", - "date_published": "2022-09-22", - "dismissed": null, - "matching_keywords": ["AI", "neural net"], - "similarity": 0.9964958016539909, - "text": "![A pink waveform on a blue background, poetically suggesting\naudio.](https://cdn.arstechnica.net/wp-\ncontent/uploads/2022/09/waveform_hero_1-800x448.jpg)\n\nBenj Edwards / Ars Technica\n\nOn Wednesday, OpenAI released a new open source AI model called\n[Whisper](https://openai.com/blog/whisper/) that recognizes and translates\naudio at a level that approaches human recognition ability. It can transcribe\ninterviews, podcasts, conversations, and more.\n\nOpenAI [trained Whisper](https://cdn.openai.com/papers/whisper.pdf) on 680,000\nhours of audio data and matching transcripts in 98 languages collected from\nthe web. According to OpenAI, this open-collection approach has led to\n\"improved robustness to accents, background noise, and technical language.\" It\ncan also detect the spoken language and translate it to English.\n\nOpenAI describes Whisper as an [encoder-decoder\ntransformer](https://kikaben.com/transformers-encoder-decoder/), a type of\nneural network that can use context gleaned from input data to learn\nassociations that can then be translated into the model's output. OpenAI\npresents this overview of Whisper's operation:\n\n\u003e Input audio is split into 30-second chunks, converted into a log-Mel\n\u003e spectrogram, and then passed into an encoder. A decoder is trained to\n\u003e predict the corresponding text caption, intermixed with special tokens that\n\u003e direct the single model to perform tasks such as language identification,\n\u003e phrase-level timestamps, multilingual speech transcription, and to-English\n\u003e speech translation.\n\nBy open-sourcing Whisper, OpenAI hopes to introduce a new foundation model\nthat others can build on in the future to improve speech processing and\naccessibility tools. OpenAI has a significant track record on this front. In\nJanuary 2021, OpenAI released [CLIP](https://openai.com/blog/clip/), an open\nsource computer vision model that arguably ignited the recent era of rapidly\nprogressing image synthesis technology such as DALL-E 2 and [Stable\nDiffusion](https://arstechnica.com/information-technology/2022/09/with-stable-\ndiffusion-you-may-never-believe-what-you-see-online-again/).\n\nAt Ars Technica, we tested Whisper from code [available on\nGitHub](https://github.com/openai/whisper), and we fed it multiple samples,\nincluding a podcast episode and a particularly difficult-to-understand section\nof audio taken from a telephone interview. Although it took some time while\nrunning through a standard Intel desktop CPU (the technology doesn't work in\nreal time yet), Whisper did a good job of transcribing the audio into text\nthrough the demonstration Python program—far better than some AI-powered audio\ntranscription services we have tried in the past.\n\n[![Example console output from the OpenAI's Whisper demonstration program as\nit transcribes a podcast.](https://cdn.arstechnica.net/wp-\ncontent/uploads/2022/09/whisper_podcast_output-640x142.jpg)](https://cdn.arstechnica.net/wp-\ncontent/uploads/2022/09/whisper_podcast_output.jpg)\n\n[Enlarge](https://cdn.arstechnica.net/wp-\ncontent/uploads/2022/09/whisper_podcast_output.jpg) /\n\nExample console output from the OpenAI's Whisper demonstration program as it\ntranscribes a podcast.\n\nBenj Edwards / Ars Technica\n\nWith the proper setup, Whisper could easily be used to transcribe interviews,\npodcasts, and potentially translate podcasts produced in non-English languages\nto English on your machine—for free. That's a potent combination that might\neventually disrupt the transcription industry.\n\nAs with almost every major new AI model these days, Whisper brings positive\nadvantages and the potential for misuse. On Whisper's [model\ncard](https://github.com/openai/whisper/blob/main/model-card.md) (under the\n\"Broader Implications\" section), OpenAI warns that Whisper could be used to\nautomate surveillance or identify individual speakers in a conversation, but\nthe company hopes it will be used \"primarily for beneficial purposes.\"\n\n", - "title": "AI model from OpenAI automatically recognizes speech and translates it to English | Ars Technica", - "url": "https://arstechnica.com/?p=1883524" - }, - { - "__typename": "Candidate", - "date_published": "2022-09-21", - "dismissed": null, - "matching_keywords": ["AI", "facial recognition"], - "similarity": 0.0, - "text": "![Censored medical images found in the LAION-5B data set used to train AI. The\nblack bars and distortion have been added.](https://cdn.arstechnica.net/wp-\ncontent/uploads/2022/09/medical_images_hero2-800x448.jpg)\n\n[Enlarge](https://cdn.arstechnica.net/wp-\ncontent/uploads/2022/09/medical_images_hero2.jpg) /\n\nCensored medical images found in the LAION-5B data set used to train AI. The\nblack bars and distortion have been added.\n\nArs Technica\n\nLate last week, a California-based [AI\nartist](https://twitter.com/LapineDeLaTerre) who goes by the name Lapine\n[discovered](https://twitter.com/LapineDeLaTerre/status/1570889343845404672?s=20\u0026t=KThzGIaLvD7nV0GNxmu0UA)\nprivate medical record photos taken by her doctor in 2013 referenced in the\n[LAION-5B](https://laion.ai/blog/laion-5b/) image set, which is a scrape of\npublicly available images on the web. AI researchers download a subset of that\ndata to train AI image synthesis models such as Stable Diffusion and [Google\nImagen](https://imagen.research.google/).\n\nLapine discovered her medical photos on a site called [Have I Been\nTrained](https://arstechnica.com/information-technology/2022/09/have-ai-image-\ngenerators-assimilated-your-art-new-tool-lets-you-check/), which lets artists\nsee if their work is in the LAION-5B data set. Instead of doing a text search\non the site, Lapine uploaded a recent photo of herself using the site's\nreverse image search feature. She was surprised to discover a set of two\nbefore-and-after medical photos of her face, which had only been authorized\nfor private use by her doctor, as reflected in an authorization form Lapine\n[tweeted](https://twitter.com/LapineDeLaTerre/status/1570889343845404672) and\nalso provided to Ars.\n\nLapine has a genetic condition called [Dyskeratosis\nCongenita](https://rarediseases.org/rare-diseases/dyskeratosis-congenita/).\n\"It affects everything from my skin to my bones and teeth,\" Lapine told Ars\nTechnica in an interview. \"In 2013, I underwent a small set of procedures to\nrestore facial contours after having been through so many rounds of mouth and\njaw surgeries. These pictures are from my last set of procedures with this\nsurgeon.\"\n\nThe surgeon who possessed the medical photos died of cancer in 2018, according\nto Lapine, and she suspects that they somehow left his practice's custody\nafter that. \"It’s the digital equivalent of receiving stolen property,\" says\nLapine. \"Someone stole the image from my deceased doctor’s files and it ended\nup somewhere online, and then it was scraped into this dataset.\"\n\nLapine prefers to conceal her identity for medical privacy reasons. With\nrecords and photos provided by Lapine, Ars confirmed that there are medical\nimages of her referenced in the LAION data set. During our search for Lapine's\nphotos, we also discovered thousands of similar patient medical record photos\nin the data set, each of which may have a similar questionable ethical or\nlegal status, many of which have likely been integrated into popular image\nsynthesis models that companies like Midjourney and Stability AI offer as a\ncommercial service.\n\nThis does not mean that anyone can suddenly create an AI version of Lapine's\nface (as the technology stands at the moment)—and her name is not linked to\nthe photos—but it bothers her that private medical images have been baked into\na product without any form of consent or recourse to remove them. \"It’s bad\nenough to have a photo leaked, but now it’s part of a product,\" says Lapine.\n\"And this goes for anyone’s photos, medical record or not. And the future\nabuse potential is really high.\"\n\n## Who watches the watchers?\n\nLAION [describes itself](https://laion.ai/about/) as a nonprofit organization\nwith members worldwide, \"aiming to make large-scale machine learning models,\ndatasets and related code available to the general public.\" Its data can be\nused in various projects, from facial recognition to computer vision to image\nsynthesis.\n\nFor example, after an AI training process, some of the images in the LAION\ndata set become the basis of Stable Diffusion's [amazing\nability](https://arstechnica.com/information-technology/2022/09/with-stable-\ndiffusion-you-may-never-believe-what-you-see-online-again/) to generate images\nfrom text descriptions. Since LAION is a [set of URLs](https://laion.ai/faq/)\npointing to images on the web, LAION does not host the images themselves.\nInstead, LAION [says](https://laion.ai/faq/) that researchers must download\nthe images from various locations when they want to use them in a project.\n\n[![The LAION data set is replete with potentially sensitive images collected\nfrom the Internet, such as these, which are now being integrated into\ncommercial machine learning products. Black bars have been added by Ars for\nprivacy purposes.](https://cdn.arstechnica.net/wp-\ncontent/uploads/2022/09/medical_images-640x405.jpg)](https://cdn.arstechnica.net/wp-\ncontent/uploads/2022/09/medical_images.jpg)\n\n[Enlarge](https://cdn.arstechnica.net/wp-\ncontent/uploads/2022/09/medical_images.jpg) /\n\nThe LAION data set is replete with potentially sensitive images collected from\nthe Internet, such as these, which are now being integrated into commercial\nmachine learning products. Black bars have been added by Ars for privacy\npurposes.\n\nArs Technica\n\nUnder these conditions, responsibility for a particular image's inclusion in\nthe LAION set then becomes a fancy game of pass the buck. A friend of Lapine's\nposed an open question on the #safety-and-privacy channel of LAION's Discord\nserver last Friday asking how to remove her images from the set. LAION\nengineer Romain Beaumont replied, \"The best way to remove an image from the\nInternet is to ask for the hosting website to stop hosting it,\" wrote\nBeaumont. \"We are not hosting any of these images.\"\n\nIn the US, scraping publicly available data from the Internet [appears to be\nlegal](https://medium.com/@tjwaterman99/web-scraping-is-now-\nlegal-6bf0e5730a78), as the results from a 2019 court case affirm. Is it\nmostly the deceased doctor's fault, then? Or the site that hosts Lapine's\nillicit images on the web?\n\nArs contacted LAION for comment on these questions but did not receive a\nresponse by press time. LAION's website does provide [a\nform](https://laion.ai/gdpr/) where European citizens can request information\nremoved from their database to comply with the EU's GDPR laws, but only if a\nphoto of a person is associated with a name in the image's metadata. Thanks to\nservices such as [PimEyes](https://pimeyes.com/en), however, it has become\ntrivial to associate someone's face with names through other means.\n\nUltimately, Lapine understands how the chain of custody over her private\nimages failed but still would like to see her images removed from the LAION\ndata set. \"I would like to have a way for anyone to ask to have their image\nremoved from the data set without sacrificing personal information. Just\nbecause they scraped it from the web doesn’t mean it was supposed to be public\ninformation, or even on the web at all.\"\n\n", - "title": "Artist finds private medical record photos in popular AI training data set | Ars Technica", - "url": "https://arstechnica.com/?p=1882591" - } - ] - } -} diff --git a/site/gatsby-site/cypress/fixtures/checklists/riskSortingChecklist.json b/site/gatsby-site/cypress/fixtures/checklists/riskSortingChecklist.json deleted file mode 100644 index e9299b1765..0000000000 --- a/site/gatsby-site/cypress/fixtures/checklists/riskSortingChecklist.json +++ /dev/null @@ -1,137 +0,0 @@ -{ - "data": { - "checklist": { - "__typename": "Checklist", - "about": "", - "date_created": "2024-01-02T23:38:07.875Z", - "date_updated": "2024-01-02T23:38:07.875Z", - "id": "1b4d984c-84c9-4417-a57f-a04acde37c36", - "name": "Unspecified System", - "risks": [ - { - "__typename": "ChecklistRisk", - "generated": false, - "id": "7876ca98-ca8a-4365-8c39-203474c1dc38", - "likelihood": "", - "precedents": [ - { - "__typename": "ChecklistRiskPrecedent", - "description": "The French digital care company, Nabla, in researching GPT-3’s capabilities for medical documentation, diagnosis support, and treatment recommendation, found its inconsistency and lack of scientific and medical expertise unviable and risky in healthcare applications. This incident has been downgraded to an issue as it does not meet current ingestion criteria.", - "incident_id": 287, - "tags": [ - "GMF:Known AI Goal:Question Answering", - "GMF:Known AI Technology:Transformer", - "GMF:Known AI Technology:Language Modeling", - "GMF:Known AI Technology:Distributional Learning", - "GMF:Known AI Technology Classification Discussion:Distributional Learning: If no training data citing sources is available and/or not enough data from a medical domain were available.", - "GMF:Potential AI Technical Failure:Limited Dataset", - "GMF:Potential AI Technical Failure:Problematic Input", - "GMF:Potential AI Technical Failure:Robustness Failure", - "GMF:Potential AI Technical Failure:Overfitting", - "GMF:Potential AI Technical Failure:Underfitting", - "GMF:Potential AI Technical Failure:Inadequate Sequential Memory", - "GMF:Potential AI Technical Failure Classification Discussion:Limited Dataset: If no training data citing sources is available and/or not enough data from a medical domain were available.\n\nProblematic Input: If the prompt does not state that references are required.\n\nOverfitting: System does not capture the semantic content of the prompt, but focuses on specific verbage.\n\nUnderfitting: Due to lack of fine-tuning, the model can be considered as having a poor fit for specific medical questions.", - "GMF:Known AI Technical Failure:Distributional Artifacts" - ], - "title": "OpenAI’s GPT-3 Reported as Unviable in Medical Tasks by Healthcare Firm" - } - ], - "risk_notes": "", - "risk_status": "Mitigated", - "severity": "Minor", - "tags": [ - "GMF:Known AI Technical Failure:Distributional Artifacts" - ], - "title": "Distributional Artifacts", - "touched": false - }, - { - "__typename": "ChecklistRisk", - "generated": false, - "id": "9a1d30d3-b45c-4d7f-a593-8d7dfe78ffb7", - "likelihood": "", - "precedents": [ - { - "__typename": "ChecklistRiskPrecedent", - "description": "Facebook's automatic language translation software incorrectly translated an Arabic post saying \"Good morning\" into Hebrew saying \"hurt them,\" leading to the arrest of a Palestinian man in Beitar Illit, Israel.", - "incident_id": 72, - "tags": [ - "GMF:Known AI Goal:Translation", - "GMF:Known AI Goal Classification Discussion:Translation: Presumably the arabic dialect in the text is not represented adequately in the training data, hence the translation performance issues. \n", - 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"GMF:Potential AI Technical Failure:Generalization Failure", - "GMF:Potential AI Technical Failure Classification Discussion:Generalization Failure: Perhaps only one (standard arabic) or a few dialects are supported, and one or more language models is used as fallback for all arabic languages.", - "GMF:Known AI Technology:Convolutional Neural Network", - "GMF:Known AI Technology:Recurrent Neural Network", - "GMF:Known AI Technology:Distributional Learning", - "GMF:Potential AI Technology:Intermediate modeling", - "GMF:Potential AI Technology:Classification", - "GMF:Potential AI Technology:Multimodal Learning", - "GMF:Potential AI Technology:Image Classification", - "GMF:Potential AI Technology Classification Discussion:Intermediate modeling: Perhaps intermmediate languages are used (i.e. if no model has been trained to translate X to Y, use X->Z and then Z->Y), which accumulate errors.\n\nClassification: GIven the amount of supported languages for translation, a system must exist to detect the input language and classify amongst supported languages.\n\nMultimodal Learning: If image was also utilized to generate the translation, that would provide additional evidence to the mistranslation.\n\nImage Classification: If multimodal learning is used, perhaps the buldozer was recognized and its extracted keyword contributed to the bias in the NLP domain." - ], - "title": "Facebook translates 'good morning' into 'attack them', leading to arrest" - } - ], - "risk_notes": "", - "risk_status": "Mitigated", - "severity": "Severe", - "tags": [ - "GMF:Known AI Technical Failure:Distributional Bias" - ], - "title": "Distributional Bias", - "touched": false - } - ], - "tags_goals": [ - "GMF:Known AI Goal:Question Answering" - ], - "tags_methods": [ - "GMF:Potential AI Technology:Classification", - "GMF:Known AI Technology:Language Modeling" - ], - "tags_other": [] - } - } -} diff --git a/site/gatsby-site/cypress/fixtures/checklists/riskSortingRisks.json b/site/gatsby-site/cypress/fixtures/checklists/riskSortingRisks.json deleted file mode 100644 index 7bb6f3732c..0000000000 --- a/site/gatsby-site/cypress/fixtures/checklists/riskSortingRisks.json +++ /dev/null @@ -1,415 +0,0 @@ -{ - "data": { - "risks": [ - { - "__typename": "RisksPayloadItem", - "precedents": [ - { - "__typename": "RisksPayloadPrecedent", - "description": "There are multiple reports of Amazon Alexa products (Echo, Echo Dot) reacting and acting upon unintended stimulus, usually from television commercials or news reporter's voices.", - 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], - "title": "Amazon Alexa Responding to Environmental Inputs" - }, - { - "__typename": "RisksPayloadPrecedent", - "description": "In Taiwan, a Tesla Model 3 on Autopilot mode whose driver did not pay attention to the road collided with a road repair truck; a road engineer immediately placed crash warnings in front of the Tesla, but soon after got hit and was killed by a BMW when its driver failed to see the sign and crashed into the accident.", - "incident_id": 221, - "tags": [ - "GMF:Potential AI Technology:Convolutional Neural Network", - "GMF:Potential AI Technology:Visual Object Detection", - "GMF:Potential AI Technology:Classification", - "GMF:Known AI Technology:Image Segmentation", - "GMF:Potential AI Technology Classification Discussion:Visual Object Detection: Potentially subtask of segmentation.\n\nClassification: Potentially subtask of segmentation.", - "GMF:Known AI Technical Failure:Misuse", - "GMF:Known AI Technical Failure:Generalization Failure", - "GMF:Known AI Technical Failure Classification Discussion:Misuse: Driver should not use autopilot without supervision.", - 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"GMF:Potential AI Technical Failure:Context Misidentification", - "GMF:Potential AI Technical Failure:Inadequate Data Sampling", - "GMF:Potential AI Technical Failure:Problematic Input", - "GMF:Known AI Technical Failure:Lack of Explainability", - "GMF:Known AI Technical Failure:Incomplete Data Attribute Capture", - "GMF:Known AI Technical Failure Classification Discussion:Incomplete Data Attribute Capture: Makeup skill assessment with verbal descriptions, rather visual media." - ], - "title": "Three Make-Up Artists Lost Jobs Following Black-Box Automated Decision by HireVue" - } - ], - "tags": [ - "GMF:Known AI Technical Failure:Lack of Explainability" - ], - "title": "Lack of Explainability" - }, - { - "__typename": "RisksPayloadItem", - "precedents": [ - { - "__typename": "RisksPayloadPrecedent", - "description": "Three make-up artists lost their positions following an algorithmically-assessed video interview by HireVue who reportedly failed to provide adequate explanation of the findings.", - "incident_id": 192, - "tags": [ - "GMF:Known AI Goal:Automatic Skill Assessment", - "GMF:Known AI Technology:Automatic Speech Recognition", - "GMF:Potential AI Technology:Regression", - "GMF:Potential AI Technology:Classification", - "GMF:Potential AI Technical Failure:Dataset Imbalance", - "GMF:Potential AI Technical Failure:Context Misidentification", - "GMF:Potential AI Technical Failure:Inadequate Data Sampling", - "GMF:Potential AI Technical Failure:Problematic Input", - "GMF:Known AI Technical Failure:Lack of Explainability", - "GMF:Known AI Technical Failure:Incomplete Data Attribute Capture", - "GMF:Known AI Technical Failure Classification Discussion:Incomplete Data Attribute Capture: Makeup skill assessment with verbal descriptions, rather visual media." - ], - "title": "Three Make-Up Artists Lost Jobs Following Black-Box Automated Decision by HireVue" - } - ], - "tags": [ - "GMF:Known AI Technical Failure:Incomplete Data Attribute Capture" - ], - "title": "Incomplete Data Attribute Capture" - }, - { - "__typename": "RisksPayloadItem", - "precedents": [ - { - "__typename": "RisksPayloadPrecedent", - "description": "In Taiwan, a Tesla Model 3 on Autopilot mode whose driver did not pay attention to the road collided with a road repair truck; a road engineer immediately placed crash warnings in front of the Tesla, but soon after got hit and was killed by a BMW when its driver failed to see the sign and crashed into the accident.", - "incident_id": 221, - "tags": [ - "GMF:Potential AI Technology:Convolutional Neural Network", - "GMF:Potential AI Technology:Visual Object Detection", - "GMF:Potential AI Technology:Classification", - "GMF:Known AI Technology:Image Segmentation", - "GMF:Potential AI Technology Classification Discussion:Visual Object Detection: Potentially subtask of segmentation.\n\nClassification: Potentially subtask of segmentation.", - "GMF:Known AI Technical Failure:Misuse", - "GMF:Known AI Technical Failure:Generalization Failure", - "GMF:Known AI Technical Failure Classification Discussion:Misuse: Driver should not use autopilot without supervision.", - "GMF:Known AI Goal:Autonomous Driving" - ], - "title": "A Road Engineer Killed Following a Collision Involving a Tesla on Autopilot" - } - ], - "tags": [ - "GMF:Known AI Technical Failure:Generalization Failure" - ], - "title": "Generalization Failure" - }, - { - "__typename": "RisksPayloadItem", - "precedents": [ - { - "__typename": "RisksPayloadPrecedent", - "description": "The French digital care company, Nabla, in researching GPT-3’s capabilities for medical documentation, diagnosis support, and treatment recommendation, found its inconsistency and lack of scientific and medical expertise unviable and risky in healthcare applications. This incident has been downgraded to an issue as it does not meet current ingestion criteria.", - "incident_id": 287, - "tags": [ - "GMF:Known AI Goal:Question Answering", - "GMF:Known AI Technology:Transformer", - "GMF:Known AI Technology:Language Modeling", - "GMF:Known AI Technology:Distributional Learning", - "GMF:Known AI Technology Classification Discussion:Distributional Learning: If no training data citing sources is available and/or not enough data from a medical domain were available.", - "GMF:Potential AI Technical Failure:Limited Dataset", - "GMF:Potential AI Technical Failure:Problematic Input", - "GMF:Potential AI Technical Failure:Robustness Failure", - "GMF:Potential AI Technical Failure:Overfitting", - "GMF:Potential AI Technical Failure:Underfitting", - "GMF:Potential AI Technical Failure:Inadequate Sequential Memory", - "GMF:Potential AI Technical Failure Classification Discussion:Limited Dataset: If no training data citing sources is available and/or not enough data from a medical domain were available.\n\nProblematic Input: If the prompt does not state that references are required.\n\nOverfitting: System does not capture the semantic content of the prompt, but focuses on specific verbage.\n\nUnderfitting: Due to lack of fine-tuning, the model can be considered as having a poor fit for specific medical questions.", - "GMF:Known AI Technical Failure:Distributional Artifacts" - ], - "title": "OpenAI’s GPT-3 Reported as Unviable in Medical Tasks by Healthcare Firm" - } - ], - "tags": [ - "GMF:Known AI Technical Failure:Distributional Artifacts" - ], - "title": "Distributional Artifacts" - } - ] - } -} diff --git a/site/gatsby-site/cypress/fixtures/classifications/cssettool.json b/site/gatsby-site/cypress/fixtures/classifications/cssettool.json deleted file mode 100644 index 51b71a641e..0000000000 --- a/site/gatsby-site/cypress/fixtures/classifications/cssettool.json +++ /dev/null @@ -1,644 +0,0 @@ -{ - "data": { - "classifications": [ - { - "__typename": "Classification", - "_id": "6422fb4ad6ebced21049f7a2", - "attributes": [ - { - "__typename": "ClassificationAttribute", - "short_name": "Physical Objects", - "value_json": "\"yes\"" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "Entertainment Industry", - "value_json": "\"no\"" - }, - { - "__typename": "ClassificationAttribute", - 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This could have minimized damaged during the crash. Ultimately, the Tesla was responsible for the crash\\\"\"}]}]" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "Lives Lost", - "value_json": "null" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "Injuries", - "value_json": "null" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "Estimated Harm Quantities", - "value_json": "false" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "Notes ( Tangible Harm Quantities Information)", - "value_json": null - }, - { - "__typename": "ClassificationAttribute", - "short_name": "AI System Description", - "value_json": null - }, - { - "__typename": "ClassificationAttribute", - "short_name": "Data Inputs", - "value_json": "[\"road data\",\"traffic\",\"GPS\"]" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "Sector of Deployment", - "value_json": null - }, - { - "__typename": "ClassificationAttribute", - "short_name": "Public Sector Deployment", - "value_json": null - }, - { - "__typename": "ClassificationAttribute", - "short_name": "Autonomy Level", - "value_json": null - }, - { - "__typename": "ClassificationAttribute", - "short_name": "Notes (Information about AI System)", - "value_json": null - }, - { - "__typename": "ClassificationAttribute", - "short_name": "Intentional Harm", - "value_json": null - }, - { - "__typename": "ClassificationAttribute", - "short_name": "Physical System Type", - "value_json": null - }, - { - "__typename": "ClassificationAttribute", - "short_name": "AI Task", - "value_json": null - }, - { - "__typename": "ClassificationAttribute", - "short_name": "AI tools and methods", - "value_json": null - }, - { - "__typename": "ClassificationAttribute", - "short_name": "Notes (AI Functionality and Techniques)", - "value_json": null - } - ], - "incidents": [ - { - "incident_id": 52 - } - ], - "namespace": "CSETv1_Annotator-2", - "notes": "This a note from the annotator 2", - "publish": false - } - ] - } -} \ No newline at end of file diff --git a/site/gatsby-site/cypress/fixtures/classifications/incident97Classifications.json b/site/gatsby-site/cypress/fixtures/classifications/incident97Classifications.json deleted file mode 100644 index 393edb1022..0000000000 --- a/site/gatsby-site/cypress/fixtures/classifications/incident97Classifications.json +++ /dev/null @@ -1,79 +0,0 @@ -{ - "data": { - "classifications": [ - { - "__typename": "Classification", - "_id": "61d8cecbb9af57365c029cd7", - "classifications": { - "AIApplications": [ - "autonomous driving" - ], - "AISystemDescription": "Tesla's Autopilot can carry out some driving functions autonomously, but requires the driver to keep their hands on the wheel", - "AITechniques": [ - "computer vision" - ], - "AnnotationStatus": "4. Peer review complete", - "Annotator": "1", - "BeginningDate": "", - "DataInputs": [ - "images of surroundings" - ], - "EndingDate": "", - "FinancialCost": "", - "FullDescription": "A Tesla Model 3 driver shared a video of their car's Autopilot system malfunctioning. The screen inside the car shows that Autopilot believes it is facing a traffic light that is alternating between red and yellow, when in fact it is looking at several white flags with the letters \"COOP\" written vertically.", - "HarmDistributionBasis": [], - "HarmType": [ - "Other" - ], - "InfrastructureSectors": [ - "Transportation" - ], - "Intent": "Accident", - "LawsImplicated": [], - "LevelOfAutonomy": "Medium", - "LivesLost": false, - "Location": "Switzerland", - "NamedEntities": [ - "Starbucks", - "Kronos" - ], - "NatureOfEndUser": "Amateur", - "NearMiss": "Near miss", - "PhysicalSystem": [ - "Vehicle/mobile robot" - ], - "ProblemNature": [ - "Robustness" - ], - "PublicSectorDeployment": false, - "Publish": null, - "RelevantAIFunctions": [ - "Perception", - "Cognition", - "Action" - ], - "Reviewer": "8", - "SectorOfDeployment": [ - "Transportation and storage" - ], - "Severity": "Negligible", - "ShortDescription": "A Tesla Model 3 mis-recognized flags with \"COOP\" written vertically on them as traffic lights.", - "SystemDeveloper": [ - "Tesla" - ], - "TechnologyPurveyor": [ - "Tesla" - ], - "__typename": "ClassificationClassification" - }, - "incidents": [ - { - "incident_id": 97 - } - ], - "namespace": "CSET", - "notes": "" - } - ] - } -} \ No newline at end of file diff --git a/site/gatsby-site/cypress/fixtures/classifications/missingAttributes.json b/site/gatsby-site/cypress/fixtures/classifications/missingAttributes.json deleted file mode 100644 index 441c7e4f63..0000000000 --- a/site/gatsby-site/cypress/fixtures/classifications/missingAttributes.json +++ /dev/null @@ -1,474 +0,0 @@ -[ - { - "_id": { - "$oid": "648faecdf5ee4963902350e2" - }, - "attributes": [ - { - "short_name": "Incident Number", - "value_json": "40" - }, - { - "short_name": "Annotator", - "value_json": "\"007\"" - }, - { - "short_name": "Annotation Status", - "value_json": "\"2. Initial annotation complete\"" - }, - { - "short_name": "Peer Reviewer" - }, - { - "value_json": "false", - "short_name": "Quality Control" - }, - { - "value_json": "\"no\"", - "short_name": "Physical Objects" - }, - { - "short_name": "Entertainment Industry", - "value_json": "\"no\"" - }, - { - "value_json": "\"no\"", - "short_name": "Report, Test, or Study of data" - }, - { - "value_json": "\"yes\"", - "short_name": "Deployed" - }, - { - "short_name": "Producer Test in Controlled Conditions", - "value_json": "\"no\"" - }, - { - "value_json": "\"no\"", - "short_name": "Producer Test in Operational Conditions" - }, - { - "short_name": "User Test in Controlled Conditions", - "value_json": "\"no\"" - }, - { - "short_name": "User Test in Operational Conditions", - "value_json": "\"yes\"" - }, - { - "short_name": "Harm Domain", - "value_json": "\"yes\"" - }, - { - "short_name": "Tangible Harm", - "value_json": "\"unclear\"" - }, - { - "short_name": "AI System", - "value_json": "\"yes\"" - }, - { - "short_name": "Clear Link to AI", - "value_json": "\"yes\"" - }, - { - "short_name": "There is a potentially identifiable specific entity that experienced the harm", - "value_json": "false" - }, - { - "short_name": "AI Harm Level", - "value_json": "\"unclear\"" - }, - { - "short_name": "AI Tangible Harm Level Notes", - "value_json": "\"It is unclear from who are the specific entities that where false accused\"" - }, - { - "short_name": "Impact on Critical Services", - "value_json": "\"no\"" - }, - { - "short_name": "Rights Violation", - "value_json": "\"yes\"" - }, - { - "short_name": "Involving Minor", - "value_json": "\"no\"" - }, - { - "short_name": "Detrimental Content", - "value_json": "\"no\"" - }, - { - "short_name": "Protected Characteristic", - "value_json": "\"yes\"" - }, - { - "short_name": "Harm Distribution Basis", - "value_json": "[\"race\"]" - }, - { - "short_name": "Notes (special interest intangible harm)" - }, - { - "short_name": "Special Interest Intangible Harm", - "value_json": "\"yes\"" - }, - { - "value_json": "\"yes\"", - "short_name": "AI Linked to Special Interest Intangible Harm" - }, - { - "short_name": "Harmed Class of Entities", - "value_json": "true" - }, - { - "short_name": "Annotator’s AI special interest intangible harm assessment", - "value_json": "\"yes\"" - }, - { - "short_name": "Notes (AI special interest intangible harm)" - }, - { - "short_name": "Date of Incident Year", - "value_json": "\"2016\"" - }, - { - "short_name": "Date of Incident Month" - }, - { - "short_name": "Date of Incident Day" - }, - { - "short_name": "Estimated Date", - "value_json": "false" - }, - { - "short_name": "Multiple AI Interaction", - "value_json": "\"no\"" - }, - { - "value_json": "\"yes\"", - "short_name": "Embedded" - }, - { - "short_name": "Location City" - }, - { - "short_name": "Location State/Province (two letters)" - }, - { - "short_name": "Location Country (two letters)", - "value_json": "\"US\"" - }, - { - "value_json": "\"North America\"", - "short_name": "Location Region" - }, - { - "short_name": "Infrastructure Sectors" - }, - { - "short_name": "Operating Conditions" - }, - { - "short_name": "Notes (Environmental and Temporal Characteristics)" - }, - { - "short_name": "Entities", - "value_json": "[{\"attributes\":[{\"short_name\":\"Entity\",\"value_json\":\"\\\"COMPAS\\\"\"},{\"short_name\":\"Named Entity\",\"value_json\":\"true\"},{\"short_name\":\"Entity type\",\"value_json\":\"\\\"product\\\"\"},{\"short_name\":\"Entity Relationship to the AI\",\"value_json\":\"[\\\"AI\\\"]\"},{\"short_name\":\"Harm Category Experienced\",\"value_json\":\"\\\"not applicable\\\"\"},{\"short_name\":\"Harm Type Experienced\",\"value_json\":\"\\\"not applicable\\\"\"}]},{\"attributes\":[{\"short_name\":\"Entity\",\"value_json\":\"\\\"Equivant\\\"\"},{\"short_name\":\"Named Entity\",\"value_json\":\"true\"},{\"short_name\":\"Entity type\",\"value_json\":\"\\\"for-profit organization\\\"\"},{\"short_name\":\"Entity Relationship to the AI\",\"value_json\":\"[\\\"developer\\\"]\"},{\"short_name\":\"Harm Category Experienced\",\"value_json\":\"\\\"not applicable\\\"\"},{\"short_name\":\"Harm Type Experienced\",\"value_json\":\"\\\"not applicable\\\"\"}]},{\"attributes\":[{\"short_name\":\"Entity\",\"value_json\":\"\\\"ProPublica\\\"\"},{\"short_name\":\"Named Entity\",\"value_json\":\"true\"},{\"short_name\":\"Entity Relationship to the AI\",\"value_json\":\"[\\\"researcher\\\"]\"},{\"short_name\":\"Entity type\",\"value_json\":\"\\\"for-profit organization\\\"\"},{\"short_name\":\"Harm Category Experienced\",\"value_json\":\"\\\"not applicable\\\"\"},{\"short_name\":\"Harm Type Experienced\",\"value_json\":\"\\\"not applicable\\\"\"}]},{\"attributes\":[{\"short_name\":\"Entity\",\"value_json\":\"\\\"Wisconsin Supreme Court\\\"\"},{\"short_name\":\"Named Entity\",\"value_json\":\"true\"},{\"short_name\":\"Entity type\",\"value_json\":\"\\\"government entity\\\"\"},{\"short_name\":\"Entity Relationship to the AI\",\"value_json\":\"[\\\"deployer\\\"]\"},{\"short_name\":\"Harm Category Experienced\",\"value_json\":\"\\\"not applicable\\\"\"},{\"short_name\":\"Harm Type Experienced\",\"value_json\":\"\\\"not applicable\\\"\"}]},{\"attributes\":[{\"short_name\":\"Entity\",\"value_json\":\"\\\"Black people\\\"\"},{\"short_name\":\"Named Entity\",\"value_json\":\"false\"},{\"short_name\":\"Entity type\",\"value_json\":\"\\\"group of individuals\\\"\"},{\"short_name\":\"Entity Relationship to the AI\",\"value_json\":\"[\\\"user\\\"]\"},{\"short_name\":\"Harm Category Experienced\",\"value_json\":\"\\\"AI special interest intangible harm\\\"\"},{\"short_name\":\"Harm Type Experienced\",\"value_json\":\"\\\"violation of human rights, civil liberties, civil rights, or democratic norms\\\"\"}]}]" - }, - { - "short_name": "Lives Lost", - "value_json": "0" - }, - { - "short_name": "Injuries", - "value_json": "0" - }, - { - "short_name": "Estimated Harm Quantities", - "value_json": "false" - }, - { - "short_name": "Notes ( Tangible Harm Quantities Information)" - }, - { - "short_name": "AI System Description", - "value_json": "\"automated decision-making (ADM) systems that predict the likelihood of criminals reoffending, used in the US criminal justice system\"" - }, - { - "value_json": "[\"Unclear\"]", - "short_name": "Data Inputs" - }, - { - "short_name": "Sector of Deployment", - "value_json": "[\"law enforcement\"]" - }, - { - "short_name": "Public Sector Deployment", - "value_json": "\"no\"" - }, - { - "value_json": "\"Autonomy3\"", - "short_name": "Autonomy Level" - }, - { - "short_name": "Notes (Information about AI System)" - }, - { - "short_name": "Intentional Harm", - "value_json": "\"No. Not intentionally designed to perform harm\"" - }, - { - "short_name": "Physical System Type" - }, - { - "value_json": "[\"predict a defendant’s risk of committing another crime\"]", - "short_name": "AI Task" - }, - { - "short_name": "AI tools and methods", - "value_json": "[\"unclear\"]" - }, - { - "short_name": "Notes (AI Functionality and Techniques)" - } - ], - "namespace": "CSETv1_Annotator-3", - "notes": "", - "publish": false, - "incidents": [ - { - "$numberInt": "40" - } - ], - "reports": [] - }, - { - "_id": { - "$oid": "63f3d2cface82aca35c26da3" - }, - "publish": true, - "attributes": [ - { - "short_name": "Incident Number", - "value_json": "208" - }, - { - "value_json": "\"003\"", - "short_name": "Annotator" - }, - { - "short_name": "Annotation Status", - "value_json": "\"2. 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In peer review", - "Annotator": "2", - "BeginningDate": "2015-01-01", - "DataInputs": [ - "asdasd" - ], - "EndingDate": "2015-01-01", - "FinancialCost": "dasdas", - "FullDescription": "The staff scheduling tool used by Starbucks has led to staff working hours volatile and erratic schedules. Some store managers use a scheduling software, Kronos, to optimize scheduling and cut labor costs, however Starbucks refuses to accept or deny using Kronos.", - "HarmDistributionBasis": [], - "HarmType": [ - "Psychological harm" - ], - "InfrastructureSectors": [ - "Emergency services", - "Energy", - "Financial services" - ], - "Intent": "Unclear", - "LawsImplicated": [ - "dsadasd" - ], - "LevelOfAutonomy": "High", - "LivesLost": false, - "Location": "Global", - "NamedEntities": [ - "Starbucks", - "Kronos" - ], - "NatureOfEndUser": "Expert", - "NearMiss": "Unclear/unknown", - "PhysicalSystem": [ - "Weapons system" - ], - "ProblemNature": [], - "PublicSectorDeployment": false, - "Publish": null, - "RelevantAIFunctions": [ - "Cognition", - "Action", - "Unclear" - ], - "Reviewer": "6", - "SectorOfDeployment": [ - "Manufacturing", - "Electricity, gas, steam and air conditioning supply", - "Water supply", - "Construction", - "Wholesale and retail trade", - "Transportation and storage", - "Accommodation and food service activities", - "Information and communication", - "Financial and insurance activities", - "Real estate activities", - "Professional, scientific and technical activities", - "Administrative and support service activities", - "Public administration and defence", - "Education", - "Human health and social work activities", - "Arts, entertainment and recreation", - "Other service activities", - "Activities of households as employers", - "Activities of extraterritorial organizations and bodies" - ], - "Severity": "Negligible", - "ShortDescription": "Issues with Starbucks worker's schedules may be linked to a staffing software, Kronos.", - "SystemDeveloper": [ - "dasdasd" - ], - "TechnologyPurveyor": [ - "Starbuck" - ], - "__typename": "ClassificationClassification" - }, - "incidents": [ - { - "incident_id": 10 - } - ], - "namespace": "CSET", - "notes": "This is an updated note" - } - } -} \ No newline at end of file diff --git a/site/gatsby-site/cypress/fixtures/classifications/upsertCSETv1merge.json b/site/gatsby-site/cypress/fixtures/classifications/upsertCSETv1merge.json deleted file mode 100644 index 7950a6199c..0000000000 --- a/site/gatsby-site/cypress/fixtures/classifications/upsertCSETv1merge.json +++ /dev/null @@ -1,321 +0,0 @@ -{ - "data": { - "upsertOneClassification": { - "__typename": "Classification", - "_id": "646ff84fb0365cada1ea87c7", - "attributes": [ - { - "__typename": "ClassificationAttribute", - "short_name": "Incident Number", - "value_json": "52" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "Annotator", - "value_json": "\"006\"" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "Annotation Status", - "value_json": "\"2. Initial annotation complete\"" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "Peer Reviewer", - "value_json": "\"weqweq\"" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "Quality Control", - "value_json": "true" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "Physical Objects", - "value_json": "\"yes\"" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "Entertainment Industry", - "value_json": "\"no\"" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "Report, Test, or Study of data", - "value_json": "\"no\"" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "Deployed", - "value_json": "\"yes\"" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "Producer Test in Controlled Conditions", - "value_json": "\"no\"" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "Producer Test in Operational Conditions", - "value_json": "\"no\"" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "User Test in Controlled Conditions", - "value_json": "\"no\"" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "User Test in Operational Conditions", - "value_json": "\"no\"" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "Harm Domain", - "value_json": "\"yes\"" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "Tangible Harm", - "value_json": "\"tangible harm definitively occurred\"" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "AI System", - "value_json": "\"yes\"" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "Clear Link to AI", - "value_json": "\"yes\"" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "There is a potentially identifiable specific entity that experienced the harm", - "value_json": "true" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "AI Harm Level", - "value_json": "\"AI tangible harm near-miss\"" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "AI Tangible Harm Level Notes", - "value_json": "\"Annotator 1: \\n\\n The Tesla's autopilot failed to notice the trailer of the trucker and deploy breaks. This could have minimized damage during the crash. However, the crash was ultimately at fault of the Tesla driver.\"" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "Impact on Critical Services", - "value_json": "\"no\"" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "Rights Violation", - "value_json": "\"no\"" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "Involving Minor", - "value_json": "\"no\"" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "Detrimental Content", - "value_json": "\"no\"" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "Protected Characteristic", - "value_json": "\"no\"" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "Harm Distribution Basis", - "value_json": "[\"none\",\"ideology\",\"financial means\",\"disability\"]" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "Notes (special interest intangible harm)", - "value_json": "\"\"" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "Special Interest Intangible Harm", - "value_json": "\"no\"" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "AI Linked to Special Interest Intangible Harm", - "value_json": "\"no\"" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "Harmed Class of Entities", - "value_json": "true" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "Annotator’s AI special interest intangible harm assessment", - "value_json": "\"no\"" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "Notes (AI special interest intangible harm)", - "value_json": "\"\"" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "Date of Incident Year", - "value_json": "\"2016\"" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "Date of Incident Month", - "value_json": "\"05\"" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "Date of Incident Day", - "value_json": "\"07\"" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "Estimated Date", - "value_json": "true" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "Multiple AI Interaction", - "value_json": "\"no\"" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "Embedded", - "value_json": "\"yes\"" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "Location City", - "value_json": "\"Williston\"" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "Location State/Province (two letters)", - "value_json": "\"FL\"" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "Location Country (two letters)", - "value_json": "\"US\"" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "Location Region", - "value_json": "\"North America\"" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "Infrastructure Sectors", - "value_json": "\"\"" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "Operating Conditions", - "value_json": "\"\"" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "Notes (Environmental and Temporal Characteristics)", - "value_json": "\"\"" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "Entities", - "value_json": "[{\"attributes\":[{\"short_name\":\"Entity\",\"value_json\":\"\\\"Joshua Brown\\\"\"},{\"short_name\":\"Named Entity\",\"value_json\":\"true\"},{\"short_name\":\"Entity type\",\"value_json\":\"\\\"individual\\\"\"},{\"short_name\":\"Entity Relationship to the AI\",\"value_json\":\"[\\\"user\\\"]\"},{\"short_name\":\"Harm Category Experienced\",\"value_json\":\"\\\"AI tangible harm event\\\"\"},{\"short_name\":\"Harm Type Experienced\",\"value_json\":\"\\\"physical health/safety\\\"\"},{\"short_name\":\"Notes (Characterizing Entities and the Harm)\",\"value_json\":\"\\\"Tesla Autopilot failed to detected truck and deploy its breaks. This could have minimized damaged during the crash. Ultimately, the Tesla was responsible for the crash\\\"\"}]},{\"attributes\":[{\"short_name\":\"Entity\",\"value_json\":\"\\\"Tesla Model S\\\"\"},{\"short_name\":\"Named Entity\",\"value_json\":\"true\"},{\"short_name\":\"Entity type\",\"value_json\":\"\\\"product\\\"\"},{\"short_name\":\"Entity Relationship to the AI\",\"value_json\":\"[\\\"product containing AI\\\"]\"},{\"short_name\":\"Harm Category Experienced\",\"value_json\":\"\\\"AI tangible harm event\\\"\"},{\"short_name\":\"Harm Type Experienced\",\"value_json\":\"\\\"physical property\\\"\"},{\"short_name\":\"Notes (Characterizing Entities and the Harm)\",\"value_json\":\"\\\"Autopilot failed to deploy breaks which could have minimized damage in the crash\\\"\"}]},{\"attributes\":[{\"short_name\":\"Entity\",\"value_json\":\"\\\"Frank Baressi\\\"\"},{\"short_name\":\"Named Entity\",\"value_json\":\"true\"},{\"short_name\":\"Entity type\",\"value_json\":\"\\\"individual\\\"\"},{\"short_name\":\"Entity Relationship to the AI\",\"value_json\":\"[\\\"user\\\"]\"},{\"short_name\":\"Harm Category Experienced\",\"value_json\":\"\\\"AI tangible harm event\\\"\"},{\"short_name\":\"Harm Type Experienced\",\"value_json\":\"\\\"physical property\\\"\"},{\"short_name\":\"Notes (Characterizing Entities and the Harm)\",\"value_json\":\"\\\"Truck driver hit by Tesla. Damage to truck could have been minimized by the deployment of breaks\\\"\"}]}]" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "Lives Lost", - "value_json": "1" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "Injuries", - "value_json": "-2" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "Estimated Harm Quantities", - "value_json": "true" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "Notes ( Tangible Harm Quantities Information)", - "value_json": "\"\"" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "AI System Description", - "value_json": "\"Tesla Autopilot. \"" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "Data Inputs", - "value_json": "[\"video input\",\"navigation\",\"sensor data\",\"tra\",\"road data\",\"traffic\",\"GPS\"]" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "Sector of Deployment", - "value_json": "[\"transportation and storage\"]" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "Public Sector Deployment", - "value_json": "\"no\"" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "Autonomy Level", - "value_json": "\"Autonomy2\"" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "Notes (Information about AI System)", - "value_json": "\"\"" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "Intentional Harm", - "value_json": "\"No. Not intentionally designed to perform harm\"" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "Physical System Type", - "value_json": "\"Automobile\"" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "AI Task", - "value_json": "[\"navigation\",\"transportation\"]" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "AI tools and methods", - "value_json": "[\"Autonomous Driving\"]" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "Notes (AI Functionality and Techniques)", - "value_json": "\"\"" - } - ], - "incidents": [ - 52 - ], - "namespace": "CSETv1", - "notes": "Annotator 1: \n\n This a note from the annotator 1\n\nAnnotator 2: \n\n This a note from the annotator 2", - "publish": null - } - } -} \ No newline at end of file diff --git a/site/gatsby-site/cypress/fixtures/classifications/upsertDuplicateClassification.json b/site/gatsby-site/cypress/fixtures/classifications/upsertDuplicateClassification.json deleted file mode 100644 index 5a9494c3c5..0000000000 --- a/site/gatsby-site/cypress/fixtures/classifications/upsertDuplicateClassification.json +++ /dev/null @@ -1,325 +0,0 @@ -{ - "data": { - "upsertOneClassification": { - "__typename": "Classification", - "_id": "64ac2b05c70973a5bd3fa61a", - "attributes": [ - { - "__typename": "ClassificationAttribute", - "short_name": "Physical Objects", - "value_json": "\"no\"" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "Entertainment Industry", - "value_json": "\"no\"" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "Report, Test, or Study of data", - "value_json": "\"no\"" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "Deployed", - "value_json": "\"yes\"" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "Producer Test in Controlled Conditions", - "value_json": "\"no\"" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "Producer Test in Operational Conditions", - "value_json": "\"no\"" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "User Test in Controlled Conditions", - "value_json": "\"no\"" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "User Test in Operational Conditions", - "value_json": "\"no\"" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "Harm Domain", - "value_json": "\"yes\"" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "Tangible Harm", - "value_json": "\"tangible harm definitively occurred\"" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "AI System", - "value_json": "\"yes\"" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "Clear link to technology", - "value_json": "\"yes\"" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "There is a potentially identifiable specific entity that experienced the harm", - "value_json": "true" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "AI Harm Level", - "value_json": "\"AI tangible harm event\"" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "Impact on Critical Services", - "value_json": "\"no\"" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "Rights Violation", - "value_json": "\"no\"" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "Involving Minor", - "value_json": "\"no\"" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "Detrimental Content", - "value_json": "\"no\"" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "Protected Characteristic", - 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"value_json": "[]" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "Operating Conditions", - "value_json": "\"\"" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "Notes (Environmental and Temporal Characteristics)", - "value_json": "\"\"" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "Entities", - "value_json": "[{\"attributes\":[{\"short_name\":\"Entity\",\"value_json\":\"\\\"Starbucks\\\"\"},{\"short_name\":\"Named Entity\",\"value_json\":\"true\"},{\"short_name\":\"Entity type\",\"value_json\":\"\\\"for-profit organization\\\"\"},{\"short_name\":\"Entity Relationship to the AI\",\"value_json\":\"[\\\"deployer\\\"]\"},{\"short_name\":\"Harm Category Experienced\",\"value_json\":\"\\\"not applicable\\\"\"},{\"short_name\":\"Harm Type Experienced\",\"value_json\":\"\\\"not applicable\\\"\"}]},{\"attributes\":[{\"short_name\":\"Entity\",\"value_json\":\"\\\"Kronos\\\"\"},{\"short_name\":\"Named Entity\",\"value_json\":\"true\"},{\"short_name\":\"Entity type\",\"value_json\":\"\\\"for-profit organization\\\"\"},{\"short_name\":\"Entity Relationship to the AI\",\"value_json\":\"[\\\"developer\\\"]\"},{\"short_name\":\"Harm Category Experienced\",\"value_json\":\"\\\"not applicable\\\"\"},{\"short_name\":\"Harm Type Experienced\",\"value_json\":\"\\\"not applicable\\\"\"}]},{\"attributes\":[{\"short_name\":\"Entity\",\"value_json\":\"\\\"Kronos scheduling algorithm\\\"\"},{\"short_name\":\"Named Entity\",\"value_json\":\"false\"},{\"short_name\":\"Entity type\",\"value_json\":\"\\\"product\\\"\"},{\"short_name\":\"Entity Relationship to the AI\",\"value_json\":\"[\\\"product containing AI\\\"]\"},{\"short_name\":\"Harm Category Experienced\",\"value_json\":\"\\\"not applicable\\\"\"},{\"short_name\":\"Harm Type Experienced\",\"value_json\":\"\\\"not applicable\\\"\"}]},{\"attributes\":[{\"short_name\":\"Entity\",\"value_json\":\"\\\"Starbucks employees\\\"\"},{\"short_name\":\"Named Entity\",\"value_json\":\"false\"},{\"short_name\":\"Entity type\",\"value_json\":\"\\\"group of individuals\\\"\"},{\"short_name\":\"Notes (Characterizing Entities and the Harm)\",\"value_json\":\"\\\"7.6 - 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Unpredictable schedules cause stress through financial and scheduling instability\\\"\"}]},{\"attributes\":[{\"short_name\":\"Entity\",\"value_json\":\"\\\"Jannette Navarro\\\"\"},{\"short_name\":\"Named Entity\",\"value_json\":\"true\"},{\"short_name\":\"Entity type\",\"value_json\":\"\\\"individual\\\"\"},{\"short_name\":\"Entity Relationship to the AI\",\"value_json\":\"[\\\"affected non-user\\\"]\"},{\"short_name\":\"Harm Category Experienced\",\"value_json\":\"\\\"Other harm not meeting CSET definitions\\\"\"},{\"short_name\":\"Harm Type Experienced\",\"value_json\":\"\\\"other intangible harm\\\"\"},{\"short_name\":\"Notes (Characterizing Entities and the Harm)\",\"value_json\":\"\\\"7.6 - Her unpredictable schedules caused serious stress and instability in her life.\\\"\"}]}]" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "Lives Lost", - "value_json": "0" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "Injuries", - "value_json": "0" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "Estimated Harm Quantities", - "value_json": "false" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "Notes ( Tangible Harm Quantities Information)", - "value_json": "\"\"" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "AI System Description", - "value_json": "\"The Kronos scheduling algorithm is designed to optimize the productivity of stores like Starbucks by scheduling workers inconsistently throughout and across weeks based on predicted store traffic.\"" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "Data Inputs", - "value_json": "[\"schedules\",\"worker profiles\",\"store traffic\"]" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "Notes (Information about AI System)", - "value_json": "\"\"" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "Physical System Type", - "value_json": "\"\"" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "AI Task", - "value_json": "[\"scheduling\",\"productivity optimization\",\"predict store traffic\"]" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "Notes (AI Functionality and Techniques)", - "value_json": "\"\"" - } - ], - "incidents": [ - { - "__typename": "Incident", - "incident_id": 50 - } - ], - "namespace": "CSETv1", - "notes": "", - "publish": true, - "reports": [] - } - } -} diff --git a/site/gatsby-site/cypress/fixtures/history/incidentHistory.json b/site/gatsby-site/cypress/fixtures/history/incidentHistory.json deleted file mode 100644 index b8c13be934..0000000000 --- a/site/gatsby-site/cypress/fixtures/history/incidentHistory.json +++ /dev/null @@ -1,99 +0,0 @@ -{ - "data": { - "history_incidents": [ - { - "AllegedDeployerOfAISystem": ["youtube", "google"], - "AllegedDeveloperOfAISystem": ["google"], - "AllegedHarmedOrNearlyHarmedParties": ["google"], - "_id": "64cc42271a57ad8fd9b338d0", - "date": "2023-08-02", - "description": "this is the description v2", - "editor_dissimilar_incidents": [], - "editor_notes": "This is the editor notes v3", - "editor_similar_incidents": [], - "editors": ["1", "2"], - "embedding": null, - "epoch_date_modified": 1691107500, - "flagged_dissimilar_incidents": null, - "incident_id": 10, - "modifiedBy": "1", - "nlp_similar_incidents": [], - "reports": [3205, 3206], - "title": "Google Bungles AI Reveal With Incorrect Webb Telescope Facts v2", - "tsne": { - "x": null, - "y": null - } - }, - { - "AllegedDeployerOfAISystem": ["youtube", "google"], - "AllegedDeveloperOfAISystem": ["google"], - "AllegedHarmedOrNearlyHarmedParties": ["google"], - "_id": "64cc40ace0efed3dceee2e90", - "date": "2023-08-02", - "description": "this is the description v2", - "editor_dissimilar_incidents": [], - "editor_notes": "This is the editor notes v3", - "editor_similar_incidents": [], - "editors": ["1", "2"], - "embedding": null, - "epoch_date_modified": 1691107497, - "flagged_dissimilar_incidents": null, - "incident_id": 10, - "modifiedBy": "1", - "nlp_similar_incidents": [], - "reports": [3205], - "title": "Google Bungles AI Reveal With Incorrect Webb Telescope Facts v2", - "tsne": { - "x": null, - "y": null - } - }, - { - "AllegedDeployerOfAISystem": ["youtube"], - "AllegedDeveloperOfAISystem": ["google"], - "AllegedHarmedOrNearlyHarmedParties": ["google"], - "_id": "64cc1d50a8af01b3b524593a", - "date": "2023-08-02", - "description": "this is the description v2", - "editor_dissimilar_incidents": [], - "editor_notes": "This is the editor notes v2", - "editor_similar_incidents": [], - "editors": ["1", "2"], - "embedding": null, - "epoch_date_modified": 1691098445, - "flagged_dissimilar_incidents": null, - "incident_id": 10, - "modifiedBy": "1", - "nlp_similar_incidents": [], - "reports": [3205], - "title": "Google Bungles AI Reveal With Incorrect Webb Telescope Facts v2", - "tsne": { - "x": null, - "y": null - } - }, - { - "AllegedDeployerOfAISystem": ["youtube"], - "AllegedDeveloperOfAISystem": ["google"], - "AllegedHarmedOrNearlyHarmedParties": ["tesla"], - "_id": "64cc1d0c1c1f73dbc022f0b6", - "date": "2023-08-03", - "description": "this is the description", - "editor_dissimilar_incidents": [], - "editor_notes": null, - "editor_similar_incidents": [], - "editors": ["2"], - "embedding": null, - "epoch_date_modified": 1691098359, - "flagged_dissimilar_incidents": null, - "incident_id": 10, - "modifiedBy": "1", - "nlp_similar_incidents": [], - "reports": [3205], - "title": "Google Bungles AI Reveal With Incorrect Webb Telescope Facts", - "tsne": null - } - ] - } -} diff --git a/site/gatsby-site/cypress/fixtures/history/reportHistory.json b/site/gatsby-site/cypress/fixtures/history/reportHistory.json deleted file mode 100644 index 2e05ddc6e2..0000000000 --- a/site/gatsby-site/cypress/fixtures/history/reportHistory.json +++ /dev/null @@ -1,291 +0,0 @@ -{ - "data": { - "history_reports": [ - { - "_id": "64d18f7a1a4fcdeaf137442b", - "authors": ["Pablo Costa v2", "Pablo Costa v3", "Pablo Costa v4"], - "cloudinary_id": "reports/image.cnbcfm.com/api/v1/image/100266733-38cbdbd7a4bcc3cf98120f6574fe94a3afe0fca6.jpg?v=1529461359&w=1920&h=1080", - "date_downloaded": "2023-08-04", - "date_modified": "2023-08-07", - "date_published": "2023-08-04", - "date_submitted": "2023-08-03", - "description": null, - "editor_notes": "editor notes v2", - "embedding": null, - "epoch_date_downloaded": 1691107200, - "epoch_date_modified": 1691455351, - "epoch_date_published": 1691107200, - "epoch_date_submitted": 1691020800, - "flag": null, - "image_url": "https://image.cnbcfm.com/api/v1/image/100266733-38cbdbd7a4bcc3cf98120f6574fe94a3afe0fca6.jpg?v=1529461359&w=1920&h=1080", - "inputs_outputs": ["input 1"], - "is_incident_report": true, - "language": "es", - "modifiedBy": "1", - "plain_text": "\n\nBy Aimee Picchi\n\nSeptember 24, 2015 / 11:41 AM / MoneyWatch\n\nFor Starbucks (SBUX) barista Kylei Weisse, working at the coffee chain helps him secure health insurance and some extra money while he studies at Georgia Perimeter College. What it doesn't provide is the kind of stable schedule that the company promised its workers last year.\n\n\"It's the wild inconsistency\" of the hours that's a problem, Weisse, 32, said. \"We're supposed to get them 10 days in advance, which often happens, but there's no guarantee. If our manager doesn't get it to us on time, we just have to deal with it.\"\n\nThat became a problem recently when Weisse's manager gave him only a few days notice on his work hours, which ended up conflicting with an anatomy and physiology exam at his college. Weisse ended up paying another worker $20 to take his shift so he could take the exam.\n\nThe short notice is especially frustrating because of Starbucks' vow last year to post employees' schedules at least 10 days in advance, as well as the company's insistence that workers provide at least one-month notice when they need to take a day off.\n\nWhat's behind Starbucks price increases? 03:04\n\nWeisse isn't alone in complaining that Starbucks isn't living up to its promises to overhaul its labor practices for its roughly 130,000 baristas. That vow followed an article last year by The New York Times that detailed how workers were struggling to manage childcare and other obligations when the company provided only a few days notice about their schedules.\n\nAbout half of roughly 200 Starbucks baristas in a recent survey said they are still receiving their schedule with less than one week's notice. Others also reported being asked to handle \"clopens,\" split shifts in which employees work a closing shift late into the evening and then an early opening shift the following morning. The company last year promised to end the practice.\n\nOf course, Starbucks isn't alone in using \"just-in-time\" scheduling, with the retail and restaurant industry increasingly turning to software that allows them to change work schedules at the last minute, depending on whether business picks up or slows down. But it is Starbucks that has become a lightning rod on the issue given its vows to improve how it treats employees and its own emphatic claims to valuing workers, whom it labels \"partners.\"\n\n\"Starbucks has the values and wants to do right by their employees,\" said Carrie Gleason, director of the Fair Workweek Initiative at the Center for Popular Democracy, an advocacy group focused on workers'rights, and a co-author of the group's new report on the company's labor practices. \"However, since last year when the company recognized there was a serious problem with the way it scheduled workers and pledged to reform, still so many of the same issues persist.\"\n\nStarbucks didn't respond to requests for comment on the study or on baristas' reports of labor practices that are failing to meet the company's stated goals.\n\nIn an internal memo this week published by Time, Starbucks executive Cliff Burrows wrote that the company couldn't validate the survey, but added that \"the findings suggest, contrary to the expectations we have in place, that some partners are receiving their schedules less than one week in advance and that there is a continuing issue with some partners working a close and then an opening shift the following morning.\" He asks store managers \"to go the extra mile to ensure partners have a consistent schedule.\"\n\nStarbucks ends \"race together\" campaign amid public backlash 00:26\n\nTo be sure, some Starbucks workers are receiving at least 10 days notice on their work hours, with the survey finding that about one-third receive two weeks notice and another 18 percent get their schedules three weeks in advance. But that leaves almost half of workers who only receive one week's notice, making it more difficult from them manage other obligations, such as school, family commitments or other jobs.\n\nClopens remain a problem, as well. About 60 percent of workers who have to handle a clopen receive seven or fewer hours of rest between a closing and an opening shift, the study found.\n\nThat's prompted one former Starbucks employee to start a petition to end the practice of scheduling clopens. Ciara Moran noted in her petition that she sometimes was only able to get four or five hours of sleep on the days she was scheduled for clopens. She said she quit her job because she doubted whether it was possible to get ahead given the demands on workers.\n\nEven if Starbucks stuck with its policy of providing eight hours between shifts, that's not enough time, especially given that many workers in the service sector have long commutes, the study said.\n\nAnother issue singled out by the report is Starbucks' practices on sick time. Since paid time off is only available to workers with at least a year on the job, about 40 percent of employees in the survey said they had dealt with barriers in taking sick days.\n\nIn a perfect world, Weisse said he'd like to receive his schedule either a month or a quarter in advance. Asked if he's upset about the last-minute schedules, Weisse said he had become resigned to it.\n\n\"I don't know if you've ever seen experiments with rats,\" he said. \"First they get upset if you treat them badly. Then they lay down and try not to move too much. I'm like that now. Things are terrible.\"\n\nV2\n", - "report_number": 3206, - "submitters": ["Test User v2"], - "tags": [], - "text": "[](/moneywatch)\n\nBy Aimee Picchi\n\nSeptember 24, 2015 / 11:41 AM / MoneyWatch\n\nFor Starbucks ([SBUX](http://markets.cbsnews.com/SBUX/quote/)) barista Kylei Weisse, working at the coffee chain helps him secure health insurance and some extra money while he studies at Georgia Perimeter College. What it doesn't provide is the kind of stable schedule that the [company promised its workers](https://www.cbsnews.com/news/for-some-starbucks-workers-job-leaves-bitter-taste/) last year.\n\n\"It's the wild inconsistency\" of the hours that's a problem, Weisse, 32, said. \"We're supposed to get them 10 days in advance, which often happens, but there's no guarantee. If our manager doesn't get it to us on time, we just have to deal with it.\"\n\nThat became a problem recently when Weisse's manager gave him only a few days notice on his work hours, which ended up conflicting with an anatomy and physiology exam at his college. Weisse ended up paying another worker $20 to take his shift so he could take the exam.\n\nThe short notice is especially frustrating because of Starbucks' vow last year to post employees' schedules at least 10 days in advance, as well as the company's insistence that workers provide at least one-month notice when they need to take a day off.\n\n[What's behind Starbucks price increases? 03:04](https://www.cbsnews.com/video/whats-behind-starbucks-price-increases/)\n\nWeisse isn't alone in complaining that Starbucks isn't living up to its promises to overhaul its labor practices for its roughly 130,000 baristas. That vow followed an [article last year by The New York Times](http://www.nytimes.com/interactive/2014/08/13/us/starbucks-workers-scheduling-hours.html?_r=1) that detailed how workers were struggling to manage childcare and other obligations when the company provided only a few days notice about their schedules.\n\nAbout half of roughly 200 Starbucks baristas [in a recent survey](http://static1.squarespace.com/static/556496efe4b02c9d26fdf26a/t/56027485e4b02ead4f16bc2a/1443001477305/The+Grind.pdf) said they are still receiving their schedule with less than one week's notice. Others also reported being asked to handle \"clopens,\" split shifts in which employees work a closing shift late into the evening and then an early opening shift the following morning. The company last year promised to end the practice.\n\nOf course, Starbucks isn't alone in using [\"just-in-time\" scheduling](https://www.cbsnews.com/news/in-retail-life-on-the-job-often-leads-to-a-dead-end/), with the retail and restaurant industry increasingly turning to software that allows them to change work schedules at the last minute, depending on whether business picks up or slows down. But it is Starbucks that has become a lightning rod on the issue given its vows to improve how it treats employees and its own [emphatic claims](http://www.starbucks.com/careers/working-at-starbucks) to valuing workers, whom it labels \"partners.\"\n\n\"Starbucks has the values and wants to do right by their employees,\" said Carrie Gleason, director of the Fair Workweek Initiative at the Center for Popular Democracy, an advocacy group focused on workers'rights, and a co-author of the group's new [report on the company's labor practices](http://populardemocracy.org/sites/default/files/The%2BGrind.pdf). \"However, since last year when the company recognized there was a serious problem with the way it scheduled workers and pledged to reform, still so many of the same issues persist.\"\n\nStarbucks didn't respond to requests for comment on the study or on baristas' reports of labor practices that are failing to meet the company's stated goals.\n\nIn an internal memo this week [published by Time](http://time.com/4047359/starbucks-scheduling-labor-practices-memo/), Starbucks executive Cliff Burrows wrote that the company couldn't validate the survey, but added that \"the findings suggest, contrary to the expectations we have in place, that some partners are receiving their schedules less than one week in advance and that there is a continuing issue with some partners working a close and then an opening shift the following morning.\" He asks store managers \"to go the extra mile to ensure partners have a consistent schedule.\"\n\n[Starbucks ends \"race together\" campaign amid public backlash 00:26](https://www.cbsnews.com/video/starbucks-ends-race-together-campaign-amid-public-backlash/)\n\nTo be sure, some Starbucks workers are receiving at least 10 days notice on their work hours, with the survey finding that about one-third receive two weeks notice and another 18 percent get their schedules three weeks in advance. But that leaves almost half of workers who only receive one week's notice, making it more difficult from them manage other obligations, such as school, family commitments or other jobs.\n\nClopens remain a problem, as well. About 60 percent of workers who have to handle a clopen receive seven or fewer hours of rest between a closing and an opening shift, the study found.\n\nThat's prompted one former Starbucks employee to start a petition to end the practice of scheduling clopens. Ciara Moran noted in her petition that she sometimes was only able to get [four or five hours of sleep](https://www.coworker.org/petitions/starbucks-end-clopens-now) on the days she was scheduled for clopens. She said she quit her job because she doubted whether it was possible to get ahead given the demands on workers.\n\nEven if Starbucks stuck with its policy of providing eight hours between shifts, that's not enough time, especially given that many workers in the service sector have long commutes, the study said.\n\nAnother issue singled out by the report is Starbucks' practices on sick time. Since paid time off is only available to workers with at least a year on the job, about 40 percent of employees in the survey said they had dealt with barriers in taking sick days.\n\nIn a perfect world, Weisse said he'd like to receive his schedule either a month or a quarter in advance. Asked if he's upset about the last-minute schedules, Weisse said he had become resigned to it.\n\n\"I don't know if you've ever seen experiments with rats,\" he said. \"First they get upset if you treat them badly. Then they lay down and try not to move too much. I'm like that now. Things are terrible.\"\n\nV2", - "title": "​Is Starbucks shortchanging its baristas? v2", - "url": "https://www.cbsnews.com/news/is-starbucks-shortchanging-its-baristas", - "source_domain": "cbsnews.com", - "user": "1" - }, - { - "_id": "64d18f66f549de65c4e7841e", - "authors": ["Pablo Costa v2", "Pablo Costa v3", "Pablo Costa v4"], - "cloudinary_id": "reports/image.cnbcfm.com/api/v1/image/100266733-38cbdbd7a4bcc3cf98120f6574fe94a3afe0fca6.jpg?v=1529461359&w=1920&h=1080", - "date_downloaded": "2023-08-04", - "date_modified": "2023-08-07", - "date_published": "2023-08-04", - "date_submitted": "2023-08-03", - "description": null, - "editor_notes": "editor notes v2", - "embedding": null, - "epoch_date_downloaded": 1691107200, - "epoch_date_modified": 1691455331, - "epoch_date_published": 1691107200, - "epoch_date_submitted": 1691020800, - "flag": null, - "image_url": "https://image.cnbcfm.com/api/v1/image/100266733-38cbdbd7a4bcc3cf98120f6574fe94a3afe0fca6.jpg?v=1529461359&w=1920&h=1080", - "inputs_outputs": ["input 1"], - "is_incident_report": true, - "language": "es", - "modifiedBy": "1", - "plain_text": "\n\nBy Aimee Picchi\n\nSeptember 24, 2015 / 11:41 AM / MoneyWatch\n\nFor Starbucks (SBUX) barista Kylei Weisse, working at the coffee chain helps him secure health insurance and some extra money while he studies at Georgia Perimeter College. What it doesn't provide is the kind of stable schedule that the company promised its workers last year.\n\n\"It's the wild inconsistency\" of the hours that's a problem, Weisse, 32, said. \"We're supposed to get them 10 days in advance, which often happens, but there's no guarantee. If our manager doesn't get it to us on time, we just have to deal with it.\"\n\nThat became a problem recently when Weisse's manager gave him only a few days notice on his work hours, which ended up conflicting with an anatomy and physiology exam at his college. Weisse ended up paying another worker $20 to take his shift so he could take the exam.\n\nThe short notice is especially frustrating because of Starbucks' vow last year to post employees' schedules at least 10 days in advance, as well as the company's insistence that workers provide at least one-month notice when they need to take a day off.\n\nWhat's behind Starbucks price increases? 03:04\n\nWeisse isn't alone in complaining that Starbucks isn't living up to its promises to overhaul its labor practices for its roughly 130,000 baristas. That vow followed an article last year by The New York Times that detailed how workers were struggling to manage childcare and other obligations when the company provided only a few days notice about their schedules.\n\nAbout half of roughly 200 Starbucks baristas in a recent survey said they are still receiving their schedule with less than one week's notice. Others also reported being asked to handle \"clopens,\" split shifts in which employees work a closing shift late into the evening and then an early opening shift the following morning. The company last year promised to end the practice.\n\nOf course, Starbucks isn't alone in using \"just-in-time\" scheduling, with the retail and restaurant industry increasingly turning to software that allows them to change work schedules at the last minute, depending on whether business picks up or slows down. But it is Starbucks that has become a lightning rod on the issue given its vows to improve how it treats employees and its own emphatic claims to valuing workers, whom it labels \"partners.\"\n\n\"Starbucks has the values and wants to do right by their employees,\" said Carrie Gleason, director of the Fair Workweek Initiative at the Center for Popular Democracy, an advocacy group focused on workers'rights, and a co-author of the group's new report on the company's labor practices. \"However, since last year when the company recognized there was a serious problem with the way it scheduled workers and pledged to reform, still so many of the same issues persist.\"\n\nStarbucks didn't respond to requests for comment on the study or on baristas' reports of labor practices that are failing to meet the company's stated goals.\n\nIn an internal memo this week published by Time, Starbucks executive Cliff Burrows wrote that the company couldn't validate the survey, but added that \"the findings suggest, contrary to the expectations we have in place, that some partners are receiving their schedules less than one week in advance and that there is a continuing issue with some partners working a close and then an opening shift the following morning.\" He asks store managers \"to go the extra mile to ensure partners have a consistent schedule.\"\n\nStarbucks ends \"race together\" campaign amid public backlash 00:26\n\nTo be sure, some Starbucks workers are receiving at least 10 days notice on their work hours, with the survey finding that about one-third receive two weeks notice and another 18 percent get their schedules three weeks in advance. But that leaves almost half of workers who only receive one week's notice, making it more difficult from them manage other obligations, such as school, family commitments or other jobs.\n\nClopens remain a problem, as well. About 60 percent of workers who have to handle a clopen receive seven or fewer hours of rest between a closing and an opening shift, the study found.\n\nThat's prompted one former Starbucks employee to start a petition to end the practice of scheduling clopens. Ciara Moran noted in her petition that she sometimes was only able to get four or five hours of sleep on the days she was scheduled for clopens. She said she quit her job because she doubted whether it was possible to get ahead given the demands on workers.\n\nEven if Starbucks stuck with its policy of providing eight hours between shifts, that's not enough time, especially given that many workers in the service sector have long commutes, the study said.\n\nAnother issue singled out by the report is Starbucks' practices on sick time. Since paid time off is only available to workers with at least a year on the job, about 40 percent of employees in the survey said they had dealt with barriers in taking sick days.\n\nIn a perfect world, Weisse said he'd like to receive his schedule either a month or a quarter in advance. Asked if he's upset about the last-minute schedules, Weisse said he had become resigned to it.\n\n\"I don't know if you've ever seen experiments with rats,\" he said. \"First they get upset if you treat them badly. Then they lay down and try not to move too much. I'm like that now. Things are terrible.\"\n\nV2\n", - "report_number": 3206, - "submitters": ["Test User v2"], - "tags": ["response"], - "text": "[](/moneywatch)\n\nBy Aimee Picchi\n\nSeptember 24, 2015 / 11:41 AM / MoneyWatch\n\nFor Starbucks ([SBUX](http://markets.cbsnews.com/SBUX/quote/)) barista Kylei Weisse, working at the coffee chain helps him secure health insurance and some extra money while he studies at Georgia Perimeter College. What it doesn't provide is the kind of stable schedule that the [company promised its workers](https://www.cbsnews.com/news/for-some-starbucks-workers-job-leaves-bitter-taste/) last year.\n\n\"It's the wild inconsistency\" of the hours that's a problem, Weisse, 32, said. \"We're supposed to get them 10 days in advance, which often happens, but there's no guarantee. If our manager doesn't get it to us on time, we just have to deal with it.\"\n\nThat became a problem recently when Weisse's manager gave him only a few days notice on his work hours, which ended up conflicting with an anatomy and physiology exam at his college. Weisse ended up paying another worker $20 to take his shift so he could take the exam.\n\nThe short notice is especially frustrating because of Starbucks' vow last year to post employees' schedules at least 10 days in advance, as well as the company's insistence that workers provide at least one-month notice when they need to take a day off.\n\n[What's behind Starbucks price increases? 03:04](https://www.cbsnews.com/video/whats-behind-starbucks-price-increases/)\n\nWeisse isn't alone in complaining that Starbucks isn't living up to its promises to overhaul its labor practices for its roughly 130,000 baristas. That vow followed an [article last year by The New York Times](http://www.nytimes.com/interactive/2014/08/13/us/starbucks-workers-scheduling-hours.html?_r=1) that detailed how workers were struggling to manage childcare and other obligations when the company provided only a few days notice about their schedules.\n\nAbout half of roughly 200 Starbucks baristas [in a recent survey](http://static1.squarespace.com/static/556496efe4b02c9d26fdf26a/t/56027485e4b02ead4f16bc2a/1443001477305/The+Grind.pdf) said they are still receiving their schedule with less than one week's notice. Others also reported being asked to handle \"clopens,\" split shifts in which employees work a closing shift late into the evening and then an early opening shift the following morning. The company last year promised to end the practice.\n\nOf course, Starbucks isn't alone in using [\"just-in-time\" scheduling](https://www.cbsnews.com/news/in-retail-life-on-the-job-often-leads-to-a-dead-end/), with the retail and restaurant industry increasingly turning to software that allows them to change work schedules at the last minute, depending on whether business picks up or slows down. But it is Starbucks that has become a lightning rod on the issue given its vows to improve how it treats employees and its own [emphatic claims](http://www.starbucks.com/careers/working-at-starbucks) to valuing workers, whom it labels \"partners.\"\n\n\"Starbucks has the values and wants to do right by their employees,\" said Carrie Gleason, director of the Fair Workweek Initiative at the Center for Popular Democracy, an advocacy group focused on workers'rights, and a co-author of the group's new [report on the company's labor practices](http://populardemocracy.org/sites/default/files/The%2BGrind.pdf). \"However, since last year when the company recognized there was a serious problem with the way it scheduled workers and pledged to reform, still so many of the same issues persist.\"\n\nStarbucks didn't respond to requests for comment on the study or on baristas' reports of labor practices that are failing to meet the company's stated goals.\n\nIn an internal memo this week [published by Time](http://time.com/4047359/starbucks-scheduling-labor-practices-memo/), Starbucks executive Cliff Burrows wrote that the company couldn't validate the survey, but added that \"the findings suggest, contrary to the expectations we have in place, that some partners are receiving their schedules less than one week in advance and that there is a continuing issue with some partners working a close and then an opening shift the following morning.\" He asks store managers \"to go the extra mile to ensure partners have a consistent schedule.\"\n\n[Starbucks ends \"race together\" campaign amid public backlash 00:26](https://www.cbsnews.com/video/starbucks-ends-race-together-campaign-amid-public-backlash/)\n\nTo be sure, some Starbucks workers are receiving at least 10 days notice on their work hours, with the survey finding that about one-third receive two weeks notice and another 18 percent get their schedules three weeks in advance. But that leaves almost half of workers who only receive one week's notice, making it more difficult from them manage other obligations, such as school, family commitments or other jobs.\n\nClopens remain a problem, as well. About 60 percent of workers who have to handle a clopen receive seven or fewer hours of rest between a closing and an opening shift, the study found.\n\nThat's prompted one former Starbucks employee to start a petition to end the practice of scheduling clopens. Ciara Moran noted in her petition that she sometimes was only able to get [four or five hours of sleep](https://www.coworker.org/petitions/starbucks-end-clopens-now) on the days she was scheduled for clopens. She said she quit her job because she doubted whether it was possible to get ahead given the demands on workers.\n\nEven if Starbucks stuck with its policy of providing eight hours between shifts, that's not enough time, especially given that many workers in the service sector have long commutes, the study said.\n\nAnother issue singled out by the report is Starbucks' practices on sick time. Since paid time off is only available to workers with at least a year on the job, about 40 percent of employees in the survey said they had dealt with barriers in taking sick days.\n\nIn a perfect world, Weisse said he'd like to receive his schedule either a month or a quarter in advance. Asked if he's upset about the last-minute schedules, Weisse said he had become resigned to it.\n\n\"I don't know if you've ever seen experiments with rats,\" he said. \"First they get upset if you treat them badly. Then they lay down and try not to move too much. I'm like that now. Things are terrible.\"\n\nV2", - "title": "​Is Starbucks shortchanging its baristas? v2", - "url": "https://www.cbsnews.com/news/is-starbucks-shortchanging-its-baristas", - "source_domain": "cbsnews.com", - "user": "1" - }, - { - "_id": "64d18f471bae3610af2ab1bc", - "authors": ["Pablo Costa v2", "Pablo Costa v3", "Pablo Costa v4"], - "cloudinary_id": "reports/image.cnbcfm.com/api/v1/image/100266733-38cbdbd7a4bcc3cf98120f6574fe94a3afe0fca6.jpg?v=1529461359&w=1920&h=1080", - "date_downloaded": "2023-08-04", - "date_modified": "2023-08-07", - "date_published": "2023-08-04", - "date_submitted": "2023-08-03", - "description": null, - "editor_notes": "editor notes v2", - "embedding": null, - "epoch_date_downloaded": 1691107200, - "epoch_date_modified": 1691455300, - "epoch_date_published": 1691107200, - "epoch_date_submitted": 1691020800, - "flag": null, - "image_url": "https://image.cnbcfm.com/api/v1/image/100266733-38cbdbd7a4bcc3cf98120f6574fe94a3afe0fca6.jpg?v=1529461359&w=1920&h=1080", - "inputs_outputs": ["input 1"], - "is_incident_report": true, - "language": "es", - "modifiedBy": "1", - "plain_text": "\n\nBy Aimee Picchi\n\nSeptember 24, 2015 / 11:41 AM / MoneyWatch\n\nFor Starbucks (SBUX) barista Kylei Weisse, working at the coffee chain helps him secure health insurance and some extra money while he studies at Georgia Perimeter College. What it doesn't provide is the kind of stable schedule that the company promised its workers last year.\n\n\"It's the wild inconsistency\" of the hours that's a problem, Weisse, 32, said. \"We're supposed to get them 10 days in advance, which often happens, but there's no guarantee. If our manager doesn't get it to us on time, we just have to deal with it.\"\n\nThat became a problem recently when Weisse's manager gave him only a few days notice on his work hours, which ended up conflicting with an anatomy and physiology exam at his college. Weisse ended up paying another worker $20 to take his shift so he could take the exam.\n\nThe short notice is especially frustrating because of Starbucks' vow last year to post employees' schedules at least 10 days in advance, as well as the company's insistence that workers provide at least one-month notice when they need to take a day off.\n\nWhat's behind Starbucks price increases? 03:04\n\nWeisse isn't alone in complaining that Starbucks isn't living up to its promises to overhaul its labor practices for its roughly 130,000 baristas. That vow followed an article last year by The New York Times that detailed how workers were struggling to manage childcare and other obligations when the company provided only a few days notice about their schedules.\n\nAbout half of roughly 200 Starbucks baristas in a recent survey said they are still receiving their schedule with less than one week's notice. Others also reported being asked to handle \"clopens,\" split shifts in which employees work a closing shift late into the evening and then an early opening shift the following morning. The company last year promised to end the practice.\n\nOf course, Starbucks isn't alone in using \"just-in-time\" scheduling, with the retail and restaurant industry increasingly turning to software that allows them to change work schedules at the last minute, depending on whether business picks up or slows down. But it is Starbucks that has become a lightning rod on the issue given its vows to improve how it treats employees and its own emphatic claims to valuing workers, whom it labels \"partners.\"\n\n\"Starbucks has the values and wants to do right by their employees,\" said Carrie Gleason, director of the Fair Workweek Initiative at the Center for Popular Democracy, an advocacy group focused on workers'rights, and a co-author of the group's new report on the company's labor practices. \"However, since last year when the company recognized there was a serious problem with the way it scheduled workers and pledged to reform, still so many of the same issues persist.\"\n\nStarbucks didn't respond to requests for comment on the study or on baristas' reports of labor practices that are failing to meet the company's stated goals.\n\nIn an internal memo this week published by Time, Starbucks executive Cliff Burrows wrote that the company couldn't validate the survey, but added that \"the findings suggest, contrary to the expectations we have in place, that some partners are receiving their schedules less than one week in advance and that there is a continuing issue with some partners working a close and then an opening shift the following morning.\" He asks store managers \"to go the extra mile to ensure partners have a consistent schedule.\"\n\nStarbucks ends \"race together\" campaign amid public backlash 00:26\n\nTo be sure, some Starbucks workers are receiving at least 10 days notice on their work hours, with the survey finding that about one-third receive two weeks notice and another 18 percent get their schedules three weeks in advance. But that leaves almost half of workers who only receive one week's notice, making it more difficult from them manage other obligations, such as school, family commitments or other jobs.\n\nClopens remain a problem, as well. About 60 percent of workers who have to handle a clopen receive seven or fewer hours of rest between a closing and an opening shift, the study found.\n\nThat's prompted one former Starbucks employee to start a petition to end the practice of scheduling clopens. Ciara Moran noted in her petition that she sometimes was only able to get four or five hours of sleep on the days she was scheduled for clopens. She said she quit her job because she doubted whether it was possible to get ahead given the demands on workers.\n\nEven if Starbucks stuck with its policy of providing eight hours between shifts, that's not enough time, especially given that many workers in the service sector have long commutes, the study said.\n\nAnother issue singled out by the report is Starbucks' practices on sick time. Since paid time off is only available to workers with at least a year on the job, about 40 percent of employees in the survey said they had dealt with barriers in taking sick days.\n\nIn a perfect world, Weisse said he'd like to receive his schedule either a month or a quarter in advance. Asked if he's upset about the last-minute schedules, Weisse said he had become resigned to it.\n\n\"I don't know if you've ever seen experiments with rats,\" he said. \"First they get upset if you treat them badly. Then they lay down and try not to move too much. I'm like that now. Things are terrible.\"\n\nV2\n", - "report_number": 3206, - "submitters": ["Test User v2"], - "tags": [], - "text": "[](/moneywatch)\n\nBy Aimee Picchi\n\nSeptember 24, 2015 / 11:41 AM / MoneyWatch\n\nFor Starbucks ([SBUX](http://markets.cbsnews.com/SBUX/quote/)) barista Kylei Weisse, working at the coffee chain helps him secure health insurance and some extra money while he studies at Georgia Perimeter College. What it doesn't provide is the kind of stable schedule that the [company promised its workers](https://www.cbsnews.com/news/for-some-starbucks-workers-job-leaves-bitter-taste/) last year.\n\n\"It's the wild inconsistency\" of the hours that's a problem, Weisse, 32, said. \"We're supposed to get them 10 days in advance, which often happens, but there's no guarantee. If our manager doesn't get it to us on time, we just have to deal with it.\"\n\nThat became a problem recently when Weisse's manager gave him only a few days notice on his work hours, which ended up conflicting with an anatomy and physiology exam at his college. Weisse ended up paying another worker $20 to take his shift so he could take the exam.\n\nThe short notice is especially frustrating because of Starbucks' vow last year to post employees' schedules at least 10 days in advance, as well as the company's insistence that workers provide at least one-month notice when they need to take a day off.\n\n[What's behind Starbucks price increases? 03:04](https://www.cbsnews.com/video/whats-behind-starbucks-price-increases/)\n\nWeisse isn't alone in complaining that Starbucks isn't living up to its promises to overhaul its labor practices for its roughly 130,000 baristas. That vow followed an [article last year by The New York Times](http://www.nytimes.com/interactive/2014/08/13/us/starbucks-workers-scheduling-hours.html?_r=1) that detailed how workers were struggling to manage childcare and other obligations when the company provided only a few days notice about their schedules.\n\nAbout half of roughly 200 Starbucks baristas [in a recent survey](http://static1.squarespace.com/static/556496efe4b02c9d26fdf26a/t/56027485e4b02ead4f16bc2a/1443001477305/The+Grind.pdf) said they are still receiving their schedule with less than one week's notice. Others also reported being asked to handle \"clopens,\" split shifts in which employees work a closing shift late into the evening and then an early opening shift the following morning. The company last year promised to end the practice.\n\nOf course, Starbucks isn't alone in using [\"just-in-time\" scheduling](https://www.cbsnews.com/news/in-retail-life-on-the-job-often-leads-to-a-dead-end/), with the retail and restaurant industry increasingly turning to software that allows them to change work schedules at the last minute, depending on whether business picks up or slows down. But it is Starbucks that has become a lightning rod on the issue given its vows to improve how it treats employees and its own [emphatic claims](http://www.starbucks.com/careers/working-at-starbucks) to valuing workers, whom it labels \"partners.\"\n\n\"Starbucks has the values and wants to do right by their employees,\" said Carrie Gleason, director of the Fair Workweek Initiative at the Center for Popular Democracy, an advocacy group focused on workers'rights, and a co-author of the group's new [report on the company's labor practices](http://populardemocracy.org/sites/default/files/The%2BGrind.pdf). \"However, since last year when the company recognized there was a serious problem with the way it scheduled workers and pledged to reform, still so many of the same issues persist.\"\n\nStarbucks didn't respond to requests for comment on the study or on baristas' reports of labor practices that are failing to meet the company's stated goals.\n\nIn an internal memo this week [published by Time](http://time.com/4047359/starbucks-scheduling-labor-practices-memo/), Starbucks executive Cliff Burrows wrote that the company couldn't validate the survey, but added that \"the findings suggest, contrary to the expectations we have in place, that some partners are receiving their schedules less than one week in advance and that there is a continuing issue with some partners working a close and then an opening shift the following morning.\" He asks store managers \"to go the extra mile to ensure partners have a consistent schedule.\"\n\n[Starbucks ends \"race together\" campaign amid public backlash 00:26](https://www.cbsnews.com/video/starbucks-ends-race-together-campaign-amid-public-backlash/)\n\nTo be sure, some Starbucks workers are receiving at least 10 days notice on their work hours, with the survey finding that about one-third receive two weeks notice and another 18 percent get their schedules three weeks in advance. But that leaves almost half of workers who only receive one week's notice, making it more difficult from them manage other obligations, such as school, family commitments or other jobs.\n\nClopens remain a problem, as well. About 60 percent of workers who have to handle a clopen receive seven or fewer hours of rest between a closing and an opening shift, the study found.\n\nThat's prompted one former Starbucks employee to start a petition to end the practice of scheduling clopens. Ciara Moran noted in her petition that she sometimes was only able to get [four or five hours of sleep](https://www.coworker.org/petitions/starbucks-end-clopens-now) on the days she was scheduled for clopens. She said she quit her job because she doubted whether it was possible to get ahead given the demands on workers.\n\nEven if Starbucks stuck with its policy of providing eight hours between shifts, that's not enough time, especially given that many workers in the service sector have long commutes, the study said.\n\nAnother issue singled out by the report is Starbucks' practices on sick time. Since paid time off is only available to workers with at least a year on the job, about 40 percent of employees in the survey said they had dealt with barriers in taking sick days.\n\nIn a perfect world, Weisse said he'd like to receive his schedule either a month or a quarter in advance. Asked if he's upset about the last-minute schedules, Weisse said he had become resigned to it.\n\n\"I don't know if you've ever seen experiments with rats,\" he said. \"First they get upset if you treat them badly. Then they lay down and try not to move too much. I'm like that now. Things are terrible.\"\n\nV2", - "title": "​Is Starbucks shortchanging its baristas? v2", - "url": "https://www.cbsnews.com/news/is-starbucks-shortchanging-its-baristas", - "source_domain": "cbsnews.com", - "user": "1" - }, - { - "_id": "64d18f2f1a4fcdeaf1372bf4", - "authors": ["Pablo Costa v2", "Pablo Costa v3", "Pablo Costa v4"], - "cloudinary_id": "reports/image.cnbcfm.com/api/v1/image/100266733-38cbdbd7a4bcc3cf98120f6574fe94a3afe0fca6.jpg?v=1529461359&w=1920&h=1080", - "date_downloaded": "2023-08-04", - "date_modified": "2023-08-07", - "date_published": "2023-08-04", - "date_submitted": "2023-08-03", - "description": null, - "editor_notes": "editor notes v2", - "embedding": null, - "epoch_date_downloaded": 1691107200, - "epoch_date_modified": 1691455276, - "epoch_date_published": 1691107200, - "epoch_date_submitted": 1691020800, - "flag": null, - "image_url": "https://image.cnbcfm.com/api/v1/image/100266733-38cbdbd7a4bcc3cf98120f6574fe94a3afe0fca6.jpg?v=1529461359&w=1920&h=1080", - "inputs_outputs": ["input 1", "output 1", "input 2", "output 2"], - "is_incident_report": true, - "language": "es", - "modifiedBy": "1", - "plain_text": "\n\nBy Aimee Picchi\n\nSeptember 24, 2015 / 11:41 AM / MoneyWatch\n\nFor Starbucks (SBUX) barista Kylei Weisse, working at the coffee chain helps him secure health insurance and some extra money while he studies at Georgia Perimeter College. What it doesn't provide is the kind of stable schedule that the company promised its workers last year.\n\n\"It's the wild inconsistency\" of the hours that's a problem, Weisse, 32, said. \"We're supposed to get them 10 days in advance, which often happens, but there's no guarantee. If our manager doesn't get it to us on time, we just have to deal with it.\"\n\nThat became a problem recently when Weisse's manager gave him only a few days notice on his work hours, which ended up conflicting with an anatomy and physiology exam at his college. Weisse ended up paying another worker $20 to take his shift so he could take the exam.\n\nThe short notice is especially frustrating because of Starbucks' vow last year to post employees' schedules at least 10 days in advance, as well as the company's insistence that workers provide at least one-month notice when they need to take a day off.\n\nWhat's behind Starbucks price increases? 03:04\n\nWeisse isn't alone in complaining that Starbucks isn't living up to its promises to overhaul its labor practices for its roughly 130,000 baristas. That vow followed an article last year by The New York Times that detailed how workers were struggling to manage childcare and other obligations when the company provided only a few days notice about their schedules.\n\nAbout half of roughly 200 Starbucks baristas in a recent survey said they are still receiving their schedule with less than one week's notice. Others also reported being asked to handle \"clopens,\" split shifts in which employees work a closing shift late into the evening and then an early opening shift the following morning. The company last year promised to end the practice.\n\nOf course, Starbucks isn't alone in using \"just-in-time\" scheduling, with the retail and restaurant industry increasingly turning to software that allows them to change work schedules at the last minute, depending on whether business picks up or slows down. But it is Starbucks that has become a lightning rod on the issue given its vows to improve how it treats employees and its own emphatic claims to valuing workers, whom it labels \"partners.\"\n\n\"Starbucks has the values and wants to do right by their employees,\" said Carrie Gleason, director of the Fair Workweek Initiative at the Center for Popular Democracy, an advocacy group focused on workers'rights, and a co-author of the group's new report on the company's labor practices. \"However, since last year when the company recognized there was a serious problem with the way it scheduled workers and pledged to reform, still so many of the same issues persist.\"\n\nStarbucks didn't respond to requests for comment on the study or on baristas' reports of labor practices that are failing to meet the company's stated goals.\n\nIn an internal memo this week published by Time, Starbucks executive Cliff Burrows wrote that the company couldn't validate the survey, but added that \"the findings suggest, contrary to the expectations we have in place, that some partners are receiving their schedules less than one week in advance and that there is a continuing issue with some partners working a close and then an opening shift the following morning.\" He asks store managers \"to go the extra mile to ensure partners have a consistent schedule.\"\n\nStarbucks ends \"race together\" campaign amid public backlash 00:26\n\nTo be sure, some Starbucks workers are receiving at least 10 days notice on their work hours, with the survey finding that about one-third receive two weeks notice and another 18 percent get their schedules three weeks in advance. But that leaves almost half of workers who only receive one week's notice, making it more difficult from them manage other obligations, such as school, family commitments or other jobs.\n\nClopens remain a problem, as well. About 60 percent of workers who have to handle a clopen receive seven or fewer hours of rest between a closing and an opening shift, the study found.\n\nThat's prompted one former Starbucks employee to start a petition to end the practice of scheduling clopens. Ciara Moran noted in her petition that she sometimes was only able to get four or five hours of sleep on the days she was scheduled for clopens. She said she quit her job because she doubted whether it was possible to get ahead given the demands on workers.\n\nEven if Starbucks stuck with its policy of providing eight hours between shifts, that's not enough time, especially given that many workers in the service sector have long commutes, the study said.\n\nAnother issue singled out by the report is Starbucks' practices on sick time. Since paid time off is only available to workers with at least a year on the job, about 40 percent of employees in the survey said they had dealt with barriers in taking sick days.\n\nIn a perfect world, Weisse said he'd like to receive his schedule either a month or a quarter in advance. Asked if he's upset about the last-minute schedules, Weisse said he had become resigned to it.\n\n\"I don't know if you've ever seen experiments with rats,\" he said. \"First they get upset if you treat them badly. Then they lay down and try not to move too much. I'm like that now. Things are terrible.\"\n\nV2\n", - "report_number": 3206, - "submitters": ["Test User v2"], - "tags": [], - "text": "[](/moneywatch)\n\nBy Aimee Picchi\n\nSeptember 24, 2015 / 11:41 AM / MoneyWatch\n\nFor Starbucks ([SBUX](http://markets.cbsnews.com/SBUX/quote/)) barista Kylei Weisse, working at the coffee chain helps him secure health insurance and some extra money while he studies at Georgia Perimeter College. What it doesn't provide is the kind of stable schedule that the [company promised its workers](https://www.cbsnews.com/news/for-some-starbucks-workers-job-leaves-bitter-taste/) last year.\n\n\"It's the wild inconsistency\" of the hours that's a problem, Weisse, 32, said. \"We're supposed to get them 10 days in advance, which often happens, but there's no guarantee. If our manager doesn't get it to us on time, we just have to deal with it.\"\n\nThat became a problem recently when Weisse's manager gave him only a few days notice on his work hours, which ended up conflicting with an anatomy and physiology exam at his college. Weisse ended up paying another worker $20 to take his shift so he could take the exam.\n\nThe short notice is especially frustrating because of Starbucks' vow last year to post employees' schedules at least 10 days in advance, as well as the company's insistence that workers provide at least one-month notice when they need to take a day off.\n\n[What's behind Starbucks price increases? 03:04](https://www.cbsnews.com/video/whats-behind-starbucks-price-increases/)\n\nWeisse isn't alone in complaining that Starbucks isn't living up to its promises to overhaul its labor practices for its roughly 130,000 baristas. That vow followed an [article last year by The New York Times](http://www.nytimes.com/interactive/2014/08/13/us/starbucks-workers-scheduling-hours.html?_r=1) that detailed how workers were struggling to manage childcare and other obligations when the company provided only a few days notice about their schedules.\n\nAbout half of roughly 200 Starbucks baristas [in a recent survey](http://static1.squarespace.com/static/556496efe4b02c9d26fdf26a/t/56027485e4b02ead4f16bc2a/1443001477305/The+Grind.pdf) said they are still receiving their schedule with less than one week's notice. Others also reported being asked to handle \"clopens,\" split shifts in which employees work a closing shift late into the evening and then an early opening shift the following morning. The company last year promised to end the practice.\n\nOf course, Starbucks isn't alone in using [\"just-in-time\" scheduling](https://www.cbsnews.com/news/in-retail-life-on-the-job-often-leads-to-a-dead-end/), with the retail and restaurant industry increasingly turning to software that allows them to change work schedules at the last minute, depending on whether business picks up or slows down. But it is Starbucks that has become a lightning rod on the issue given its vows to improve how it treats employees and its own [emphatic claims](http://www.starbucks.com/careers/working-at-starbucks) to valuing workers, whom it labels \"partners.\"\n\n\"Starbucks has the values and wants to do right by their employees,\" said Carrie Gleason, director of the Fair Workweek Initiative at the Center for Popular Democracy, an advocacy group focused on workers'rights, and a co-author of the group's new [report on the company's labor practices](http://populardemocracy.org/sites/default/files/The%2BGrind.pdf). \"However, since last year when the company recognized there was a serious problem with the way it scheduled workers and pledged to reform, still so many of the same issues persist.\"\n\nStarbucks didn't respond to requests for comment on the study or on baristas' reports of labor practices that are failing to meet the company's stated goals.\n\nIn an internal memo this week [published by Time](http://time.com/4047359/starbucks-scheduling-labor-practices-memo/), Starbucks executive Cliff Burrows wrote that the company couldn't validate the survey, but added that \"the findings suggest, contrary to the expectations we have in place, that some partners are receiving their schedules less than one week in advance and that there is a continuing issue with some partners working a close and then an opening shift the following morning.\" He asks store managers \"to go the extra mile to ensure partners have a consistent schedule.\"\n\n[Starbucks ends \"race together\" campaign amid public backlash 00:26](https://www.cbsnews.com/video/starbucks-ends-race-together-campaign-amid-public-backlash/)\n\nTo be sure, some Starbucks workers are receiving at least 10 days notice on their work hours, with the survey finding that about one-third receive two weeks notice and another 18 percent get their schedules three weeks in advance. But that leaves almost half of workers who only receive one week's notice, making it more difficult from them manage other obligations, such as school, family commitments or other jobs.\n\nClopens remain a problem, as well. About 60 percent of workers who have to handle a clopen receive seven or fewer hours of rest between a closing and an opening shift, the study found.\n\nThat's prompted one former Starbucks employee to start a petition to end the practice of scheduling clopens. Ciara Moran noted in her petition that she sometimes was only able to get [four or five hours of sleep](https://www.coworker.org/petitions/starbucks-end-clopens-now) on the days she was scheduled for clopens. She said she quit her job because she doubted whether it was possible to get ahead given the demands on workers.\n\nEven if Starbucks stuck with its policy of providing eight hours between shifts, that's not enough time, especially given that many workers in the service sector have long commutes, the study said.\n\nAnother issue singled out by the report is Starbucks' practices on sick time. Since paid time off is only available to workers with at least a year on the job, about 40 percent of employees in the survey said they had dealt with barriers in taking sick days.\n\nIn a perfect world, Weisse said he'd like to receive his schedule either a month or a quarter in advance. Asked if he's upset about the last-minute schedules, Weisse said he had become resigned to it.\n\n\"I don't know if you've ever seen experiments with rats,\" he said. \"First they get upset if you treat them badly. Then they lay down and try not to move too much. I'm like that now. Things are terrible.\"\n\nV2", - "title": "​Is Starbucks shortchanging its baristas? v2", - "url": "https://www.cbsnews.com/news/is-starbucks-shortchanging-its-baristas", - "source_domain": "cbsnews.com", - "user": "1" - }, - { - "_id": "64d18e9e1bae3610af2a8fee", - "authors": ["Pablo Costa v2", "Pablo Costa v3", "Pablo Costa v4"], - "cloudinary_id": "reports/image.cnbcfm.com/api/v1/image/100266733-38cbdbd7a4bcc3cf98120f6574fe94a3afe0fca6.jpg?v=1529461359&w=1920&h=1080", - "date_downloaded": "2023-08-04", - "date_modified": "2023-08-07", - "date_published": "2023-08-04", - "date_submitted": "2023-08-03", - "description": null, - "editor_notes": "editor notes v2", - "embedding": null, - "epoch_date_downloaded": 1691107200, - "epoch_date_modified": 1691455131, - "epoch_date_published": 1691107200, - "epoch_date_submitted": 1691020800, - "flag": null, - "image_url": "https://image.cnbcfm.com/api/v1/image/100266733-38cbdbd7a4bcc3cf98120f6574fe94a3afe0fca6.jpg?v=1529461359&w=1920&h=1080", - "inputs_outputs": ["input 1", "output 1", "input 2", "output 2", "input 3", "output 3"], - "is_incident_report": true, - "language": "es", - "modifiedBy": "1", - "plain_text": "\n\nBy Aimee Picchi\n\nSeptember 24, 2015 / 11:41 AM / MoneyWatch\n\nFor Starbucks (SBUX) barista Kylei Weisse, working at the coffee chain helps him secure health insurance and some extra money while he studies at Georgia Perimeter College. What it doesn't provide is the kind of stable schedule that the company promised its workers last year.\n\n\"It's the wild inconsistency\" of the hours that's a problem, Weisse, 32, said. \"We're supposed to get them 10 days in advance, which often happens, but there's no guarantee. If our manager doesn't get it to us on time, we just have to deal with it.\"\n\nThat became a problem recently when Weisse's manager gave him only a few days notice on his work hours, which ended up conflicting with an anatomy and physiology exam at his college. Weisse ended up paying another worker $20 to take his shift so he could take the exam.\n\nThe short notice is especially frustrating because of Starbucks' vow last year to post employees' schedules at least 10 days in advance, as well as the company's insistence that workers provide at least one-month notice when they need to take a day off.\n\nWhat's behind Starbucks price increases? 03:04\n\nWeisse isn't alone in complaining that Starbucks isn't living up to its promises to overhaul its labor practices for its roughly 130,000 baristas. That vow followed an article last year by The New York Times that detailed how workers were struggling to manage childcare and other obligations when the company provided only a few days notice about their schedules.\n\nAbout half of roughly 200 Starbucks baristas in a recent survey said they are still receiving their schedule with less than one week's notice. Others also reported being asked to handle \"clopens,\" split shifts in which employees work a closing shift late into the evening and then an early opening shift the following morning. The company last year promised to end the practice.\n\nOf course, Starbucks isn't alone in using \"just-in-time\" scheduling, with the retail and restaurant industry increasingly turning to software that allows them to change work schedules at the last minute, depending on whether business picks up or slows down. But it is Starbucks that has become a lightning rod on the issue given its vows to improve how it treats employees and its own emphatic claims to valuing workers, whom it labels \"partners.\"\n\n\"Starbucks has the values and wants to do right by their employees,\" said Carrie Gleason, director of the Fair Workweek Initiative at the Center for Popular Democracy, an advocacy group focused on workers'rights, and a co-author of the group's new report on the company's labor practices. \"However, since last year when the company recognized there was a serious problem with the way it scheduled workers and pledged to reform, still so many of the same issues persist.\"\n\nStarbucks didn't respond to requests for comment on the study or on baristas' reports of labor practices that are failing to meet the company's stated goals.\n\nIn an internal memo this week published by Time, Starbucks executive Cliff Burrows wrote that the company couldn't validate the survey, but added that \"the findings suggest, contrary to the expectations we have in place, that some partners are receiving their schedules less than one week in advance and that there is a continuing issue with some partners working a close and then an opening shift the following morning.\" He asks store managers \"to go the extra mile to ensure partners have a consistent schedule.\"\n\nStarbucks ends \"race together\" campaign amid public backlash 00:26\n\nTo be sure, some Starbucks workers are receiving at least 10 days notice on their work hours, with the survey finding that about one-third receive two weeks notice and another 18 percent get their schedules three weeks in advance. But that leaves almost half of workers who only receive one week's notice, making it more difficult from them manage other obligations, such as school, family commitments or other jobs.\n\nClopens remain a problem, as well. About 60 percent of workers who have to handle a clopen receive seven or fewer hours of rest between a closing and an opening shift, the study found.\n\nThat's prompted one former Starbucks employee to start a petition to end the practice of scheduling clopens. Ciara Moran noted in her petition that she sometimes was only able to get four or five hours of sleep on the days she was scheduled for clopens. She said she quit her job because she doubted whether it was possible to get ahead given the demands on workers.\n\nEven if Starbucks stuck with its policy of providing eight hours between shifts, that's not enough time, especially given that many workers in the service sector have long commutes, the study said.\n\nAnother issue singled out by the report is Starbucks' practices on sick time. Since paid time off is only available to workers with at least a year on the job, about 40 percent of employees in the survey said they had dealt with barriers in taking sick days.\n\nIn a perfect world, Weisse said he'd like to receive his schedule either a month or a quarter in advance. Asked if he's upset about the last-minute schedules, Weisse said he had become resigned to it.\n\n\"I don't know if you've ever seen experiments with rats,\" he said. \"First they get upset if you treat them badly. Then they lay down and try not to move too much. I'm like that now. Things are terrible.\"\n\nV2\n", - "report_number": 3206, - "submitters": ["Test User v2"], - "tags": [], - "text": "[](/moneywatch)\n\nBy Aimee Picchi\n\nSeptember 24, 2015 / 11:41 AM / MoneyWatch\n\nFor Starbucks ([SBUX](http://markets.cbsnews.com/SBUX/quote/)) barista Kylei Weisse, working at the coffee chain helps him secure health insurance and some extra money while he studies at Georgia Perimeter College. What it doesn't provide is the kind of stable schedule that the [company promised its workers](https://www.cbsnews.com/news/for-some-starbucks-workers-job-leaves-bitter-taste/) last year.\n\n\"It's the wild inconsistency\" of the hours that's a problem, Weisse, 32, said. \"We're supposed to get them 10 days in advance, which often happens, but there's no guarantee. If our manager doesn't get it to us on time, we just have to deal with it.\"\n\nThat became a problem recently when Weisse's manager gave him only a few days notice on his work hours, which ended up conflicting with an anatomy and physiology exam at his college. Weisse ended up paying another worker $20 to take his shift so he could take the exam.\n\nThe short notice is especially frustrating because of Starbucks' vow last year to post employees' schedules at least 10 days in advance, as well as the company's insistence that workers provide at least one-month notice when they need to take a day off.\n\n[What's behind Starbucks price increases? 03:04](https://www.cbsnews.com/video/whats-behind-starbucks-price-increases/)\n\nWeisse isn't alone in complaining that Starbucks isn't living up to its promises to overhaul its labor practices for its roughly 130,000 baristas. That vow followed an [article last year by The New York Times](http://www.nytimes.com/interactive/2014/08/13/us/starbucks-workers-scheduling-hours.html?_r=1) that detailed how workers were struggling to manage childcare and other obligations when the company provided only a few days notice about their schedules.\n\nAbout half of roughly 200 Starbucks baristas [in a recent survey](http://static1.squarespace.com/static/556496efe4b02c9d26fdf26a/t/56027485e4b02ead4f16bc2a/1443001477305/The+Grind.pdf) said they are still receiving their schedule with less than one week's notice. Others also reported being asked to handle \"clopens,\" split shifts in which employees work a closing shift late into the evening and then an early opening shift the following morning. The company last year promised to end the practice.\n\nOf course, Starbucks isn't alone in using [\"just-in-time\" scheduling](https://www.cbsnews.com/news/in-retail-life-on-the-job-often-leads-to-a-dead-end/), with the retail and restaurant industry increasingly turning to software that allows them to change work schedules at the last minute, depending on whether business picks up or slows down. But it is Starbucks that has become a lightning rod on the issue given its vows to improve how it treats employees and its own [emphatic claims](http://www.starbucks.com/careers/working-at-starbucks) to valuing workers, whom it labels \"partners.\"\n\n\"Starbucks has the values and wants to do right by their employees,\" said Carrie Gleason, director of the Fair Workweek Initiative at the Center for Popular Democracy, an advocacy group focused on workers'rights, and a co-author of the group's new [report on the company's labor practices](http://populardemocracy.org/sites/default/files/The%2BGrind.pdf). \"However, since last year when the company recognized there was a serious problem with the way it scheduled workers and pledged to reform, still so many of the same issues persist.\"\n\nStarbucks didn't respond to requests for comment on the study or on baristas' reports of labor practices that are failing to meet the company's stated goals.\n\nIn an internal memo this week [published by Time](http://time.com/4047359/starbucks-scheduling-labor-practices-memo/), Starbucks executive Cliff Burrows wrote that the company couldn't validate the survey, but added that \"the findings suggest, contrary to the expectations we have in place, that some partners are receiving their schedules less than one week in advance and that there is a continuing issue with some partners working a close and then an opening shift the following morning.\" He asks store managers \"to go the extra mile to ensure partners have a consistent schedule.\"\n\n[Starbucks ends \"race together\" campaign amid public backlash 00:26](https://www.cbsnews.com/video/starbucks-ends-race-together-campaign-amid-public-backlash/)\n\nTo be sure, some Starbucks workers are receiving at least 10 days notice on their work hours, with the survey finding that about one-third receive two weeks notice and another 18 percent get their schedules three weeks in advance. But that leaves almost half of workers who only receive one week's notice, making it more difficult from them manage other obligations, such as school, family commitments or other jobs.\n\nClopens remain a problem, as well. About 60 percent of workers who have to handle a clopen receive seven or fewer hours of rest between a closing and an opening shift, the study found.\n\nThat's prompted one former Starbucks employee to start a petition to end the practice of scheduling clopens. Ciara Moran noted in her petition that she sometimes was only able to get [four or five hours of sleep](https://www.coworker.org/petitions/starbucks-end-clopens-now) on the days she was scheduled for clopens. She said she quit her job because she doubted whether it was possible to get ahead given the demands on workers.\n\nEven if Starbucks stuck with its policy of providing eight hours between shifts, that's not enough time, especially given that many workers in the service sector have long commutes, the study said.\n\nAnother issue singled out by the report is Starbucks' practices on sick time. Since paid time off is only available to workers with at least a year on the job, about 40 percent of employees in the survey said they had dealt with barriers in taking sick days.\n\nIn a perfect world, Weisse said he'd like to receive his schedule either a month or a quarter in advance. Asked if he's upset about the last-minute schedules, Weisse said he had become resigned to it.\n\n\"I don't know if you've ever seen experiments with rats,\" he said. \"First they get upset if you treat them badly. Then they lay down and try not to move too much. I'm like that now. Things are terrible.\"\n\nV2", - "title": "​Is Starbucks shortchanging its baristas? v2", - "url": "https://www.cbsnews.com/news/is-starbucks-shortchanging-its-baristas", - "source_domain": "cbsnews.com", - "user": "1" - }, - { - "_id": "64d18dccf549de65c4e6f6d9", - "authors": ["Pablo Costa v2", "Pablo Costa v3", "Pablo Costa v4"], - "cloudinary_id": "reports/image.cnbcfm.com/api/v1/image/100266733-38cbdbd7a4bcc3cf98120f6574fe94a3afe0fca6.jpg?v=1529461359&w=1920&h=1080", - "date_downloaded": "2023-08-04", - "date_modified": "2023-08-07", - "date_published": "2023-08-04", - "date_submitted": "2023-08-03", - "description": null, - "editor_notes": "editor notes v2", - "embedding": null, - "epoch_date_downloaded": 1691107200, - "epoch_date_modified": 1691454921, - "epoch_date_published": 1691107200, - "epoch_date_submitted": 1691020800, - "flag": null, - "image_url": "https://image.cnbcfm.com/api/v1/image/100266733-38cbdbd7a4bcc3cf98120f6574fe94a3afe0fca6.jpg?v=1529461359&w=1920&h=1080", - "inputs_outputs": ["input 1", "output 1", "input 2", "output 2"], - "is_incident_report": true, - "language": "es", - "modifiedBy": "1", - "plain_text": "\n\nBy Aimee Picchi\n\nSeptember 24, 2015 / 11:41 AM / MoneyWatch\n\nFor Starbucks (SBUX) barista Kylei Weisse, working at the coffee chain helps him secure health insurance and some extra money while he studies at Georgia Perimeter College. What it doesn't provide is the kind of stable schedule that the company promised its workers last year.\n\n\"It's the wild inconsistency\" of the hours that's a problem, Weisse, 32, said. \"We're supposed to get them 10 days in advance, which often happens, but there's no guarantee. If our manager doesn't get it to us on time, we just have to deal with it.\"\n\nThat became a problem recently when Weisse's manager gave him only a few days notice on his work hours, which ended up conflicting with an anatomy and physiology exam at his college. Weisse ended up paying another worker $20 to take his shift so he could take the exam.\n\nThe short notice is especially frustrating because of Starbucks' vow last year to post employees' schedules at least 10 days in advance, as well as the company's insistence that workers provide at least one-month notice when they need to take a day off.\n\nWhat's behind Starbucks price increases? 03:04\n\nWeisse isn't alone in complaining that Starbucks isn't living up to its promises to overhaul its labor practices for its roughly 130,000 baristas. That vow followed an article last year by The New York Times that detailed how workers were struggling to manage childcare and other obligations when the company provided only a few days notice about their schedules.\n\nAbout half of roughly 200 Starbucks baristas in a recent survey said they are still receiving their schedule with less than one week's notice. Others also reported being asked to handle \"clopens,\" split shifts in which employees work a closing shift late into the evening and then an early opening shift the following morning. The company last year promised to end the practice.\n\nOf course, Starbucks isn't alone in using \"just-in-time\" scheduling, with the retail and restaurant industry increasingly turning to software that allows them to change work schedules at the last minute, depending on whether business picks up or slows down. But it is Starbucks that has become a lightning rod on the issue given its vows to improve how it treats employees and its own emphatic claims to valuing workers, whom it labels \"partners.\"\n\n\"Starbucks has the values and wants to do right by their employees,\" said Carrie Gleason, director of the Fair Workweek Initiative at the Center for Popular Democracy, an advocacy group focused on workers'rights, and a co-author of the group's new report on the company's labor practices. \"However, since last year when the company recognized there was a serious problem with the way it scheduled workers and pledged to reform, still so many of the same issues persist.\"\n\nStarbucks didn't respond to requests for comment on the study or on baristas' reports of labor practices that are failing to meet the company's stated goals.\n\nIn an internal memo this week published by Time, Starbucks executive Cliff Burrows wrote that the company couldn't validate the survey, but added that \"the findings suggest, contrary to the expectations we have in place, that some partners are receiving their schedules less than one week in advance and that there is a continuing issue with some partners working a close and then an opening shift the following morning.\" He asks store managers \"to go the extra mile to ensure partners have a consistent schedule.\"\n\nStarbucks ends \"race together\" campaign amid public backlash 00:26\n\nTo be sure, some Starbucks workers are receiving at least 10 days notice on their work hours, with the survey finding that about one-third receive two weeks notice and another 18 percent get their schedules three weeks in advance. But that leaves almost half of workers who only receive one week's notice, making it more difficult from them manage other obligations, such as school, family commitments or other jobs.\n\nClopens remain a problem, as well. About 60 percent of workers who have to handle a clopen receive seven or fewer hours of rest between a closing and an opening shift, the study found.\n\nThat's prompted one former Starbucks employee to start a petition to end the practice of scheduling clopens. Ciara Moran noted in her petition that she sometimes was only able to get four or five hours of sleep on the days she was scheduled for clopens. She said she quit her job because she doubted whether it was possible to get ahead given the demands on workers.\n\nEven if Starbucks stuck with its policy of providing eight hours between shifts, that's not enough time, especially given that many workers in the service sector have long commutes, the study said.\n\nAnother issue singled out by the report is Starbucks' practices on sick time. Since paid time off is only available to workers with at least a year on the job, about 40 percent of employees in the survey said they had dealt with barriers in taking sick days.\n\nIn a perfect world, Weisse said he'd like to receive his schedule either a month or a quarter in advance. Asked if he's upset about the last-minute schedules, Weisse said he had become resigned to it.\n\n\"I don't know if you've ever seen experiments with rats,\" he said. \"First they get upset if you treat them badly. Then they lay down and try not to move too much. I'm like that now. Things are terrible.\"\n\nV2\n", - "report_number": 3206, - "submitters": ["Test User v2"], - "tags": [], - "text": "[](/moneywatch)\n\nBy Aimee Picchi\n\nSeptember 24, 2015 / 11:41 AM / MoneyWatch\n\nFor Starbucks ([SBUX](http://markets.cbsnews.com/SBUX/quote/)) barista Kylei Weisse, working at the coffee chain helps him secure health insurance and some extra money while he studies at Georgia Perimeter College. What it doesn't provide is the kind of stable schedule that the [company promised its workers](https://www.cbsnews.com/news/for-some-starbucks-workers-job-leaves-bitter-taste/) last year.\n\n\"It's the wild inconsistency\" of the hours that's a problem, Weisse, 32, said. \"We're supposed to get them 10 days in advance, which often happens, but there's no guarantee. If our manager doesn't get it to us on time, we just have to deal with it.\"\n\nThat became a problem recently when Weisse's manager gave him only a few days notice on his work hours, which ended up conflicting with an anatomy and physiology exam at his college. Weisse ended up paying another worker $20 to take his shift so he could take the exam.\n\nThe short notice is especially frustrating because of Starbucks' vow last year to post employees' schedules at least 10 days in advance, as well as the company's insistence that workers provide at least one-month notice when they need to take a day off.\n\n[What's behind Starbucks price increases? 03:04](https://www.cbsnews.com/video/whats-behind-starbucks-price-increases/)\n\nWeisse isn't alone in complaining that Starbucks isn't living up to its promises to overhaul its labor practices for its roughly 130,000 baristas. That vow followed an [article last year by The New York Times](http://www.nytimes.com/interactive/2014/08/13/us/starbucks-workers-scheduling-hours.html?_r=1) that detailed how workers were struggling to manage childcare and other obligations when the company provided only a few days notice about their schedules.\n\nAbout half of roughly 200 Starbucks baristas [in a recent survey](http://static1.squarespace.com/static/556496efe4b02c9d26fdf26a/t/56027485e4b02ead4f16bc2a/1443001477305/The+Grind.pdf) said they are still receiving their schedule with less than one week's notice. Others also reported being asked to handle \"clopens,\" split shifts in which employees work a closing shift late into the evening and then an early opening shift the following morning. The company last year promised to end the practice.\n\nOf course, Starbucks isn't alone in using [\"just-in-time\" scheduling](https://www.cbsnews.com/news/in-retail-life-on-the-job-often-leads-to-a-dead-end/), with the retail and restaurant industry increasingly turning to software that allows them to change work schedules at the last minute, depending on whether business picks up or slows down. But it is Starbucks that has become a lightning rod on the issue given its vows to improve how it treats employees and its own [emphatic claims](http://www.starbucks.com/careers/working-at-starbucks) to valuing workers, whom it labels \"partners.\"\n\n\"Starbucks has the values and wants to do right by their employees,\" said Carrie Gleason, director of the Fair Workweek Initiative at the Center for Popular Democracy, an advocacy group focused on workers'rights, and a co-author of the group's new [report on the company's labor practices](http://populardemocracy.org/sites/default/files/The%2BGrind.pdf). \"However, since last year when the company recognized there was a serious problem with the way it scheduled workers and pledged to reform, still so many of the same issues persist.\"\n\nStarbucks didn't respond to requests for comment on the study or on baristas' reports of labor practices that are failing to meet the company's stated goals.\n\nIn an internal memo this week [published by Time](http://time.com/4047359/starbucks-scheduling-labor-practices-memo/), Starbucks executive Cliff Burrows wrote that the company couldn't validate the survey, but added that \"the findings suggest, contrary to the expectations we have in place, that some partners are receiving their schedules less than one week in advance and that there is a continuing issue with some partners working a close and then an opening shift the following morning.\" He asks store managers \"to go the extra mile to ensure partners have a consistent schedule.\"\n\n[Starbucks ends \"race together\" campaign amid public backlash 00:26](https://www.cbsnews.com/video/starbucks-ends-race-together-campaign-amid-public-backlash/)\n\nTo be sure, some Starbucks workers are receiving at least 10 days notice on their work hours, with the survey finding that about one-third receive two weeks notice and another 18 percent get their schedules three weeks in advance. But that leaves almost half of workers who only receive one week's notice, making it more difficult from them manage other obligations, such as school, family commitments or other jobs.\n\nClopens remain a problem, as well. About 60 percent of workers who have to handle a clopen receive seven or fewer hours of rest between a closing and an opening shift, the study found.\n\nThat's prompted one former Starbucks employee to start a petition to end the practice of scheduling clopens. Ciara Moran noted in her petition that she sometimes was only able to get [four or five hours of sleep](https://www.coworker.org/petitions/starbucks-end-clopens-now) on the days she was scheduled for clopens. She said she quit her job because she doubted whether it was possible to get ahead given the demands on workers.\n\nEven if Starbucks stuck with its policy of providing eight hours between shifts, that's not enough time, especially given that many workers in the service sector have long commutes, the study said.\n\nAnother issue singled out by the report is Starbucks' practices on sick time. Since paid time off is only available to workers with at least a year on the job, about 40 percent of employees in the survey said they had dealt with barriers in taking sick days.\n\nIn a perfect world, Weisse said he'd like to receive his schedule either a month or a quarter in advance. Asked if he's upset about the last-minute schedules, Weisse said he had become resigned to it.\n\n\"I don't know if you've ever seen experiments with rats,\" he said. \"First they get upset if you treat them badly. Then they lay down and try not to move too much. I'm like that now. Things are terrible.\"\n\nV2", - "title": "​Is Starbucks shortchanging its baristas? v2", - "url": "https://www.cbsnews.com/news/is-starbucks-shortchanging-its-baristas", - "source_domain": "cbsnews.com", - "user": "1" - }, - { - "_id": "64d18b101a4fcdeaf136613a", - "authors": ["Pablo Costa v2"], - "cloudinary_id": "reports/image.cnbcfm.com/api/v1/image/100266733-38cbdbd7a4bcc3cf98120f6574fe94a3afe0fca6.jpg?v=1529461359&w=1920&h=1080", - "date_downloaded": "2023-08-04", - "date_modified": "2023-08-07", - "date_published": "2023-08-04", - "date_submitted": "2023-08-03", - "description": null, - "editor_notes": "editor notes v2", - "embedding": null, - "epoch_date_downloaded": 1691107200, - "epoch_date_modified": 1691454221, - "epoch_date_published": 1691107200, - "epoch_date_submitted": 1691020800, - "flag": null, - "image_url": "https://image.cnbcfm.com/api/v1/image/100266733-38cbdbd7a4bcc3cf98120f6574fe94a3afe0fca6.jpg?v=1529461359&w=1920&h=1080", - "inputs_outputs": ["input 1", "output 1", "input 2", "output 2"], - "is_incident_report": true, - "language": "es", - "modifiedBy": "1", - "plain_text": "\n\nBy Aimee Picchi\n\nSeptember 24, 2015 / 11:41 AM / MoneyWatch\n\nFor Starbucks (SBUX) barista Kylei Weisse, working at the coffee chain helps him secure health insurance and some extra money while he studies at Georgia Perimeter College. What it doesn't provide is the kind of stable schedule that the company promised its workers last year.\n\n\"It's the wild inconsistency\" of the hours that's a problem, Weisse, 32, said. \"We're supposed to get them 10 days in advance, which often happens, but there's no guarantee. If our manager doesn't get it to us on time, we just have to deal with it.\"\n\nThat became a problem recently when Weisse's manager gave him only a few days notice on his work hours, which ended up conflicting with an anatomy and physiology exam at his college. Weisse ended up paying another worker $20 to take his shift so he could take the exam.\n\nThe short notice is especially frustrating because of Starbucks' vow last year to post employees' schedules at least 10 days in advance, as well as the company's insistence that workers provide at least one-month notice when they need to take a day off.\n\nWhat's behind Starbucks price increases? 03:04\n\nWeisse isn't alone in complaining that Starbucks isn't living up to its promises to overhaul its labor practices for its roughly 130,000 baristas. That vow followed an article last year by The New York Times that detailed how workers were struggling to manage childcare and other obligations when the company provided only a few days notice about their schedules.\n\nAbout half of roughly 200 Starbucks baristas in a recent survey said they are still receiving their schedule with less than one week's notice. Others also reported being asked to handle \"clopens,\" split shifts in which employees work a closing shift late into the evening and then an early opening shift the following morning. The company last year promised to end the practice.\n\nOf course, Starbucks isn't alone in using \"just-in-time\" scheduling, with the retail and restaurant industry increasingly turning to software that allows them to change work schedules at the last minute, depending on whether business picks up or slows down. But it is Starbucks that has become a lightning rod on the issue given its vows to improve how it treats employees and its own emphatic claims to valuing workers, whom it labels \"partners.\"\n\n\"Starbucks has the values and wants to do right by their employees,\" said Carrie Gleason, director of the Fair Workweek Initiative at the Center for Popular Democracy, an advocacy group focused on workers'rights, and a co-author of the group's new report on the company's labor practices. \"However, since last year when the company recognized there was a serious problem with the way it scheduled workers and pledged to reform, still so many of the same issues persist.\"\n\nStarbucks didn't respond to requests for comment on the study or on baristas' reports of labor practices that are failing to meet the company's stated goals.\n\nIn an internal memo this week published by Time, Starbucks executive Cliff Burrows wrote that the company couldn't validate the survey, but added that \"the findings suggest, contrary to the expectations we have in place, that some partners are receiving their schedules less than one week in advance and that there is a continuing issue with some partners working a close and then an opening shift the following morning.\" He asks store managers \"to go the extra mile to ensure partners have a consistent schedule.\"\n\nStarbucks ends \"race together\" campaign amid public backlash 00:26\n\nTo be sure, some Starbucks workers are receiving at least 10 days notice on their work hours, with the survey finding that about one-third receive two weeks notice and another 18 percent get their schedules three weeks in advance. But that leaves almost half of workers who only receive one week's notice, making it more difficult from them manage other obligations, such as school, family commitments or other jobs.\n\nClopens remain a problem, as well. About 60 percent of workers who have to handle a clopen receive seven or fewer hours of rest between a closing and an opening shift, the study found.\n\nThat's prompted one former Starbucks employee to start a petition to end the practice of scheduling clopens. Ciara Moran noted in her petition that she sometimes was only able to get four or five hours of sleep on the days she was scheduled for clopens. She said she quit her job because she doubted whether it was possible to get ahead given the demands on workers.\n\nEven if Starbucks stuck with its policy of providing eight hours between shifts, that's not enough time, especially given that many workers in the service sector have long commutes, the study said.\n\nAnother issue singled out by the report is Starbucks' practices on sick time. Since paid time off is only available to workers with at least a year on the job, about 40 percent of employees in the survey said they had dealt with barriers in taking sick days.\n\nIn a perfect world, Weisse said he'd like to receive his schedule either a month or a quarter in advance. Asked if he's upset about the last-minute schedules, Weisse said he had become resigned to it.\n\n\"I don't know if you've ever seen experiments with rats,\" he said. \"First they get upset if you treat them badly. Then they lay down and try not to move too much. I'm like that now. Things are terrible.\"\n\nV2\n", - "report_number": 3206, - "submitters": ["Test User v2"], - "tags": [], - "text": "[](/moneywatch)\n\nBy Aimee Picchi\n\nSeptember 24, 2015 / 11:41 AM / MoneyWatch\n\nFor Starbucks ([SBUX](http://markets.cbsnews.com/SBUX/quote/)) barista Kylei Weisse, working at the coffee chain helps him secure health insurance and some extra money while he studies at Georgia Perimeter College. What it doesn't provide is the kind of stable schedule that the [company promised its workers](https://www.cbsnews.com/news/for-some-starbucks-workers-job-leaves-bitter-taste/) last year.\n\n\"It's the wild inconsistency\" of the hours that's a problem, Weisse, 32, said. \"We're supposed to get them 10 days in advance, which often happens, but there's no guarantee. If our manager doesn't get it to us on time, we just have to deal with it.\"\n\nThat became a problem recently when Weisse's manager gave him only a few days notice on his work hours, which ended up conflicting with an anatomy and physiology exam at his college. Weisse ended up paying another worker $20 to take his shift so he could take the exam.\n\nThe short notice is especially frustrating because of Starbucks' vow last year to post employees' schedules at least 10 days in advance, as well as the company's insistence that workers provide at least one-month notice when they need to take a day off.\n\n[What's behind Starbucks price increases? 03:04](https://www.cbsnews.com/video/whats-behind-starbucks-price-increases/)\n\nWeisse isn't alone in complaining that Starbucks isn't living up to its promises to overhaul its labor practices for its roughly 130,000 baristas. That vow followed an [article last year by The New York Times](http://www.nytimes.com/interactive/2014/08/13/us/starbucks-workers-scheduling-hours.html?_r=1) that detailed how workers were struggling to manage childcare and other obligations when the company provided only a few days notice about their schedules.\n\nAbout half of roughly 200 Starbucks baristas [in a recent survey](http://static1.squarespace.com/static/556496efe4b02c9d26fdf26a/t/56027485e4b02ead4f16bc2a/1443001477305/The+Grind.pdf) said they are still receiving their schedule with less than one week's notice. Others also reported being asked to handle \"clopens,\" split shifts in which employees work a closing shift late into the evening and then an early opening shift the following morning. The company last year promised to end the practice.\n\nOf course, Starbucks isn't alone in using [\"just-in-time\" scheduling](https://www.cbsnews.com/news/in-retail-life-on-the-job-often-leads-to-a-dead-end/), with the retail and restaurant industry increasingly turning to software that allows them to change work schedules at the last minute, depending on whether business picks up or slows down. But it is Starbucks that has become a lightning rod on the issue given its vows to improve how it treats employees and its own [emphatic claims](http://www.starbucks.com/careers/working-at-starbucks) to valuing workers, whom it labels \"partners.\"\n\n\"Starbucks has the values and wants to do right by their employees,\" said Carrie Gleason, director of the Fair Workweek Initiative at the Center for Popular Democracy, an advocacy group focused on workers'rights, and a co-author of the group's new [report on the company's labor practices](http://populardemocracy.org/sites/default/files/The%2BGrind.pdf). \"However, since last year when the company recognized there was a serious problem with the way it scheduled workers and pledged to reform, still so many of the same issues persist.\"\n\nStarbucks didn't respond to requests for comment on the study or on baristas' reports of labor practices that are failing to meet the company's stated goals.\n\nIn an internal memo this week [published by Time](http://time.com/4047359/starbucks-scheduling-labor-practices-memo/), Starbucks executive Cliff Burrows wrote that the company couldn't validate the survey, but added that \"the findings suggest, contrary to the expectations we have in place, that some partners are receiving their schedules less than one week in advance and that there is a continuing issue with some partners working a close and then an opening shift the following morning.\" He asks store managers \"to go the extra mile to ensure partners have a consistent schedule.\"\n\n[Starbucks ends \"race together\" campaign amid public backlash 00:26](https://www.cbsnews.com/video/starbucks-ends-race-together-campaign-amid-public-backlash/)\n\nTo be sure, some Starbucks workers are receiving at least 10 days notice on their work hours, with the survey finding that about one-third receive two weeks notice and another 18 percent get their schedules three weeks in advance. But that leaves almost half of workers who only receive one week's notice, making it more difficult from them manage other obligations, such as school, family commitments or other jobs.\n\nClopens remain a problem, as well. About 60 percent of workers who have to handle a clopen receive seven or fewer hours of rest between a closing and an opening shift, the study found.\n\nThat's prompted one former Starbucks employee to start a petition to end the practice of scheduling clopens. Ciara Moran noted in her petition that she sometimes was only able to get [four or five hours of sleep](https://www.coworker.org/petitions/starbucks-end-clopens-now) on the days she was scheduled for clopens. She said she quit her job because she doubted whether it was possible to get ahead given the demands on workers.\n\nEven if Starbucks stuck with its policy of providing eight hours between shifts, that's not enough time, especially given that many workers in the service sector have long commutes, the study said.\n\nAnother issue singled out by the report is Starbucks' practices on sick time. Since paid time off is only available to workers with at least a year on the job, about 40 percent of employees in the survey said they had dealt with barriers in taking sick days.\n\nIn a perfect world, Weisse said he'd like to receive his schedule either a month or a quarter in advance. Asked if he's upset about the last-minute schedules, Weisse said he had become resigned to it.\n\n\"I don't know if you've ever seen experiments with rats,\" he said. \"First they get upset if you treat them badly. Then they lay down and try not to move too much. I'm like that now. Things are terrible.\"\n\nV2", - "title": "​Is Starbucks shortchanging its baristas? v2", - "url": "https://www.cbsnews.com/news/is-starbucks-shortchanging-its-baristas", - "source_domain": "cbsnews.com", - "user": "1" - }, - { - "_id": "64d17f86f549de65c4e31a29", - "authors": ["Pablo Costa v2"], - "cloudinary_id": "reports/image.cnbcfm.com/api/v1/image/100266733-38cbdbd7a4bcc3cf98120f6574fe94a3afe0fca6.jpg?v=1529461359&w=1920&h=1080", - "date_downloaded": "2023-08-04", - "date_modified": "2023-08-07", - "date_published": "2023-08-04", - "date_submitted": "2023-08-03", - "description": null, - "editor_notes": "editor notes v2", - "embedding": null, - "epoch_date_downloaded": 1691107200, - "epoch_date_modified": 1691451266, - "epoch_date_published": 1691107200, - "epoch_date_submitted": 1691020800, - "flag": null, - "image_url": "https://image.cnbcfm.com/api/v1/image/100266733-38cbdbd7a4bcc3cf98120f6574fe94a3afe0fca6.jpg?v=1529461359&w=1920&h=1080", - "inputs_outputs": null, - "is_incident_report": true, - "language": "es", - "modifiedBy": "1", - "plain_text": "\n\nBy Aimee Picchi\n\nSeptember 24, 2015 / 11:41 AM / MoneyWatch\n\nFor Starbucks (SBUX) barista Kylei Weisse, working at the coffee chain helps him secure health insurance and some extra money while he studies at Georgia Perimeter College. What it doesn't provide is the kind of stable schedule that the company promised its workers last year.\n\n\"It's the wild inconsistency\" of the hours that's a problem, Weisse, 32, said. \"We're supposed to get them 10 days in advance, which often happens, but there's no guarantee. If our manager doesn't get it to us on time, we just have to deal with it.\"\n\nThat became a problem recently when Weisse's manager gave him only a few days notice on his work hours, which ended up conflicting with an anatomy and physiology exam at his college. Weisse ended up paying another worker $20 to take his shift so he could take the exam.\n\nThe short notice is especially frustrating because of Starbucks' vow last year to post employees' schedules at least 10 days in advance, as well as the company's insistence that workers provide at least one-month notice when they need to take a day off.\n\nWhat's behind Starbucks price increases? 03:04\n\nWeisse isn't alone in complaining that Starbucks isn't living up to its promises to overhaul its labor practices for its roughly 130,000 baristas. That vow followed an article last year by The New York Times that detailed how workers were struggling to manage childcare and other obligations when the company provided only a few days notice about their schedules.\n\nAbout half of roughly 200 Starbucks baristas in a recent survey said they are still receiving their schedule with less than one week's notice. Others also reported being asked to handle \"clopens,\" split shifts in which employees work a closing shift late into the evening and then an early opening shift the following morning. The company last year promised to end the practice.\n\nOf course, Starbucks isn't alone in using \"just-in-time\" scheduling, with the retail and restaurant industry increasingly turning to software that allows them to change work schedules at the last minute, depending on whether business picks up or slows down. But it is Starbucks that has become a lightning rod on the issue given its vows to improve how it treats employees and its own emphatic claims to valuing workers, whom it labels \"partners.\"\n\n\"Starbucks has the values and wants to do right by their employees,\" said Carrie Gleason, director of the Fair Workweek Initiative at the Center for Popular Democracy, an advocacy group focused on workers'rights, and a co-author of the group's new report on the company's labor practices. \"However, since last year when the company recognized there was a serious problem with the way it scheduled workers and pledged to reform, still so many of the same issues persist.\"\n\nStarbucks didn't respond to requests for comment on the study or on baristas' reports of labor practices that are failing to meet the company's stated goals.\n\nIn an internal memo this week published by Time, Starbucks executive Cliff Burrows wrote that the company couldn't validate the survey, but added that \"the findings suggest, contrary to the expectations we have in place, that some partners are receiving their schedules less than one week in advance and that there is a continuing issue with some partners working a close and then an opening shift the following morning.\" He asks store managers \"to go the extra mile to ensure partners have a consistent schedule.\"\n\nStarbucks ends \"race together\" campaign amid public backlash 00:26\n\nTo be sure, some Starbucks workers are receiving at least 10 days notice on their work hours, with the survey finding that about one-third receive two weeks notice and another 18 percent get their schedules three weeks in advance. But that leaves almost half of workers who only receive one week's notice, making it more difficult from them manage other obligations, such as school, family commitments or other jobs.\n\nClopens remain a problem, as well. About 60 percent of workers who have to handle a clopen receive seven or fewer hours of rest between a closing and an opening shift, the study found.\n\nThat's prompted one former Starbucks employee to start a petition to end the practice of scheduling clopens. Ciara Moran noted in her petition that she sometimes was only able to get four or five hours of sleep on the days she was scheduled for clopens. She said she quit her job because she doubted whether it was possible to get ahead given the demands on workers.\n\nEven if Starbucks stuck with its policy of providing eight hours between shifts, that's not enough time, especially given that many workers in the service sector have long commutes, the study said.\n\nAnother issue singled out by the report is Starbucks' practices on sick time. Since paid time off is only available to workers with at least a year on the job, about 40 percent of employees in the survey said they had dealt with barriers in taking sick days.\n\nIn a perfect world, Weisse said he'd like to receive his schedule either a month or a quarter in advance. Asked if he's upset about the last-minute schedules, Weisse said he had become resigned to it.\n\n\"I don't know if you've ever seen experiments with rats,\" he said. \"First they get upset if you treat them badly. Then they lay down and try not to move too much. I'm like that now. Things are terrible.\"\n\nV2\n", - "report_number": 3206, - "submitters": ["Test User v2"], - "tags": [], - "text": "[](/moneywatch)\n\nBy Aimee Picchi\n\nSeptember 24, 2015 / 11:41 AM / MoneyWatch\n\nFor Starbucks ([SBUX](http://markets.cbsnews.com/SBUX/quote/)) barista Kylei Weisse, working at the coffee chain helps him secure health insurance and some extra money while he studies at Georgia Perimeter College. What it doesn't provide is the kind of stable schedule that the [company promised its workers](https://www.cbsnews.com/news/for-some-starbucks-workers-job-leaves-bitter-taste/) last year.\n\n\"It's the wild inconsistency\" of the hours that's a problem, Weisse, 32, said. \"We're supposed to get them 10 days in advance, which often happens, but there's no guarantee. If our manager doesn't get it to us on time, we just have to deal with it.\"\n\nThat became a problem recently when Weisse's manager gave him only a few days notice on his work hours, which ended up conflicting with an anatomy and physiology exam at his college. Weisse ended up paying another worker $20 to take his shift so he could take the exam.\n\nThe short notice is especially frustrating because of Starbucks' vow last year to post employees' schedules at least 10 days in advance, as well as the company's insistence that workers provide at least one-month notice when they need to take a day off.\n\n[What's behind Starbucks price increases? 03:04](https://www.cbsnews.com/video/whats-behind-starbucks-price-increases/)\n\nWeisse isn't alone in complaining that Starbucks isn't living up to its promises to overhaul its labor practices for its roughly 130,000 baristas. That vow followed an [article last year by The New York Times](http://www.nytimes.com/interactive/2014/08/13/us/starbucks-workers-scheduling-hours.html?_r=1) that detailed how workers were struggling to manage childcare and other obligations when the company provided only a few days notice about their schedules.\n\nAbout half of roughly 200 Starbucks baristas [in a recent survey](http://static1.squarespace.com/static/556496efe4b02c9d26fdf26a/t/56027485e4b02ead4f16bc2a/1443001477305/The+Grind.pdf) said they are still receiving their schedule with less than one week's notice. Others also reported being asked to handle \"clopens,\" split shifts in which employees work a closing shift late into the evening and then an early opening shift the following morning. The company last year promised to end the practice.\n\nOf course, Starbucks isn't alone in using [\"just-in-time\" scheduling](https://www.cbsnews.com/news/in-retail-life-on-the-job-often-leads-to-a-dead-end/), with the retail and restaurant industry increasingly turning to software that allows them to change work schedules at the last minute, depending on whether business picks up or slows down. But it is Starbucks that has become a lightning rod on the issue given its vows to improve how it treats employees and its own [emphatic claims](http://www.starbucks.com/careers/working-at-starbucks) to valuing workers, whom it labels \"partners.\"\n\n\"Starbucks has the values and wants to do right by their employees,\" said Carrie Gleason, director of the Fair Workweek Initiative at the Center for Popular Democracy, an advocacy group focused on workers'rights, and a co-author of the group's new [report on the company's labor practices](http://populardemocracy.org/sites/default/files/The%2BGrind.pdf). \"However, since last year when the company recognized there was a serious problem with the way it scheduled workers and pledged to reform, still so many of the same issues persist.\"\n\nStarbucks didn't respond to requests for comment on the study or on baristas' reports of labor practices that are failing to meet the company's stated goals.\n\nIn an internal memo this week [published by Time](http://time.com/4047359/starbucks-scheduling-labor-practices-memo/), Starbucks executive Cliff Burrows wrote that the company couldn't validate the survey, but added that \"the findings suggest, contrary to the expectations we have in place, that some partners are receiving their schedules less than one week in advance and that there is a continuing issue with some partners working a close and then an opening shift the following morning.\" He asks store managers \"to go the extra mile to ensure partners have a consistent schedule.\"\n\n[Starbucks ends \"race together\" campaign amid public backlash 00:26](https://www.cbsnews.com/video/starbucks-ends-race-together-campaign-amid-public-backlash/)\n\nTo be sure, some Starbucks workers are receiving at least 10 days notice on their work hours, with the survey finding that about one-third receive two weeks notice and another 18 percent get their schedules three weeks in advance. But that leaves almost half of workers who only receive one week's notice, making it more difficult from them manage other obligations, such as school, family commitments or other jobs.\n\nClopens remain a problem, as well. About 60 percent of workers who have to handle a clopen receive seven or fewer hours of rest between a closing and an opening shift, the study found.\n\nThat's prompted one former Starbucks employee to start a petition to end the practice of scheduling clopens. Ciara Moran noted in her petition that she sometimes was only able to get [four or five hours of sleep](https://www.coworker.org/petitions/starbucks-end-clopens-now) on the days she was scheduled for clopens. She said she quit her job because she doubted whether it was possible to get ahead given the demands on workers.\n\nEven if Starbucks stuck with its policy of providing eight hours between shifts, that's not enough time, especially given that many workers in the service sector have long commutes, the study said.\n\nAnother issue singled out by the report is Starbucks' practices on sick time. Since paid time off is only available to workers with at least a year on the job, about 40 percent of employees in the survey said they had dealt with barriers in taking sick days.\n\nIn a perfect world, Weisse said he'd like to receive his schedule either a month or a quarter in advance. Asked if he's upset about the last-minute schedules, Weisse said he had become resigned to it.\n\n\"I don't know if you've ever seen experiments with rats,\" he said. \"First they get upset if you treat them badly. Then they lay down and try not to move too much. I'm like that now. Things are terrible.\"\n\nV2", - "title": "​Is Starbucks shortchanging its baristas? v2", - "url": "https://www.cbsnews.com/news/is-starbucks-shortchanging-its-baristas", - "source_domain": "cbsnews.com", - "user": "1" - }, - { - "_id": "64cc41211c1f73dbc02ab00c", - "authors": ["Pablo Costa"], - "cloudinary_id": "reports/assets3.cbsnewsstatic.com/hub/i/r/2015/03/17/01a38576-5108-40f7-8df8-5416164ed878/thumbnail/1200x630/ca8d35fe6bc065b5c9a747d92bc6d94c/154211248.jpg?v=523df724471d66f84598615fede8d33f", - "date_downloaded": "2023-08-03", - "date_modified": "2023-08-03", - "date_published": "2023-08-03", - "date_submitted": "2023-08-03", - "description": null, - "editor_notes": null, - "embedding": { - "__typename": "History_reportEmbedding", - "from_text_hash": "0cf0b5d5d39e3e9040ce8da9367364511c3e9532", - "vector": [ - -0.09017669409513474, 0.049546271562576294, 0.038945719599723816, -0.1436443030834198 - ] - }, - "epoch_date_downloaded": 1691020800, - "epoch_date_modified": 1691107596, - "epoch_date_published": 1691020800, - "epoch_date_submitted": 1691020800, - "flag": null, - "image_url": "https://assets3.cbsnewsstatic.com/hub/i/r/2015/03/17/01a38576-5108-40f7-8df8-5416164ed878/thumbnail/1200x630/ca8d35fe6bc065b5c9a747d92bc6d94c/154211248.jpg?v=523df724471d66f84598615fede8d33f", - "inputs_outputs": null, - "is_incident_report": true, - "language": "en", - "modifiedBy": "1", - "plain_text": "\n\nBy Aimee Picchi\n\nSeptember 24, 2015 / 11:41 AM / MoneyWatch\n\nFor Starbucks (SBUX) barista Kylei Weisse, working at the coffee chain helps him secure health insurance and some extra money while he studies at Georgia Perimeter College. What it doesn't provide is the kind of stable schedule that the company promised its workers last year.\n\n\"It's the wild inconsistency\" of the hours that's a problem, Weisse, 32, said. \"We're supposed to get them 10 days in advance, which often happens, but there's no guarantee. If our manager doesn't get it to us on time, we just have to deal with it.\"\n\nThat became a problem recently when Weisse's manager gave him only a few days notice on his work hours, which ended up conflicting with an anatomy and physiology exam at his college. Weisse ended up paying another worker $20 to take his shift so he could take the exam.\n\nThe short notice is especially frustrating because of Starbucks' vow last year to post employees' schedules at least 10 days in advance, as well as the company's insistence that workers provide at least one-month notice when they need to take a day off.\n\nWhat's behind Starbucks price increases? 03:04\n\nWeisse isn't alone in complaining that Starbucks isn't living up to its promises to overhaul its labor practices for its roughly 130,000 baristas. That vow followed an article last year by The New York Times that detailed how workers were struggling to manage childcare and other obligations when the company provided only a few days notice about their schedules.\n\nAbout half of roughly 200 Starbucks baristas in a recent survey said they are still receiving their schedule with less than one week's notice. Others also reported being asked to handle \"clopens,\" split shifts in which employees work a closing shift late into the evening and then an early opening shift the following morning. The company last year promised to end the practice.\n\nOf course, Starbucks isn't alone in using \"just-in-time\" scheduling, with the retail and restaurant industry increasingly turning to software that allows them to change work schedules at the last minute, depending on whether business picks up or slows down. But it is Starbucks that has become a lightning rod on the issue given its vows to improve how it treats employees and its own emphatic claims to valuing workers, whom it labels \"partners.\"\n\n\"Starbucks has the values and wants to do right by their employees,\" said Carrie Gleason, director of the Fair Workweek Initiative at the Center for Popular Democracy, an advocacy group focused on workers'rights, and a co-author of the group's new report on the company's labor practices. \"However, since last year when the company recognized there was a serious problem with the way it scheduled workers and pledged to reform, still so many of the same issues persist.\"\n\nStarbucks didn't respond to requests for comment on the study or on baristas' reports of labor practices that are failing to meet the company's stated goals.\n\nIn an internal memo this week published by Time, Starbucks executive Cliff Burrows wrote that the company couldn't validate the survey, but added that \"the findings suggest, contrary to the expectations we have in place, that some partners are receiving their schedules less than one week in advance and that there is a continuing issue with some partners working a close and then an opening shift the following morning.\" He asks store managers \"to go the extra mile to ensure partners have a consistent schedule.\"\n\nStarbucks ends \"race together\" campaign amid public backlash 00:26\n\nTo be sure, some Starbucks workers are receiving at least 10 days notice on their work hours, with the survey finding that about one-third receive two weeks notice and another 18 percent get their schedules three weeks in advance. But that leaves almost half of workers who only receive one week's notice, making it more difficult from them manage other obligations, such as school, family commitments or other jobs.\n\nClopens remain a problem, as well. About 60 percent of workers who have to handle a clopen receive seven or fewer hours of rest between a closing and an opening shift, the study found.\n\nThat's prompted one former Starbucks employee to start a petition to end the practice of scheduling clopens. Ciara Moran noted in her petition that she sometimes was only able to get four or five hours of sleep on the days she was scheduled for clopens. She said she quit her job because she doubted whether it was possible to get ahead given the demands on workers.\n\nEven if Starbucks stuck with its policy of providing eight hours between shifts, that's not enough time, especially given that many workers in the service sector have long commutes, the study said.\n\nAnother issue singled out by the report is Starbucks' practices on sick time. Since paid time off is only available to workers with at least a year on the job, about 40 percent of employees in the survey said they had dealt with barriers in taking sick days.\n\nIn a perfect world, Weisse said he'd like to receive his schedule either a month or a quarter in advance. Asked if he's upset about the last-minute schedules, Weisse said he had become resigned to it.\n\n\"I don't know if you've ever seen experiments with rats,\" he said. \"First they get upset if you treat them badly. Then they lay down and try not to move too much. I'm like that now. Things are terrible.\"\n", - "report_number": 3206, - "submitters": ["Test User"], - "tags": [], - "text": "[](/moneywatch)\n\nBy Aimee Picchi\n\nSeptember 24, 2015 / 11:41 AM / MoneyWatch\n\nFor Starbucks ([SBUX](http://markets.cbsnews.com/SBUX/quote/)) barista Kylei Weisse, working at the coffee chain helps him secure health insurance and some extra money while he studies at Georgia Perimeter College. What it doesn't provide is the kind of stable schedule that the [company promised its workers](https://www.cbsnews.com/news/for-some-starbucks-workers-job-leaves-bitter-taste/) last year.\n\n\"It's the wild inconsistency\" of the hours that's a problem, Weisse, 32, said. \"We're supposed to get them 10 days in advance, which often happens, but there's no guarantee. If our manager doesn't get it to us on time, we just have to deal with it.\"\n\nThat became a problem recently when Weisse's manager gave him only a few days notice on his work hours, which ended up conflicting with an anatomy and physiology exam at his college. Weisse ended up paying another worker $20 to take his shift so he could take the exam.\n\nThe short notice is especially frustrating because of Starbucks' vow last year to post employees' schedules at least 10 days in advance, as well as the company's insistence that workers provide at least one-month notice when they need to take a day off.\n\n[What's behind Starbucks price increases? 03:04](https://www.cbsnews.com/video/whats-behind-starbucks-price-increases/)\n\nWeisse isn't alone in complaining that Starbucks isn't living up to its promises to overhaul its labor practices for its roughly 130,000 baristas. That vow followed an [article last year by The New York Times](http://www.nytimes.com/interactive/2014/08/13/us/starbucks-workers-scheduling-hours.html?_r=1) that detailed how workers were struggling to manage childcare and other obligations when the company provided only a few days notice about their schedules.\n\nAbout half of roughly 200 Starbucks baristas [in a recent survey](http://static1.squarespace.com/static/556496efe4b02c9d26fdf26a/t/56027485e4b02ead4f16bc2a/1443001477305/The+Grind.pdf) said they are still receiving their schedule with less than one week's notice. Others also reported being asked to handle \"clopens,\" split shifts in which employees work a closing shift late into the evening and then an early opening shift the following morning. The company last year promised to end the practice.\n\nOf course, Starbucks isn't alone in using [\"just-in-time\" scheduling](https://www.cbsnews.com/news/in-retail-life-on-the-job-often-leads-to-a-dead-end/), with the retail and restaurant industry increasingly turning to software that allows them to change work schedules at the last minute, depending on whether business picks up or slows down. But it is Starbucks that has become a lightning rod on the issue given its vows to improve how it treats employees and its own [emphatic claims](http://www.starbucks.com/careers/working-at-starbucks) to valuing workers, whom it labels \"partners.\"\n\n\"Starbucks has the values and wants to do right by their employees,\" said Carrie Gleason, director of the Fair Workweek Initiative at the Center for Popular Democracy, an advocacy group focused on workers'rights, and a co-author of the group's new [report on the company's labor practices](http://populardemocracy.org/sites/default/files/The%2BGrind.pdf). \"However, since last year when the company recognized there was a serious problem with the way it scheduled workers and pledged to reform, still so many of the same issues persist.\"\n\nStarbucks didn't respond to requests for comment on the study or on baristas' reports of labor practices that are failing to meet the company's stated goals.\n\nIn an internal memo this week [published by Time](http://time.com/4047359/starbucks-scheduling-labor-practices-memo/), Starbucks executive Cliff Burrows wrote that the company couldn't validate the survey, but added that \"the findings suggest, contrary to the expectations we have in place, that some partners are receiving their schedules less than one week in advance and that there is a continuing issue with some partners working a close and then an opening shift the following morning.\" He asks store managers \"to go the extra mile to ensure partners have a consistent schedule.\"\n\n[Starbucks ends \"race together\" campaign amid public backlash 00:26](https://www.cbsnews.com/video/starbucks-ends-race-together-campaign-amid-public-backlash/)\n\nTo be sure, some Starbucks workers are receiving at least 10 days notice on their work hours, with the survey finding that about one-third receive two weeks notice and another 18 percent get their schedules three weeks in advance. But that leaves almost half of workers who only receive one week's notice, making it more difficult from them manage other obligations, such as school, family commitments or other jobs.\n\nClopens remain a problem, as well. About 60 percent of workers who have to handle a clopen receive seven or fewer hours of rest between a closing and an opening shift, the study found.\n\nThat's prompted one former Starbucks employee to start a petition to end the practice of scheduling clopens. Ciara Moran noted in her petition that she sometimes was only able to get [four or five hours of sleep](https://www.coworker.org/petitions/starbucks-end-clopens-now) on the days she was scheduled for clopens. She said she quit her job because she doubted whether it was possible to get ahead given the demands on workers.\n\nEven if Starbucks stuck with its policy of providing eight hours between shifts, that's not enough time, especially given that many workers in the service sector have long commutes, the study said.\n\nAnother issue singled out by the report is Starbucks' practices on sick time. Since paid time off is only available to workers with at least a year on the job, about 40 percent of employees in the survey said they had dealt with barriers in taking sick days.\n\nIn a perfect world, Weisse said he'd like to receive his schedule either a month or a quarter in advance. Asked if he's upset about the last-minute schedules, Weisse said he had become resigned to it.\n\n\"I don't know if you've ever seen experiments with rats,\" he said. \"First they get upset if you treat them badly. Then they lay down and try not to move too much. I'm like that now. Things are terrible.\"", - "title": "​Is Starbucks shortchanging its baristas?", - "url": "https://www.cbsnews.com/news/is-starbucks-shortchanging-its-baristas", - "source_domain": "cbsnews.com", - "user": "1" - } - ] - } -} diff --git a/site/gatsby-site/cypress/fixtures/history/versionReports.json b/site/gatsby-site/cypress/fixtures/history/versionReports.json deleted file mode 100644 index 32540363a4..0000000000 --- a/site/gatsby-site/cypress/fixtures/history/versionReports.json +++ /dev/null @@ -1,44 +0,0 @@ -{ - "data": { - "reports": [ - { - "__typename": "Report", - "_id": "5d34b8c29ced494f010ed472", - "authors": ["Caroline O'Donovan"], - "cloudinary_id": "reports/img.buzzfeed.com/buzzfeed-static/static/2015-06/2/17/campaign_images/webdr02/kronos-makes-changes-to-software-accused-of-produ-2-14421-1433279634-2_dblbig.jpg", - "date_published": "2015-06-03", - "description": "Kronos, a workforce management company, thinks giving managers more data can make scheduling practices more fair. But ultimately, the underpinnings of the problem may go deeper than the algorit", - "epoch_date_submitted": 1559347200, - "image_url": "https://img.buzzfeed.com/buzzfeed-static/static/2015-06/2/17/campaign_images/webdr02/kronos-makes-changes-to-software-accused-of-produ-2-14421-1433279634-2_dblbig.jpg", - "inputs_outputs": null, - "language": "en", - "report_number": 3205, - "source_domain": "buzzfeednews.com", - "submitters": ["Catherine Olsson"], - "tags": [], - "text": "In April, the New York attorney general's office launched an [investigation](http://www.reuters.com/article/2015/04/13/us-retail-workers-nyag-idUSKBN0N40G420150413) into the scheduling practices of 13 national retail chains, distributing a letter to the Gap, Target, J.C. Penney, and 10 other companies. The letter asked, among other things, whether these companies' store managers use software manufactured by a company called Kronos to algorithmically generate schedules.\n\nA few months later, Kronos was also featured prominently in an [article](http://www.nytimes.com/interactive/2014/08/13/us/starbucks-workers-scheduling-hours.html) published by the _New York Times_ about the ill effects of erratic scheduling on Starbucks employees, especially one particular family. In a [follow-up piece](http://www.nytimes.com/times-insider/2014/08/22/times-article-changes-a-policy-fast/), the author, Jodi Kantor, points directly to Kronos' scheduling software as the root of the problem. \"I saw that her life was coming apart and that the Starbucks software had contributed to the crisis,\" Kantor wrote of one of the story's subjects.\n\nThe piece's argument centered around the financial and scheduling unpredictability engendered by platforms like Kronos. When you don't know if your shift might be canceled, if or when you'll be called in, or what your hours will look like next week or the week after, it becomes [very difficult](http://www.buzzfeed.com/sapna/victorias-secret-keeps-workers-on-call-and-unpaid#.wspVMPngB) to make even the most\n\nbasic plans for your future. This can have devastating long-term financial and emotional impacts on workers. According to a [recent study](http://www.epi.org/publication/irregular-work-scheduling-and-its-consequences/) by the Economic Policy Institute, a left-leaning think tank in Washington, D.C., 17 percent of the American workforce is negatively affected by unstable schedules.\n\nFor their part, Kronos representatives argue that the algorithm is far from the root of the problem. \"The populist view is that scheduling is evil, in that it's causing erratic schedules for employees, and so forth,\" Charlie DeWitt, vice president of business development for Kronos, told BuzzFeed News. \"The fact of the matter is it's an algorithm. It does whatever you want it to do.\"\n\nAnd you don't necessarily need to work for Kronos to believe that in a competitive retail climate, the problem is more complicated than technology alone. [Lonnie Golden](http://www.abington.psu.edu/academics/faculty/dr-lonnie-golden), a Penn State economist who has extensively studied the impact of erratic scheduling, acknowledges that Kronos' product itself is less to blame than the managers who make staffing decisions based on the data it provides. \"It's not necessarily the technology that's responsible for minimum to no advance notice,\" he said. \"It's the way in which it's applied.\"\n\nBut, he added, \"where there's a technology problem, there's usually a technology solution.\" And while Kronos maintains that managers, and not the software, are responsible for early dismissals and last-minute shift cancellations, the company is nonetheless pursuing some technological solutions.\n\nKronos wants to help managers better understand how scheduling adjustments affect workers and, ultimately, the bottom line. Though the company maintains that its software doesn't produce the kind of erratic schedules that hurt wage workers, DeWitt said there was nonetheless an interest in figuring out why that perception existed — and, if possible, fixing it.\n\nTo that end, earlier this month at a [retail conference in Philadelphia](http://www.kronos.com/microsites/RetailExecSummitSpring15/), the company announced that it's working on a new plug-in that will give managers better insight into workers' schedule stability, equity of hours worked among employees, and the consistency of schedules from week to week. In addition, Kronos is improving a feature meant to help give employees more control over their schedules: Though the software already incorporates employee availability and preferences into its scheduling calculations, improvements to a shift-swapping feature on its employee-facing web and mobile apps will theoretically allow employees to work around conflicts among themselves.\n\nGolden said increased employee input and control would be a good thing. But some retailers, DeWitt pointed out, are uncomfortable making workers use an app outside of work hours; indeed, the practice could be seen as a shift of management responsibilities onto lower-paid individuals.\n\nPart of the idea behind the new Kronos plug-in is to help companies tie fairer scheduling practices to reduction in absenteeism and turnover, which can be enormously costly. In other words, if Kronos can help executives see the connection between treating workers fairly and a store's ability to increase revenue, DeWitt said, managers will have an impetus to create more predictable, stable schedules.\n\nAnd just because companies are looking at this kind of data doesn't mean they have to use it. \"Companies like Kronos and Workplace Systems are starting to integrate some of these principles into their software,\" said [Carrie Gleason](http://populardemocracy.org/carrie-gleason), director of the Fair Workweek Initiative at the Center for Popular Democracy, \"but it's all optional, so companies can decide not to do it.\" While [12 states](http://populardemocracy.org/campaign/restoring-fair-workweek) are currently considering legislation that would create new labor standards around the workweek, Gleason said the technology alone lacks a mechanism for enforcement.\n\nGiven market pressures and standard management practices, it's unlikely that any change to Kronos' technology would give workers more power — especially because, given the [competitive retail climate](http://www.buzzfeed.com/sapna/retail-winter-of-death#.krGlE3qDm) at the moment, the bottom line tends to be the priority. \"It's not just bad managers. They have extreme pressure to increase productivity on an ever-shrinking labor budget,\" Gleason said.\n\nWith these changes, Kronos has taken logical steps toward both repairing its reputation and making sure its software creates sustainable work environments. But while the company cannot control exactly how the algorithm that forecasts schedules and optimizes workforces is deployed inside different workplaces, the Kronos engineers who designed the product are nonetheless the partial architects of work environments that have been proven to be untenable for low-wage workers. The Kronos scheduling algorithm isn't designed to serve those people; it's designed to be sold to their bosses, and as such, will ultimately be shaped to serve the needs of management — until regulations exist that compel them to change how it's used.", - "title": "Report 3205 title", - "url": "https://www.buzzfeednews.com/article/carolineodonovan/kronos-makes-changes-to-software-accused-of-producing-bad-sc" - }, - { - "__typename": "Report", - "_id": "5d34b8c29ced494f010ed471", - "authors": ["Alain Sherter"], - "cloudinary_id": "reports/cbsnews2.cbsistatic.com/hub/i/r/2014/09/25/aa2708c1-7e84-4573-8f79-add177a690cd/thumbnail/1200x630/2505bd91ab74f8e2ce3e7c6f8a5d77f3/starbucks.jpg", - "date_published": "2014-09-26", - "description": "Baristas at the coffee chain worry the company won't fulfill its promise to improve working conditions", - "epoch_date_submitted": 1559347200, - "image_url": "https://cbsnews2.cbsistatic.com/hub/i/r/2014/09/25/aa2708c1-7e84-4573-8f79-add177a690cd/thumbnail/1200x630/2505bd91ab74f8e2ce3e7c6f8a5d77f3/starbucks.jpg", - "inputs_outputs": null, - "language": "en", - "report_number": 3206, - "source_domain": "cbsnews.com", - "submitters": ["Catherine Olsson"], - "tags": [], - "text": "Liberte Locke, a 32-year-old \"barista\" at a Starbucks (SBUX) in New York City, is fed up.\n\n\"Starbucks' attitude is that there's always someone else who can do the job,\" she said in running through her complaints about life at the java giant.\n\nIf that isn't necessarily the consensus among Starbucks workers, interviews with nine current and former baristas at the company make clear it's not an isolated opinion, either. Even those who say they like their job paint a picture of a business that underpays front-line workers, enforces work rules arbitrarily, and too often fails to strike a balance between corporate goals and employee needs.\n\nOf course, such complaints are nothing new in retail, where low pay and erratic schedules are the norm. But by its own account, Starbucks is no ordinary company and is ostensibly a far cry from the fast-food outlets now facing a nationwide uprising by employees tired of working for peanuts.\n\nThat's evident in the company's recruitment pitch. Starbucks invites job-seekers to \"become a part of something bigger and inspire positive change in the world,\" describing it as a chance to discover a \"deep sense of purpose.\"\n\nDamage control\n\nThat image suffered a serious blow last month after The New York Times vividly chronicled a Starbucks worker struggling with the company's scheduling practices. The story, which centered on a 22-year-old barista and single mother, amounted to a public relations nightmare for Starbucks. Perhaps not coincidentally, within days of the story's publication top executives were promising reform.\n\nIn a memo to employees earlier this month, for instance, Chief Operating Officer Troy Alstead vowed to \"transform the U.S. partner experience,\" referring to Starbucks' more than 130,000 baristas. Inviting worker feedback, he said Starbucks will examine its approach to employee pay, revisit its dress code, make it easier for people to ask for time off, and consider other changes aimed at helping baristas balance work and their personal lives.\n\nAmong other changes, the company said it would end the practice of \"clopening,\" when an employee responsible for closing a store late at night is also assigned to open it early in the morning.\n\n\"We recognize that we can do more for our partners who wear the apron every day,\" he wrote.\n\nSome baristas did not feel this August memo from Starbucks went far enough in proposing ways to improve work conditions, so they marked it up with their own ideas. CBS\n\nAlthough Starbucks workers welcome this pledge to respect the apron, they fear the company is more intent on dousing the PR flames than on genuinely improving employees' experience. After the retailer last month sent an email to workers outlining possible solutions to the kind of scheduling problems and related issues detailed by the Times, a group of baristas gave the proposal a C- and posted online a marked-up version of the memo listing their own demands (see image above).\n\n\"We hope you're ready for a commitment to give us schedules that don't mess with taking care of kids, going to school or holding onto that second job we need because Sbux wages don't make ends meet,\" wrote the baristas, who are working with a union-backed labor group, the Center for Popular Democracy.\n\nRetail jungle\n\nDespite the recent media focus on Starbucks, the company's labor practices are generally no worse than those of many large retailers. In some ways they're better, with the company offering health care to part-time, as well as full-time, workers; unusually generous 401(k) matching contributions; annual stock grants to employees; and tuition reimbursement.\n\nStarbucks highlights such benefits as an example of its commitment to employees. \"Sharing success with one another has been core to the company's heritage for more than 40 years,\" Alstead said in the September memo.\n\nMeanwhile, some baristas say they enjoy their work and feel valued by Starbucks. \"It's a decent place to work, and my manager and co-workers are great,\" said one employee who asked not to be identified.\n\nBut other current and former workers claim Starbucks has changed in recent years, saying that corporate leaders' intense focus on slashing costs has short-circuited its professed commitment to workers. Mostly, they say Starbucks doesn't listen to employees and even punishes those who identify problems.\n\n\"The biggest problem is that baristas don't have a voice,\" said Sarah Madden, a former Starbucks barista who left the company this spring after two years with the coffee vendor. \"They can't speak to issues that they know exist. Workers know how to fix them, but when [they] speak up there are serious repercussions -- your hours get cut, you're transferred to another store or isolated from other people.\"\n\nEmployees interviewed for this article said one result of Starbucks' cost-containment push is that stores are frequently understaffed, hurting customer service and forcing managers to scramble to find staff. That problem is common across the big-box s", - "title": "Report 3206 title", - "url": "https://www.cbsnews.com/news/for-some-starbucks-workers-job-leaves-bitter-taste/" - } - ] - } -} diff --git a/site/gatsby-site/cypress/fixtures/incidents/fullIncident10.json b/site/gatsby-site/cypress/fixtures/incidents/fullIncident10.json deleted file mode 100644 index 89638145c7..0000000000 --- a/site/gatsby-site/cypress/fixtures/incidents/fullIncident10.json +++ /dev/null @@ -1,582 +0,0 @@ -{ - "data": { - "incident": { - "AllegedDeployerOfAISystem": [ - { - "entity_id": "boeing", - "name": "Boeing" - } - ], - "AllegedDeveloperOfAISystem": [ - { - "entity_id": "boeing", - "name": "Boeing" - } - ], - "AllegedHarmedOrNearlyHarmedParties": [ - { - "entity_id": "airplane-passengers", - "name": "Airplane Passengers" - }, - { - "entity_id": "airplane-crew", - "name": "Airplane Crew" - } - ], - "date": "2018-10-27", - "description": "A Boeing 737 crashed into the sea, killing 189 people, after faulty sensor data caused an automated manuevering system to repeatedly push the plane's nose downward.", - 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Perez"], - "cloudinary_id": "reports/placeholder.svg", - "date_published": "2019-03-14", - "description": "Present day Air travel is one of the safest modes of travel. Statistics from the US Department of Transportation show that in 2007 and 2016 there were 11 fatalities per trillion miles of commercial ai", - "epoch_date_submitted": 1607299200, - "image_url": "placeholder.svg", - "inputs_outputs": null, - "language": "en", - "report_number": 1342, - "source_domain": "medium.com", - "submitters": ["Ingrid Dickinson (CSET)"], - "tags": [], - "text": "Present day Air travel is one of the safest modes of travel. Statistics from the US Department of Transportation show that in 2007 and 2016 there were 11 fatalities per trillion miles of commercial air travel. This is in stark contrasts to the 7,864 fatalities per trillion miles of travel on the highway ( You can check the statistics here: fatalities and miles of travel per mode of transport). Incremental improvements to air travel is a marvel of technical innovation. However, when an aircraft accident does occur, we are forced to take notice due to the magnitude of a single event.\n\nAir travel today is at a level of technical maturity that when a plane crashes by accident (i.e not due to man-made causes like terrorism or misfiring of missiles), then it is surprisingly due not to pilot error or physical equipment failure but rather because of a computer error. That is, an aircraft accident is caused by a software bug.\n\nEveryone today is intimately familiar with software bugs. Microsoft blue screen of death and the use of ctrl-alt-delete have been burned into our experiences. Even in better designed operating systems that we find in smartphones, it’s is not uncommon to force a reboot. This is much less common that we often have to look up the procedure, but it does happen nevertheless.\n\nSoftware is notoriously difficult to make bug free. It is the nature of the beast. This is because, to build bug-free software systems, we need to explicitly list all the scenarios that can go wrong and how, and then test our software for those conditions. Unfortunately, that list tends to be unbounded if our designs don’t restrict the scope of a software’s applicability. In short, software developers are able to manage the unbounded complexity by narrowing the scope of applicability. That is why even the most sophisticated “artificial intelligent” applications work well in the most narrow of areas. It is very easy to get frustrated by the limitations of voice assistants like Alexa. That’s because AI technology has not reached the level of maturity that is required for open-ended general conversation. In short, bug-free depends fundamentally on a narrow scope of application and extensive testing within this narrow scope.\n\nAs we build more sophisticated software that has higher degrees of complexity, we need to understand the scope of an application and an ever-increasing scope demands more on the testing of these systems. Thus to understand this complexity better, we need to understand the kinds of automation we are building.\n\nAs I mentioned earlier, the USDOT shows that there were over 37,000 fatalities in highway accidents in 2017 alone. Thus it makes logical sense to understand how automation affects the safety of road vehicles. The Society of Automation Engineering (SAE) has an international standard which defines six levels of driving automation (SAE J3016). This is a useful framework for classifying the levels of automation in domains outside that of cars. A broader prescription is as follows:\n\nLevel 0 (Manual Process)\n\nThe absence of any automation.\n\nLevel 1 (Attended Process)\n\nUsers are aware of the initiation and completion of the performance of each automated task. The user may undo a task in the event of incorrect execution. Users, however, are responsible for the correct sequencing of tasks.\n\nLevel 2 (Attended Multiple Processes)\n\nUsers are aware of the initiation and completion of a composite of tasks. The user, however, is not responsible for the correct sequencing of tasks. An example will be the booking of a hotel, car, and flight. The exact ordering of the booking may not be a concern of the user. However, failure of the performance of this task may require more extensive manual remedial actions. An unfortunate example of a failed remedial action is the re-accommodation of United Airlines’ paying customer.\n\nLevel 3 (Unattended Process)\n\nUsers are only notified in exceptional situations and are required to do the work in these conditions. An example of this is in systems that continuously monitor the security of a network. Practitioners take action depending on the severity of an event.\n\nLevel 4 (Intelligent Process)\n\nUsers are responsible for defining the end goals of automation, however, all aspects of the process execution, as well as the handling of in-flight exceptional conditions, are handled by the automation. The automation is capable of performing appropriate compensating action in events of in-flight failure. The user however is still responsible for identifying the specific context in which automation can be safely applied to.\n\nLevel 5 (Fully Automated Process)\n\nThis is a final and future state where human involvement is no longer required in the processes. This, of course, may not be the final level because it does not assume that the process is capable of optimizing itself to make improvements.\n\nLevel 6 (Self Optimizing Process)\n\nThis is automation that requires no human involvement and is also capable of improving itself over time. This level goes beyond the SAE requirements but may be required in certain high-performance competitive environments such as Robocar races and stock trading.\n\nThe automobiles of today have extremely sophisticated software that controls many parts of the functioning of the system. This software works at many levels and at each level the risks are different. Some software works at an extremely narrow scope that we are unaware that it is operating. So for example, a car’s fuel injection system is, in fact, fully automated. We can say this about many of the functions of a car that deals with its engine performance. So for example, many car enthusiasts buy programmers and chips that provide after-market tweaks on a car’s performance characteristics. Failure of any of these kinds of systems can still be fatal. SAE’s standards described above however apply to driving automation and not engine automation. There is a stark difference in automation that affect steering and automation that maintains the smooth running of engines.\n\nAutomation such as traction control or car stabilization does affect steering. These are engaged in exceptional narrow conditions to ensure greater passenger safety. Controlled behavior is injected in a situation so that a driver can gain better control of a vehicle that he otherwise could not have done so himself. In this context, a driver is actually momentarily not controlling the car.\n\nThere have been many cases of planes falling from the skies due to software bugs. My earliest memory of this kind of a catastrophe is Lauda Air Flight 004 in May 1991. This is when one of the engines reverse trusters engaged in mid-flight forcing the plane to spiral out of control and crash. There was no official conclusion as to the cause, however, the aviation writer Macarthur Job said that “had that Boeing 767 been of an earlier version of the type, fitted with engines that were controlled mechanically rather than electronically, then that accident could not have happened.”\n\nMore recently, there is the case of Air France 447 in 2009. The official conclusion was that there was a “temporary inconsistency between the measured speeds, likely as a result of the obstruction of the pitot tubes by ice crystals, causing autopilot disconnection and reconfiguration.” The verdict was that the human pilots were eventually part of the fault due to their inability to react appropriately to the anomalous situation. To say this differently, the pilots received incorrect information from the instrumentation and thus took inappropriate action to stabilize the plane.\n\nThere are other cases of computer caused failures. Qantas flight 72, it was determined that the CPU of the air data inertial reference unit (ADIRU) corrupted the angle of attack (AOA) data. Malaysia Air 124 that plunged 200 feet in midflight. The instrumentation displayed that the plane was “going too fast and too slow simultaneously”.\n\nIn general, it is the responsibility for the pilots to properly perform compensating actions in the case of equipment failure (known as alternate law). The point though is that computer error due to equipment failure should be no different from regular equipment error and it is the responsibility of the pilots to take appropriate measures. Typically, on equipment error, the autopilot is disengaged and the plane is to be flown manually. This is Level 3 (unattended process) automation where the scope when automation is in play is explicit. In Level 3, a pilot is made aware of an exceptional condition and takes manual control of the plane.\n\nIn Level 4 (intelligent process), a pilot must be able to recognize the exceptional condition and is able to specify when automation is applicable. Today, we have self-driving cars that are deployed in narrow applications. We have cars that can self-park and we have cars that can drive in good weather conditions on the highway. These are Level 4 automation where is up to the judgment of the driver to engage the automation. Autopilot in planes are Level 4 automation and is engaged in contexts of low complexity.\n\nThen there is the case of Boeing’s 737 Max 8’s MCAS. This I will argue is a Level 5 automation, this is a fully automated process wherein it is expected to function in all scenarios. Like electronics that control engine performance, fully automated processes aren’t generally problematic, however, when you involve driving (or steering for planes) then it opens up the question of the maturity of this level of automation.\n\nAirbus has what is called ‘Alpha Protection’:\n\n“Alpha protection” software is built into every Airbus aircraft to prevent the aircraft from exceeding its performance limits in angle of attack, and kicks in automatically when those limits are reached.\n\nFrom the definition, Alpha protection is automation that is always measuring, however, it isn’t always active. It is like a speed limiter that exits in cars today, it is constantly measuring, but is activated only when measurements exceed thresholds. However, what happens when the measurements are incorrect due to faulty sensors? One could argue that this might have been what happened to Air France 447. That is, the automation became active when the pilots did not expect it. Faulty sensors are always problematic, but faulty sensors that can trigger automated behavior can be extremely dangerous.\n\nThe Boeing 737 Max 8 has a system known as Maneuvering Characteristics Augmentation System (MCAS). The business motivation behind MCAS is itself quite revealing. Apparently, it is analogous to a software patch that attempts to fix a physical flaw of the aircraft. The Boeing 737 aircraft, introduced in 1968, is an extremely mature and reliable aircraft. The 737 is the best selling aircraft in the world, selling over 10,000 aircraft since its inception. It is has been favored by many short-haul budget airlines that have risen in the past decade. Its main competitor in the Airbus A320, where over 8,000 planes have been delivered since its inception in 1988.\n\nIn 2008, a joint American-French company CFM launched a more fuel and cost-efficient engine known as the Leap engine. Airbus fitted their new planes (Airbus A320neo) with this new engine. The reason behind the economy of the Leap engine is due to its much larger air intake diameter.\n\nTo be competitive, the Max 8, was retrofitted also with this new engine. However, unlike the A320neo, there was not enough ground clearance for the Leap engine. To compensate for this problem, Boeing reduced the distance between the engine and the underside of the wing. This, however, had the effect of changing the center of mass of the plane. The Max 8 now had the dynamic tendency of raising its nose and as a consequence increasing the risk of a stall.\n\nTo paper over this tendency, Boeing developed MCAS. The purpose of MCAS is that it is software dedicated to compensating for this flaw:\n\nBoeing engineers, in turn, came up with another makeshift solution. They developed a software that would work in the background. As soon as the nose of the aircraft pointed upward too steeply, the system would automatically activate the tailplane and bring the aircraft back to a safe cruising plane. The pilots wouldn’t even notice the software’s intervention — at least that was the idea.\n\nEmploying software to paper over a plane’s natural instability is not new. Many of the more advanced fighter jets are designed to be unstable to ensure greater maneuverability. The fighter pilots are also trained to anticipate the peculiar flight characteristics of their planes. In contrast, there have been many complaints that pilots of the Max 8 were not properly informed of the existence of the MCAS system:\n\n“There are 1,400 pages and only one mention of the infamous Maneuvering Characteristics Augmentation System (MCAS) … in the abbreviations sections. But the manual does not include an explanation of what it is…”\n\nPerhaps Boeing determined that information about this system wasn’t worth attention by pilots. After all, the intention of the MCAS system was to make the 737 Max 8 to give the same “feel” as the previous model the 737 NG. This is what we call in software circles as virtualization. That is, this is software that renders a virtual machine on the pilot’s user interface to the plane so it feels and acts like another kind of plane (i.e. one that is structurally balanced).\n\nThere is a “law” in software development knows as “The Law of Leaky Abstractions” which states “All non-trivial abstractions, to some degree, are leaky.” MCAS is perhaps a leaky abstraction, that is, it tries to creates a virtual abstraction of a legacy 737 NG without Leap engines, to hide an unbalanced airplane. Surely, nothing can leak with this kind of abstraction? It is one thing to abstract away virtual machinery and its entirely another thing to attempt to abstract away physical reality. However, in both cases, something will eventually leak through.\n\nSo does the MCAS behave when its abstractions begin to leak? Here is what is reported by pilots of the plane:\n\n“On both the NG and the MAX, when you have a runaway trim stab this can be stopped temporarily by pulling the control column in the opposite direction. But when the MCAS is activated because of a high angle of attack, this can only be stopped by cutting the electrical trim motor.”\n\nHow a pilot responds to an abstraction leak can be very different from that of the real thing it is trying to abstract. With faulty sensors, one can turn this off and use one’s understanding of the situation and the plane to make good decisions. However, when one’s understanding of the nature of the plane is virtual and not real, then you just can’t revert to reality. Reality is outside of the pilot’s comprehension and thus a cause to inproper decision making. A virtual trashcan works like a regular trash can in that you can still recover the documents you place in the trash before it is emptied. Reality however is very different than the virtual world, many times there is no undo function!\n\nThen there’s this leaky abstraction when the plane itself has exhibited its own intentions:\n\nBut the EFS never acts by itself, so we were astounded when we heard what the real reason was. (…) However, in some cases — as happened on Flight 610 — the MCAS moves by itself.\n\nand this:\n\nMCAS is activated without pilot input and only operates in manual, flaps up flight.\n\nThis is because, a virtual abstraction of a real plane, is the same as Level 5 automation! If MCAS is turned off, the pilots will find themselves to be flying an entirely different plane. When you abstract away interaction with reality, you cannot avoid introducing a process that mediates between a pilot’s action and the actual actions of the plane. The behavior of the real plane will depend on the environment that it is in. The behavior of a virtual plane will depend on just the working sensors that are available to render the virtual simulation. Level 5 automation requires a kind of intelligence that is aware of what sensors are faulty and furthermore is able to navigate a problem with partial and unobserved information. The smarts to enable this kind of Artificial Intelligence is simply not available in our current state of technological development.\n\nIn short, Boeing has decided to implement technology that is simply too ambitious. Not all software has the same level of complexity. This is not an issue of insufficient testing to uncover logical flaws in the software. This is not an issue of robustly handling sensor and equipment failure. This is an issue of attempting to implement an overly ambitious and thus a dangerous solution.\n\nAir travel is extremely reliable, but introducing software patches as a means to virtualize physical behavior can lead to unintended consequences. The reason that we still fly planes with pilots in them is that we expect pilots to be able to solve unexpected situations that automation cannot handle. MCAS like virtualization, handcuffs pilots from differentiating between the real and the simulated. I would thus recommend to regulators that in the future, MCAS like virtualization should be treated and tested very differently from other automation. They should be treated as Level 5 automation with a more exhaustive level of scrutiny.\n\n", - "title": "AI Safety, Leaking Abstractions and Boeing’s 737 Max 8", - "url": "https://medium.com/intuitionmachine/ai-safety-leaking-abstractions-and-boeings-737-max-8-5d4b3b9bf0c3" - }, - { - "authors": ["Niniek Karmini", "David Koenig"], - "cloudinary_id": "reports/www.courthousenews.com/wp-content/uploads/2018/11/AP18332052000971-300x206.jpg", - "date_published": "2018-11-29", - "description": "JAKARTA, Indonesia (AP) — Pilots fought against an automated system that pitched a Boeing jetliner’s nose down repeatedly because of a faulty sensor until they finally lost control and plunged into the Java Sea last month, Indonesian investigators said Wednesday.\nAt a news conference, safety officials said they were still struggling to understand why the plane crashed, killing all 189 people on board.\n\nThe National Transportation Safety Commission’s Nurcahyo Utomo said investigators were trying to figure out from interviews with engineers why they certified that the Boeing 737 MAX 8 was airworthy and whether they had followed required maintenance procedures. Pilots of previous flights had reported problems with control systems on the brand-new jet.", - "epoch_date_submitted": 1559347200, - "image_url": "http://www.courthousenews.com/wp-content/uploads/2018/11/AP18332052000971-300x206.jpg", - "inputs_outputs": null, - "language": "en", - "report_number": 389, - "source_domain": "courthousenews.com", - "submitters": ["Catherine Olsson"], - "tags": [], - "text": "JAKARTA, Indonesia (AP) — Pilots fought against an automated system that pitched a Boeing jetliner’s nose down repeatedly because of a faulty sensor until they finally lost control and plunged into the Java Sea last month, Indonesian investigators said Wednesday.\n\nAt a news conference, safety officials said they were still struggling to understand why the plane crashed, killing all 189 people on board.\n\nThe National Transportation Safety Commission’s Nurcahyo Utomo said investigators were trying to figure out from interviews with engineers why they certified that the Boeing 737 MAX 8 was airworthy and whether they had followed required maintenance procedures. Pilots of previous flights had reported problems with control systems on the brand-new jet.\n\nThe board issued a preliminary report that stopped short of declaring a probable cause of the crash — the investigation is continuing — but it provided new details about the pilots’ struggle to fly the highly automated jet and Lion Air’s inability to fix problems with sensors on the plane.\n\nSensors that measure speed were flushed and checked, and an electrical plug was cleaned before the fatal flight. Mechanics, however, did not check a sensor that measures whether the nose of the plane is pointing up or down.\n\nThat “angle of attack” sensor gave faulty readings throughout the short flight, triggering a system that automatically pointed the plane’s nose down more than two dozen times, with pilots responding by manually fighting to correct the pitch. Pilots even asked air traffic controllers to tell them how fast and high they were flying.\n\nThe malfunctions and warnings from the plane’s control system appeared to overwhelm the pilots almost as soon as the jet became airborne, said another investigator, Ony Suryo Wibowo.\n\n“The problem is if multiple malfunctions occur all at once, which one should be prioritized?” Wibowo said.\n\nIn a statement following release of the report, U.S.-based Boeing declared that the MAX, its newest plane, is safe. The manufacturer played up the possibility of pilot error.\n\nBoeing noted that the crew of the plane’s previous flight one day earlier had responded correctly to the automatic nose-down pitch and flew the plane manually. They also ran safety checklists. The preliminary report does not say whether pilots on the deadly flight took those steps, Boeing pointed out.\n\nBoeing has said that the procedure to correct an automatic nose-down pitch is in the plane’s operating manual and pilots should have known about it.\n\nSearchers have not yet recovered the plane’s cockpit voice recorder, which could tell investigators what the pilots were doing — or failing to do — to regain control of the plane during the brief, erratic flight.\n\nThe report by Indonesia’s safety commission repeated earlier recommendations made just after the disaster that pilots be better versed in emergency procedures and aware of past aircraft problems. They recommended that Lion Air, a fast-growing low-cost airline based in Jakarta, ensure that pilots follow proper procedures “to improve the safety culture.”\n\nMary Schiavo, a former inspector general of the U.S. Department of Transportation, said the preliminary report offered a road map of final recommendations that are likely to emerge from the investigation.\n\n“They will be looking for more precise reporting of problems (by pilots), and certainly a better maintenance response,” she said.\n\nPeter Lemme, an expert in aviation and satellite communications and a former Boeing engineer who wrote an analysis of the data on his blog, compared the scene in the cockpit to “a deadly game of tag” in which the plane pointed down, the pilots countered by manually aiming the nose higher, only for the sequence to repeat about five seconds later.\n\nThat happened 26 times during the 11-minute flight, but pilots failed to recognize what was happening and follow the known procedure for countering incorrect activation of the automated safety system, Lemme told The Associated Press.\n\nLemme said he was troubled that there weren’t easy checks to see if sensor information was correct, that the crew of the fatal flight apparently wasn’t warned about the problems on previous flights and that the Lion Air jet wasn’t fully repaired after those flights.\n\n“Had they fixed the airplane, we would not have had the accident,” he said. “Every accident is a combination of events, so there is disappointment all around here,” he said.\n\nThe U.S. National Transportation Safety Board and Boeing experts are helping the Indonesian investigators.\n\nBoeing has a great deal at stake in defending its plane.\n\nMore than 200 MAX jets have been delivered to airlines around the world. Pilots at American Airlines and Southwest Airlines complained this month that they had not been given all information about the new automated anti-stall safety system on the MAX.\n\nBoeing shares fell 14 percent in the last three weeks through Tuesday, as investigators focused on t", - "title": "Report Faults Safety Failures, Defects in Lion Air Crash", - "url": "https://www.courthousenews.com/report-faults-safety-failures-defects-in-lion-air-crash/" - }, - { - "authors": ["Niniek Karmini", "David Koenig"], - "cloudinary_id": "reports/storage.googleapis.com/afs-prod/media/media:2f65d418951d44d59e9c68c92123f4d7/2853.jpeg", - "date_published": "2018-11-29", - "description": "JAKARTA, Indonesia (AP) — Pilots fought against an automated system that pitched a Boeing jetliner's nose down repeatedly because of a faulty sensor until they finally lost control and plunged into the Java Sea last month, Indonesian investigators said Wednesday. At a news conference, safety officials said they were still struggling to understand why the plane crashed, killing all 189 people on board. The National Transportation Safety Commission's Nurcahyo Utomo said investigators were trying to figure out from interviews with engineers why they certified that the Boeing 737 MAX 8 was airworthy and whether they had followed required maintenance procedures. Pilots of previous flights had reported problems with control systems on the brand-new jet.", - "epoch_date_submitted": 1559347200, - "image_url": "https://storage.googleapis.com/afs-prod/media/media:2f65d418951d44d59e9c68c92123f4d7/2853.jpeg", - "inputs_outputs": null, - "language": "en", - "report_number": 388, - "source_domain": "apnews.com", - "submitters": ["Catherine Olsson"], - "tags": [], - "text": "National Transportation Safety Committee investigator Nurcahyo Utomo holds a model of an airplane during a press conference on the committee's preliminary findings on their investigation on the crash of Lion Air flight 610, in Jakarta, Indonesia, Wednesday, Nov. 28, 2018. Black box data collected from their crashed Boeing 737 MAX 8 show Lion Air pilots struggled to maintain control as the aircraft's automatic safety system repeatedly pushed the plane's nose down, according to a preliminary investigation into last month's disaster. (AP Photo/Achmad Ibrahim)\n\nJAKARTA, Indonesia (AP) — Pilots fought against an automated system that pitched a Boeing jetliner’s nose down repeatedly because of a faulty sensor until they finally lost control and plunged into the Java Sea last month, Indonesian investigators said Wednesday.\n\nAt a news conference, safety officials said they were still struggling to understand why the plane crashed, killing all 189 people on board.\n\nThe National Transportation Safety Commission’s Nurcahyo Utomo said investigators were trying to figure out from interviews with engineers why they certified that the Boeing 737 MAX 8 was airworthy and whether they had followed required maintenance procedures. Pilots of previous flights had reported problems with control systems on the brand-new jet.\n\nThe board issued a preliminary report that stopped short of placing blame for the crash — the investigation is continuing — but it provided new details about the pilots’ struggle to fly the highly automated jet and Lion Air’s inability to fix problems with sensors on the plane.\n\nSensors that measure speed were flushed and checked, and an electrical plug was cleaned before the fatal flight. Mechanics, however, did not check sensors that measure whether the nose of the plane is pointing up or down.\n\nAn “angle of attack” sensor gave faulty readings throughout the short flight, triggering a system that automatically pointed the plane’s nose down more than two dozen times, with pilots responding by manually fighting to correct the pitch. Pilots even asked air traffic controllers to tell them how fast and high they were flying.\n\nThe malfunctions and warnings from the plane’s control system appeared to overwhelm the pilots almost as soon as the jet became airborne, said another investigator, Ony Suryo Wibowo.\n\n“The problem is if multiple malfunctions occur all at once, which one should be prioritized?” Wibowo said.\n\nIn a statement following release of the report, U.S.-based Boeing declared that the MAX, its newest plane, is safe. The manufacturer played up the possibility of pilot error.\n\nBoeing noted that the crew of the plane’s previous flight one day earlier had responded correctly to the automatic nose-down pitch and flew the plane manually. They also ran safety checklists. The preliminary report does not say whether pilots on the deadly flight took those steps, Boeing pointed out.\n\nBoeing has said that the procedure to correct an automatic nose-down pitch is in the plane’s operating manual and pilots should have known about it.\n\nSeveral experts said, however, that Boeing likely will have to consider changes in the new anti-stall system, perhaps developing an algorithm to disregard sensor readings that appear off-base.\n\nThe report offered new details on persistent problems with sensors on the Lion Air jet and the airline’s efforts to fix them.\n\nJohn Cox, a safety consultant and former airline pilot, said Lion Air should have taken the troubled plane on a maintenance test flight.\n\n“I don’t think the airplane was ready for passenger service because they had not validated that they had fixed the problem,” he said.\n\nSearchers have not yet recovered the plane’s cockpit voice recorder, which could tell investigators what the pilots were doing — or failing to do — to regain control of the plane during the brief, erratic flight.\n\nThe report by Indonesia’s safety commission did not draw conclusions about why the crew lost control of the plane, but it repeated earlier recommendations that pilots be better versed in emergency procedures and aware of past aircraft problems. They recommended that Lion Air, a fast-growing low-cost airline based in Jakarta, ensures that pilots follow proper procedures “to improve the safety culture.”\n\nMary Schiavo, a former inspector general of the U.S. Department of Transportation, said the preliminary report offered a road map of final recommendations that are likely to emerge from the investigation.\n\n“They will be looking for more precise reporting of problems (by pilots), and certainly a better maintenance response,” she said.\n\nPeter Lemme, an expert in aviation and satellite communications and a former Boeing engineer who wrote an analysis of the data on his blog, compared the scene in the cockpit to “a deadly game of tag” in which the plane pointed down, the pilots countered by manually aiming the nose higher, only for the sequence to repeat about five seconds later.\n\nThat happened 26 times during the 11-mi", - "title": "Report faults safety failures, defects in Lion Air crash", - "url": "https://www.apnews.com/4cf97e9b0a004cf5a0db401bb1d222e1" - }, - { - "authors": ["Helen Regan", "Masrur Jamaluddin"], - "cloudinary_id": "reports/cdn.cnn.com/cnnnext/dam/assets/181107144739-01-lion-air-1106-super-tease.jpg", - "date_published": "2018-11-08", - "description": "Problems were reported on a Lion Air jet that crashed into the sea off Jakarta even after technicians replaced a sensor on board the aircraft, investigators said.", - "epoch_date_submitted": 1559347200, - "image_url": "https://cdn.cnn.com/cnnnext/dam/assets/181107144739-01-lion-air-1106-super-tease.jpg", - "inputs_outputs": null, - "language": "en", - "report_number": 387, - "source_domain": "edition.cnn.com", - "submitters": ["Catherine Olsson"], - "tags": [], - "text": "(CNN) Problems were reported on a Lion Air jet that crashed into the sea off Jakarta even after technicians replaced a sensor on board the aircraft, investigators said.\n\nIndonesian authorities confirmed Wednesday that the angle of attack (AOA) sensor was replaced after a flight from Manado, in North Sulawesi to Denpasar, Bali on October 28. The Boeing 737 MAX 8 then made another flight to Jakarta that same day, and the pilots reported further problems.\n\nAll 189 people on board Flight 610 died when the new Boeing 737 MAX 8 crashed into the sea on October 29, 13 minutes after taking off from Jakarta on a short flight to Pangkal Pinang on the Indonesian island of Bangka.\n\nInvestigators said the jet experienced problems on its last four flights -- including, crucially, the flight that crashed, according to Soerjanto Tjahjono, the head of the National Transportation Safety Committee (KNKT).\n\nBoeing released an operational bulletin on Wednesday, warning all airlines about how to address any erroneous readings related to the AOA sensor. The Federal Aviation Administration (FAA) later issued its own directive that advised pilots about how to respond to similar problems.\n\nSearch for voice recorder\n\nAlmost two weeks after the crash, authorities are still searching for the plane's cockpit voice recorder (CVR), which is believed to be buried under deep mud. If found, it should reveal what happened in the cockpit in the final seconds of the flight.\n\nInvestigators are already examining the flight data recorder that was pulled off the sea bed, some 30 meters under water, on November 1.\n\nPhotos: In photos: Lion Air plane crashes off Indonesia A relative of one of the crash victims tosses flower petals from an Indonesian Navy ship on Tuesday, November 6. Hide Caption 1 of 27 Photos: In photos: Lion Air plane crashes off Indonesia Victims' families and colleagues react on a Navy ship as they're ferried to the crash site on November 6. Hide Caption 2 of 27 Photos: In photos: Lion Air plane crashes off Indonesia An Indonesian official examines a turbine engine from the plane on Sunday, November 4. Hide Caption 3 of 27 Photos: In photos: Lion Air plane crashes off Indonesia People in Jakarta grieve over the coffin of a relative on November 4. Hide Caption 4 of 27 Photos: In photos: Lion Air plane crashes off Indonesia Indonesian Navy divers recover a flight data recorder on Thursday, November 1. Hide Caption 5 of 27 Photos: In photos: Lion Air plane crashes off Indonesia Families of the victims look through personal items that were recovered from the wreckage. Hide Caption 6 of 27 Photos: In photos: Lion Air plane crashes off Indonesia A police officer arranges shoes recovered during search operations. Hide Caption 7 of 27 Photos: In photos: Lion Air plane crashes off Indonesia A Navy ship arrives at a search area in the waters of Karawang. Hide Caption 8 of 27 Photos: In photos: Lion Air plane crashes off Indonesia A relative of a passenger cries at a Jakarta hospital on Tuesday, October 30. Family members have been providing authorities with DNA samples to help identify victims of the crash. Hide Caption 9 of 27 Photos: In photos: Lion Air plane crashes off Indonesia Officials transport a body bag in Jakarta. Hide Caption 10 of 27 Photos: In photos: Lion Air plane crashes off Indonesia Indonesian President Joko Widodo inspects debris. Hide Caption 11 of 27 Photos: In photos: Lion Air plane crashes off Indonesia Victims' relatives embrace at a police hospital in Jakarta. Hide Caption 12 of 27 Photos: In photos: Lion Air plane crashes off Indonesia Soldiers drag ashore an inflatable raft containing debris from the plane. Hide Caption 13 of 27 Photos: In photos: Lion Air plane crashes off Indonesia A wallet is seen in the water where the plane went down. Hide Caption 14 of 27 Photos: In photos: Lion Air plane crashes off Indonesia A relative of a passenger cries at a hospital in Jakarta. Hide Caption 15 of 27 Photos: In photos: Lion Air plane crashes off Indonesia President Widodo (front row, second from right) tours the operations center in Jakarta where debris is laid out. Hide Caption 16 of 27 Photos: In photos: Lion Air plane crashes off Indonesia Police work to identify personal belongings that are believed to be from the plane's wreckage. Hide Caption 17 of 27 Photos: In photos: Lion Air plane crashes off Indonesia A police officer studies a map in the search-and-rescue command center. Hide Caption 18 of 27 Photos: In photos: Lion Air plane crashes off Indonesia A diver with the Indonesian Navy enters the water off the north coast of Karawang. Hide Caption 19 of 27 Photos: In photos: Lion Air plane crashes off Indonesia Rescue workers carry a body that was recovered from the waters near Jakarta on October 29. Hide Caption 20 of 27 Photos: In photos: Lion Air plane crashes off Indonesia A woman in Pangkal Pinang prays as she and others wait for news on October 29. Hide Caption 21 of 27 Photos: In photos: Lion Air plane crashes off ", - "title": "Lion Air: Sensor was replaced day before crash but problems persisted", - "url": "https://edition.cnn.com/2018/11/08/asia/lion-air-sensor-replaced-intl/index.html" - }, - { - "authors": ["Barbara S. Peterson"], - "cloudinary_id": "reports/hips.hearstapps.com/hmg-prod.s3.amazonaws.com/images/investigators-examine-engine-parts-from-the-ill-fated-lion-news-photo-1058470458-1541688511.jpg?crop=1.00xw:0.733xh;0,0.0375xh&resize=1200:*", - "date_published": "2018-11-08", - "description": "Mounting evidence that something was very wrong with the sensors on board the Boeing 737 MAX and the flight information being sent to the pilots.", - "epoch_date_submitted": 1559347200, - "image_url": "https://hips.hearstapps.com/hmg-prod.s3.amazonaws.com/images/investigators-examine-engine-parts-from-the-ill-fated-lion-news-photo-1058470458-1541688511.jpg?crop=1.00xw:0.733xh;0,0.0375xh&resize=1200:*", - "inputs_outputs": null, - "language": "en", - "report_number": 386, - "source_domain": "popularmechanics.com", - "submitters": ["Catherine Olsson"], - "tags": [], - "text": "The FAA has issued an emergency directive to anyone flying the Boeing 737 MAX, the type of plane that crashed in the Lion Air Flight JH 610 incident, related to the faulty sensors that reportedly fed bad information to the pilots. Meanwhile, investigators have reported that the plane received a replacement angle-of-attack sensor (a system that measures whether the plane's nose is too high relative to the current of air) the day before the deadly crash.\n\nAs Bloomberg reported earlier this week, Boeing issued a bulletin to 737 MAX operators warning them that faulty inputs from airflow sensors could have contributed to the Lion Air crash. The Boeing statement gave few details, saying that “the investigation is ongoing.” The FAA's directive backs up that bulletin by giving anyone who flies the 737 MAX three days, starting Wednesday, to revise their airplane flight manual for that model.\n\nThe FAA said it was taking the action to “address possible erroneous angle of attack inputs” that could put the plane in a sudden dive. The FAA said airline operators need to revise the manual “to give crew Horizontal stabilizer trim procedures to follow under certain conditions.\" In the U.S., the order affects American, Southwest, and United, which together operate 45 MAX jets.\n\nHere's the official statement:\n\n“Boeing has released a Flight Crew Operations Manual Bulletin regarding the potential for erroneous angle of attack input on 737 Max aircraft. The FAA plans to mandate the Flight Crew Operations Manual Bulletin by issuing an Airworthiness Directive (AD). The FAA continues to work closely with Boeing, and as a part of the investigative team on the Indonesia Lion Air accident, will take further appropriate actions depending on the results of the investigation. The FAA has alerted affected domestic carriers and foreign airworthiness authorities who oversee air carriers that use the 737 MAX of the agency’s forthcoming action.\"\n\nThe new revelation—that the doomed 737 received a new sensor after a flight on October 28, the day before the deadly crash—adds to the growing evidence of a serious problem with the sensors and flight data being fed to the crew of Lion Air 610. Data downloaded from the flight data recorder, which was recovered by divers searching the underwater crash site, have revealed a series of glitches in three flights prior to the crash that gave flawed airspeed and other information to the cockpit.\n\nIf AOA sensors fail, it can cause the plane’s computers to incorrectly show the plane is heading into an aerodynamic stall—which, in turn, can put the jet into a sudden dive to restore airspeed.\n\n“It’s too early to tell if the sensors caused the accident,” said John Goglia, an aviation safety expert and former member of the National Transportation Safety Board. He said that sensor equipment has become more automated with each new generation of aircraft, and that, in turn, introduces a new risk.\n\n“This could be a training issue,” he said. “The automation keeps marching forward and this is most advanced model that Boeing has built.” It’s still unclear whether the issue is one of an actual flaw, or a procedure that wasn’t correctly followed.", - "title": "Lion Air Flight JH 160 Crash Sensor Replacement", - "url": "https://www.popularmechanics.com/flight/airlines/a24841961/sensor-replaced-one-day-before-lion-air-flight-jh-160-crash/" - }, - { - "authors": ["Jeremy Bogaisky"], - "cloudinary_id": "reports/thumbor.forbes.com/thumbor/600x315/https%3A%2F%2Fspecials-images.forbesimg.com%2Fimageserve%2F1057534088%2F960x0.jpg", - "date_published": "2018-11-09", - "description": "With autopilot and auto landing, many airline pilots now aren't getting regular manual flying practice. That may have played a role in the Lion Air crash, says safety consultant Keith Mackey.", - "epoch_date_submitted": 1559347200, - "image_url": "https://thumbor.forbes.com/thumbor/600x315/https%3A%2F%2Fspecials-images.forbesimg.com%2Fimageserve%2F1057534088%2F960x0.jpg", - "inputs_outputs": null, - "language": "en", - "report_number": 385, - "source_domain": "forbes.com", - "submitters": ["Catherine Olsson"], - "tags": [], - "text": "AFP/Getty Images\n\n[Updated: Nov. 13]\n\nThe FAA issued an emergency airworthiness directive Wednesday to airlines operating the new Boeing 737 MAX, calling on them to better instruct pilots on how to deal with a potential faulty reading from a key gauge, an angle of attack sensor, that's supposed to help keep planes from falling out of the sky. The directive follows the discovery that the sensor was malfunctioning on a Lion Air 737 MAX that plunged into the sea off Indonesia on Oct. 29, killing all 189 aboard.\n\nThe accident and the FAA warning, which comes after Boeing issued a similar bulletin, may be less an indication that there’s anything wrong with the new version of Boeing’s top-selling plane than a sign of how increasingly automated flight systems are eroding pilot skills, says Keith Mackey, a Florida-based safety consultant who’s a former airline pilot and accident investigator.\n\nTo put it simply, says Mackey, the FAA’s directive tells pilots to turn off the plane's autopilot system, and if necessary the pitch trim system too, and fly the plane yourself.\n\n“It’s unfortunate that they even have to write something like this,” says Mackey. “It should be understood.”\n\nThe angle of attack sensor is a vane on the wing that gauges airflow to determine if the wing is generating enough lift. That airflow can be disrupted if the plane slows down or goes into too steep a climb, putting the plane in danger of stalling. In that situation the 737 MAX has a pitch trim system that automatically pushes the nose down to prevent a stall. The FAA's and Boeing's bulletins explain to pilots how to disengage the system in the event that a faulty reading from the angle of attack sensor leads it to push the nose down.\n\nShortly after takeoff from Jakarta, the Lion Air plane’s pilots requested to return to the airport, but they never turned back, plunging into the water at high speed.\n\nBased on the evidence available, says Mackey, the pilots may not have disengaged the autopilot or the trim system, which given the faulty angle of attack reading, may have lowered the nose and dived the plane into the water.\n\n“The pilot should have recognized that I’m getting an erroneous indication on my airspeed,” he says. “It’s a nice day, you can look out the window and see that the airplane isn’t flying nose high.”\n\nHe sees the accident as indicative of two problems: the increasing use of autopilot is giving pilots less flight time to maintain basic skills, and a lower level of experience and training among pilots in the developing world.\n\nMany airlines will require pilots shortly after takeoff to engage the autopilot, which in modern jets essentially manage the direction, speed and altitude of a plane based on instructions programmed by the pilot. Planes can also auto land at airports that have the necessary equipment.\n\n“You get a lot of takeoffs and landings but no one gets much flying practice,” says Mackey. “They’re getting to be good computer programmers, they know which buttons to push and when to push them. When something begins to fail it becomes a puzzlement.”\n\nThe 737 MAX is the only Boeing plane in which the angle of attack sensor triggers automated movements of the jet's tail, the aviation safety consultant John Cox told the Seattle Times.\n\nBoeing appears not to have informed pilots and airlines that a new automated trim system had been introduced in the MAX, failing to add mention of it to the flight crew operations manual or to include it in training for pilots, Aviation Week and the Wall Street Journal reported Nov. 13.\n\n\"If [it] was not covered in the MAX training, that is an error,\" says Mackey, but \"a pilot proficient in hand flying the airplane should have recognized an automation failure issue quickly and reverted to hand flying the airplane to an uneventful landing, with or without the training.\"\n\nIn developing countries with small military and private aviation sectors, airlines tend to rely on Western schools for pilot training, putting many pilots at the controls of large aircraft with fewer flight hours, he says.\n\nThose same factors limit the pool of trained mechanics. With air travel taking off in Asia, that can lead to systematic maintenance issues at growing airlines, says Mackey.\n\nThe Lion Air plane’s angle of attack sensor had registered incorrect airspeed readings on the four flights prior to the one that crashed, according to the Indonesia National Transportation Safety Committee, and the sensor had been replaced the day before.\n\nMackey says it’s a red flag that the issue went unaddressed for so long.\n\nBoeing has a lot riding on the 737 MAX: It has more than 4,500 orders on the books and has delivered 219. Lion Air was the launch customer for the 737 MAX 8, the variant of the plane that crashed, and the MAX 9.\n\nBoeing's bulletin states that the angle of attack sensor can trigger the 737 MAX's automatic pitch trim system to push the nose down for as long as 10 seconds. If pilots manually pull the nose back up, the system can reengage", - "title": "Crash Of Lion Air 737 MAX Raises Questions About Autopilot And Pilot Skills", - "url": "https://www.forbes.com/sites/jeremybogaisky/2018/11/08/crash-of-lion-air-737-max-raises-questions-about-autopilot-and-pilot-skills/#133731448477" - }, - { - "authors": ["Andy Pasztor", "Andrew Tangel"], - "cloudinary_id": "reports/s.marketwatch.com/public/resources/MWimages/MW-GY683_Southw_ZG_20181116075039.jpg", - "date_published": "2018-11-16", - "description": "During the three weeks before Lion Air Flight 610 plunged into waters off Indonesia, Southwest Airlines Co. replaced two malfunctioning flight-control...", - "epoch_date_submitted": 1559347200, - "image_url": "http://s.marketwatch.com/public/resources/MWimages/MW-GY683_Southw_ZG_20181116075039.jpg", - "inputs_outputs": null, - "language": "en", - "report_number": 384, - "source_domain": "marketwatch.com", - "submitters": ["Catherine Olsson"], - "tags": [], - "text": "During the three weeks before Lion Air Flight 610 plunged into waters off Indonesia, Southwest Airlines Co. LUV, +0.25% replaced two malfunctioning flight-control sensors of the same type that has been publicly implicated in the crash, according to a summary of Southwest maintenance records reviewed by The Wall Street Journal.\n\nBoth U.S. maintenance issues involved a Boeing Co. BA, +2.56% 737 MAX 8, the same model that crashed last month in Indonesia. The sensors measure whether the jetliner is angled above or below level flight. Those sensors, or related hardware, needed repairs in the Southwest instances, according to the summary document. The document also indicates Southwest pilots reported they couldn’t engage automated throttle settings, similar to cruise control on a car.\n\nA Southwest spokeswoman said the sensors didn’t fail and were removed as a precautionary measure as part of a troubleshooting process. She said at least one was repaired.\n\nInvestigators have confirmed the same type of sensor failed on the Lion Air flight, but they haven’t determined precisely what happened between that failure and the crash.\n\nSince the accident, which killed 189 people, Boeing has warned airlines about the potential for erroneous data from what are called “angle-of-attack” sensors. “We have not experienced a sensor failure or flight issue as described in Boeing’s bulletin,” the Southwest spokeswoman said.\n\nThe Southwest incidents didn’t result in emergencies and no one was hurt. They prompted what appear to be routine reports by mechanics checking out problems with the sensors.\n\nAn expanded version of this report appears on WSJ.com.", - "title": "Southwest replaced flight-control sensors of the kind implicated in Lion Air crash", - "url": "https://www.marketwatch.com/story/southwest-replaced-flight-control-sensors-of-the-kind-implicated-in-lion-air-crash-2018-11-16" - }, - { - "authors": ["Alan Levin", "Harry Suhartono", "Julie Johnsson"], - "cloudinary_id": "reports/timedotcom.files.wordpress.com/2018/10/lion-air-jet.jpg?quality=85", - "date_published": "2018-10-31", - "description": "The instruments were checked by maintenance workers overnight", - "epoch_date_submitted": 1559347200, - "image_url": "https://timedotcom.files.wordpress.com/2018/10/lion-air-jet.jpg?quality=85", - "inputs_outputs": null, - "language": "en", - "report_number": 383, - "source_domain": "time.com", - "submitters": ["Catherine Olsson"], - "tags": [], - "text": "(Bloomberg) — The Lion Air jet that crashed into the Java Sea off Indonesia earlier this week had experienced problems with the sensors used to calculate altitude and speed on its previous flight, an issue that could help explain why the plane dove into the water.\n\nPilots on the nearly new Boeing Co. 737 Max 8 reported the issue after flying from Denpasar to Jakarta the night before the accident, Lion Air spokesman Danang Mandala Prihantoro said Wednesday. The instruments were checked by maintenance workers overnight and the plane was cleared to fly, Prihantoro said.\n\nWhile it will be days or weeks before definitive information emerges in the crash shortly after takeoff with 189 people aboard, discrepancies in speed and altitude readings can cause confusion on the cockpit and have led to accidents in the past, including the 2009 crash of an Air France plane in the Atlantic Ocean.\n\nFlight-tracking data before the two-month old Lion Air jet crashed showed the plane was varying its altitude and speed, a possible indication that the pilots weren’t getting accurate information from the aircraft’s air-pressure sensors.\n\nThe Brief Newsletter Sign up to receive the top stories you need to know right now. View Sample Sign Up Now\n\nErroneous sensors could be an explanation for the flight track data, said John Cox, president of Safety Operating Systems and a former airline pilot. But Cox and others cautioned that it’s too soon to say what happened on the Lion Air flight and some of the flight data — such as speeds that weren’t extreme and none of the highly abrupt maneuvers that preceded the Air France jet’s loss of control — may suggest some other cause.\n\nIndonesia’s National Search and Rescue Agency has picked up signals from so-called pingers on the Flight JT610’s crash-proof flight recorders.\n\n“We have located the area where we strongly believe the plane’s black box is located. The large parts of the aircraft should be nearby,” National Military Chief Hadi Tjahjanto said.\n\nInspections of the 737 Max 8 aircraft operated by Lion Air and Garuda Indonesia since the accident found no technical issues, according to a statement by the country’s Transport Ministry. A review of maintenance documents found no additional issues were reported on the airspeed and altimeter system in the past 3 months.\n\nInvestigators from around the world, including a team from Boeing, are onsite poring over evidence. For now, the planemaker has told 737 Max operators that it doesn’t recommend they take any action at this time.\n\nEven with modern GPS tracking, planes need to calculate their precise speed through the air. To determine airspeed — which can vary substantially compared to the speed over the ground due to winds — aircraft rely on Pitot tubes which measure the air rushing into them.\n\nBy comparing that pressure against the ambient air pressure — which is obtained by what are known as static ports — aircraft can determine airspeed.\n\nIf either of the pressure sensors are blocked, it can cause erroneous readings. In the case of the Air France flight, investigators concluded that a high-altitude ice storm clogged the Pitot tubes.\n\nJetliners are equipped with three separate airspeed sensor systems as backups. If one goes bad, pilots are trained to check the other readings and disregard the one that’s incorrect.\n\nUnited Technologies Corp. is a supplier of the systems to the 737 Max, according to Airframer.com, a website that tracks suppliers of aircraft components. A company representative said she wasn’t immediately able to confirm whether its equipment was on the Lion Air plane.\n\nLion Air and investigators haven’t provided details about the issue on the previous flight Sunday night. Data provided by flight tracking company FlightRadar24 showed that the jet took off and reached an altitude of 5,550 feet (1,692 meters), then dropped to 4,625 feet, an unusual altitude loss at a time when aircraft normally climb steadily.\n\nThe plane then resumed its climb, but never exceeded 28,000 feet. Jetliners usually don’t fly below 30,000 feet because cruising at the lower altitudes is less fuel efficient. However, planes with partially malfunctioning altitude sensors aren’t allowed above 28,000 feet.\n\n“Every aircraft that we have will go through transit, pre-flight and post-flight checks,” said Lion Air’s Prihantoro. “We are conducting inspection and maintenance if needed on every aircraft.”\n\nAir France\n\nIndonesia’s National Transportation Safety Committee, which is leading the investigation, has interviewed pilots from the previous flight, Ony Soerjo Wibowo, an investigator, told a news conference on Tuesday.\n\nIn the Air France accident, investigators concluded that the Pitot tubes had become clogged with ice and all three of the plane’s speed indicators failed at different times, according to the final report of France’s Office of Investigation and Analysis.\n\nThe routine flight from Rio de Janeiro to Paris was interrupted with a cacophony of alarms and confusing informatio", - "title": "Lion Air Jet Had Airspeed Sensor Failure on Previous Flight", - "url": "http://time.com/5441004/lion-air-jet-sensor-failure/" - }, - { - "authors": ["Reuters Editorial"], - "cloudinary_id": "reports/s4.reutersmedia.net/resources_v2/images/rcom-default.png", - "date_published": "2018-11-07", - "description": "Boeing Co said on Wednesday it had issued a bulletin to airlines reminding pilots how to handle circumstances where there was erroneous data from \"angle of attack\" sensors, in the wake of the Lion Air crash in Indonesia last week.", - "epoch_date_submitted": 1559347200, - "image_url": "https://s4.reutersmedia.net/resources_v2/images/rcom-default.png", - "inputs_outputs": null, - "language": "en", - "report_number": 382, - "source_domain": "reuters.com", - "submitters": ["Catherine Olsson"], - "tags": [], - "text": "ZHUHAI, China, Nov 7 (Reuters) - Boeing Co said on Wednesday it had issued a bulletin to airlines reminding pilots how to handle circumstances where there was erroneous data from “angle of attack” sensors, in the wake of the Lion Air crash in Indonesia last week.\n\nThe manufacturer said in a statement that the Indonesian National Transportation Safety Committee had indicated that the Boeing 737 MAX jet involved in the crash, which killed all 189 people on board, had experienced erroneous input from one of the sensors.\n\nThe angle of attack probe measures the attitude of the plane relative to the air flow and provides crucial data for flight controls. An angle that is too high can throw the aircraft into an aerodynamic stall.\n\nThe Boeing statement did not say whether its advice was specific to the 737 MAX or included other aircraft models. (Reporting by Tim Hepher in Zhuhai, China; Writing by Jamie Freed; Editing by Muralikumar Anantharaman)", - "title": "Boeing issues bulletin for pilots after Lion Air sensor data error", - "url": "https://www.reuters.com/article/indonesia-crash-boeing/boeing-issues-bulletin-for-pilots-after-lion-air-sensor-data-error-idUSB9N1X603X" - }, - { - "authors": ["Stanley Widianto", "Ashley Halsey III", "Aaron Gregg"], - "cloudinary_id": "reports/www.mercurynews.com/wp-content/uploads/2018/11/Indonesia-Lion-Air-Crash.jpg?w=1024&h=642", - "date_published": "2018-11-29", - "description": "JAKARTA, Indonesia – A malfunctioning sensor and an automated response from the aircraft’s software stymied pilots’ efforts to control a doomed Indonesian flight that went careening into the sea last month, according to a preliminary investigative report released Wednesday.\nThe report, which stops short of determining the cause of the crash or analyzing findings, chronicles the chaotic moments on the Lion Air flight before it crashed into the waters off the coast of Java last month, killing all 189 passengers and crew on board.\n\nIt details how sensors and other equipment were checked and fixed before the aircraft’s final flight, but not the “angle of attack” sensor. That measures where the nose is pointing and was showing erroneous readings throughout the short time the plane was airborne.", - "epoch_date_submitted": 1559347200, - "image_url": "https://www.mercurynews.com/wp-content/uploads/2018/11/Indonesia-Lion-Air-Crash.jpg?w=1024&h=642", - "inputs_outputs": null, - "language": "en", - "report_number": 381, - "source_domain": "mercurynews.com", - "submitters": ["Catherine Olsson"], - "tags": [], - "text": "JAKARTA, Indonesia – A malfunctioning sensor and an automated response from the aircraft’s software stymied pilots’ efforts to control a doomed Indonesian flight that went careening into the sea last month, according to a preliminary investigative report released Wednesday.\n\nThe report, which stops short of determining the cause of the crash or analyzing findings, chronicles the chaotic moments on the Lion Air flight before it crashed into the waters off the coast of Java last month, killing all 189 passengers and crew on board.\n\nIt details how sensors and other equipment were checked and fixed before the aircraft’s final flight, but not the “angle of attack” sensor. That measures where the nose is pointing and was showing erroneous readings throughout the short time the plane was airborne.\n\nWith the sensor insisting the nose was too high, an automatic feature kicked in, sending the plane plummeting as the pilots wrestled to regain control. Unable to trust their readings, the pilots resorted to asking air traffic control about their speed and altitude.\n\nLion Air Flight 610 plunged into the Java Sea on Oct. 29 just after taking off from the Indonesian capital, Jakarta, killing the eight crew members and 181 passengers on board, including a child and two infants.\n\nThe crash appears to have been caused by a mix of brand new technology and cockpit confusion as the pilots fought to gain altitude after an early-morning takeoff from Jakarta. The flight crew – at an altitude of just 5,000 feet – had very little time to resolve the issue before the plane crashed into the sea at a reported 450 miles per hour.\n\nThough the report contains no conclusion assigning blame, its descriptions of automated systems overtaking the aircraft – leaving pilots both confused and powerless – poses questions for Boeing and Lion Air about whether the cockpit crew was prepared for this scenario. After the Lion Air crash, pilots in the United States accused Boeing of withholding safety information on its new 737 model.\n\nThe aircraft’s pilots asked to return to Jakarta just two minutes after takeoff, reporting a “flight-control problem” but not specifying what it was.\n\nBlack-box data released by Indonesian investigators showed that the pilots were pulling back on the control column, attempting to raise the plane’s nose, with almost 100 pounds of pressure before they crashed.\n\nThe Indonesian National Transportation Safety Committee, which produced the report, also said that Lion Air, a Jakarta-based low-cost airline, should improve its “safety culture.”\n\nNo engineer briefed the pilots of the crashed plane on the multiple problems the aircraft experienced on previous flights, and it was up to him to review the maintenance logs.\n\nThe report, however, contains no conclusion on who was at fault.\n\n“When it comes to faulting, I don’t know. Our job isn’t to find faults,” National Transportation Safety Committee investigator Nurcahyo Utomo said at a news conference Wednesday.\n\nThe aircraft was the most recent incarnation of the venerable Boeing 737, a plane that first flew in 1967 and has gone through multiple iterations before it emerged as the 737 Max.\n\nThe 737 Max was equipped with more-powerful engines that are mounted farther forward on the wing, requiring that additional software be added to the autopilot to provide more control.\n\nThat software, which has been described as several lines of coding, was identified in the Boeing manual as the maneuvering characteristics augmentation system, or MCAS.\n\nWhen the sensors transmitted faulty data to the cockpit of Flight 610, the new MCAS system sensed a stall – that point at which planes do not have enough airspeed to create lift – and sought to correct for it by repeatedly pointing the nose of the aircraft down.\n\nA feature in previous 737 models that allowed pilots to manually override an “electric trimming” process – which automatically budges the nose downward to prevent a stall, does not work in Boeing’s 737 Max 8 planes, Boeing explained in a Nov. 7 bulletin.\n\nThat same week, the Federal Aviation Administration issued an emergency notice to all airlines that fly the 737 Max, warning them that erroneous sensor inputs “could cause the flight crew to have difficulty controlling the airplane,” leading to “possible impact with terrain.”\n\nThe deviation probably was caused by what is called a “runaway stabilizer.” Stabilizers are essentially those small wings on either side at the tail end of the plane. They each have flaps – called elevators – that help control the elevation of the plane.\n\nIn case of a runaway stabilizer, pilots are instructed in the cockpit checklist to hold the control column firmly, disengaging the autopilot that, in this case, contained the MCAS program. Next, they are told, disengage the auto throttle and manually fly the plane.\n\n“This corner of the performance charts is called the ‘coffin corner,’ ” said Mary Schiavo, an aviati", - "title": "Report: Lion Air pilots unable to correct for faulty sensor", - "url": "https://www.mercurynews.com/2018/11/28/report-lion-air-pilots-unable-to-correct-faulty-sensor/" - }, - { - "authors": ["Iafrica Editors Curate"], - "cloudinary_id": "reports/www.iafrica.com/wp-content/uploads/2018/11/Lion-air-e1541663580577.jpg", - "date_published": "2018-11-08", - "description": "A crucial sensor was replaced on a Lion Air jet the day before it plunged into the Java Sea, and that sensor replacement may have exacerbated other problems with the plane, Indonesian investigators said on Wednesday.\nThat sensor, known as the “angle of attack” sensor, keeps track of the angle of the aircraft nose to help prevent the plane from stalling and diving.", - "epoch_date_submitted": 1559347200, - "image_url": "https://www.iafrica.com/wp-content/uploads/2018/11/Lion-air-e1541663580577.jpg", - "inputs_outputs": null, - "language": "en", - "report_number": 380, - "source_domain": "iafrica.com", - "submitters": ["Catherine Olsson"], - "tags": [], - "text": "A crucial sensor was replaced on a Lion Air jet the day before it plunged into the Java Sea, and that sensor replacement may have exacerbated other problems with the plane, Indonesian investigators said on Wednesday.\n\nThat sensor, known as the “angle of attack” sensor, keeps track of the angle of the aircraft nose to help prevent the plane from stalling and diving.\n\nEarlier this week, Indonesian officials hinted that airspeed indicators played a role in the deadly October 29 crash that killed all 189 people on board.\n\nThe jet’s airspeed indicator malfunctioned on its last four flights, and that problem was related to the sensor issue, said Soerjanto Tjahjono, chairperson of Indonesia’s National Transportation Safety Committee, on Wednesday.\n\nLion Air’s first two attempts to address the airspeed indicator problem didn’t work, and for the Boeing 737 MAX 8 plane’s second-to-last flight on October 28, the angle of attack sensors were replaced, Tjahjono said.\n\nOn the October 28 flight, from Bali to Jakarta, the pilot’s and co-pilot’s sensors disagreed. The 2-month-old plane went into a sudden dive minutes after takeoff, which the pilots were able to recover from. They decided to fly on to Jakarta at a lower-than-normal altitude.\n\nThe next day, during the deadly crash, the plane hit the water at very high speed just 13 minutes after takeoff from Jakarta. Its flight crew had requested permission to return to the airport several minutes after taking off.\n\n“The point is that after the AOA (sensor) is replaced, the problem is not solved but the problem might even increase. Is this fatal? NTSC (National Transportation Safety Committee) wants to explore this,” he said.\n\nEven if an angle of attack sensor on a jet is faulty, there’s generally a backup system in place for the critical component, and pilots are trained to handle a plane safely if those sensors fail, airline safety experts said.\n\nThere are audio signals and physical warnings that can alert the pilot to malfunctioning equipment or other dangers, said Todd Curtis, director of the Airsafe.com Foundation.\n\n“They should have been completely engaged in what was going on inside that cockpit, and any kind of warning that came up, they would have been wise to pay attention to it,” Curtis said.\n\nInvestigators are likely focused on how a single sensor’s failure resulted in a faulty command that didn’t take into account information from a second sensor, said John Cox, CEO of Safety Operating Systems.\n\n“We don’t know what the crew knew and didn’t know yet,” Cox said. “We will.”\n\nA new procedure release\n\nBoeing, which manufactured the Lion Air plane, issues safety-related bulletins, and had previously circulated instructions about what flight crews should do if sensors fail.\n\nIndonesia’s National Transportation Safety Committee said it had agreed with Boeing on procedures that the airplane manufacturer should distribute globally on how flight crews can deal with “angle of attack” sensor problems.\n\nBut a Boeing statement said a safety bulletin, sent to airlines on Tuesday, directs flight crews to existing guidelines on how they should respond to erroneous “angle of attack” data. It wasn’t immediately clear if it plans an update, though comments from Indonesian officials indicate they expect one.\n\nIndonesian investigators said their flight procedure recommendations to Boeing were based on how the flight crew responded to problems on the Bali-to-Jakarta flight.\n\n“The draft of what will be conveyed by Boeing this morning has been presented to us,” said air accident investigator Nurcahyo Utomo.\n\n“There are some things that we ask for explanation and some that we ask to be removed, and there has been an agreement between the NTSC and Boeing to release a new procedure to all Boeing 737 MAX users in the world,” he said.\n\nIndonesia’s search and rescue agency on Wednesday extended the search effort for a second time, saying it will continue until Sunday. Body parts are still being recovered and searchers continue to hunt for the cockpit voice recorder.\n\nThe Lion Air crash is the worst airline disaster in Indonesia since 1997, when 234 people died on a Garuda flight near Medan. In December 2014, an AirAsia flight from Surabaya to Singapore plunged into the sea, killing all 162 on board.\n\nLion Air is one of Indonesia’s youngest airlines but has grown rapidly, flying to dozens of domestic and international destinations. It has been expanding aggressively in Southeast Asia, a fast-growing region of more than 600 million people.\n\nNews24", - "title": "Lion Air Plane Crash: Crucial 'Angle Of Attack' Sensor Replaced Before Crash", - "url": "https://www.iafrica.com/lion-air-plane-crash-crucial-angle-of-attack-sensor-replaced-before-crash/" - }, - { - "authors": ["CGTN Live"], - "cloudinary_id": "reports/news.cgtn.com/news/3d3d674e3163444e30457a6333566d54/img/0b16c22bf73a4262b045df07e92b840c/0b16c22bf73a4262b045df07e92b840c.jpg", - "date_published": "2018-11-10", - "description": "Indonesia's National Transportation Safety Committee said on Wednesday that a crucial sensor had been replaced on a Lion Air jet the day before it plunged into the Java Sea, killing all 189 people on board.\n\nThat sensor, known as the \"angle of attack\" (AOA) sensor, keeps track of the angle of the aircraft nose to help prevent the plane from stalling and diving.", - "epoch_date_submitted": 1559347200, - "image_url": "https://news.cgtn.com/news/3d3d674e3163444e30457a6333566d54/img/0b16c22bf73a4262b045df07e92b840c/0b16c22bf73a4262b045df07e92b840c.jpg", - "inputs_outputs": null, - "language": "en", - "report_number": 379, - "source_domain": "news.cgtn.com", - "submitters": ["Catherine Olsson"], - "tags": [], - "text": "Indonesia's National Transportation Safety Committee said on Wednesday that a crucial sensor had been replaced on a Lion Air jet the day before it plunged into the Java Sea, killing all 189 people on board.\n\nThat sensor, known as the \"angle of attack\" (AOA) sensor, keeps track of the angle of the aircraft nose to help prevent the plane from stalling and diving.\n\nExperts say the sensor is a crucial parameter that helps the aircraft's computers understand whether its nose is too high relative to the current of air. If the sensor fails to send correct information, it can confuse both the aircraft's computer and its pilots, throwing an aircraft into an aerodynamic stall and making it fall.\n\n\nAn Indonesian National Transportation Safety Commission official examines a turbine engine from the crashed Lion Air flight JT610 in Jakarta, Indonesia, November 4, 2018. /VCG Photo\n\nEarlier this week, Indonesian officials hinted that airspeed indicators played a role in Lion Air crash. One of the black boxes showed that the airspeed indicator on the jet malfunctioned on its last four flights, and that problem was related to the sensor issue, said Soerjanto Tjahjono, chairman of Indonesia's National Transportation Safety Committee, on Wednesday.\n\nLion Air's first two attempts to address the airspeed indicator problem didn't work, and the AOA sensors were not replaced until the Boeing 737 MAX 8 plane's second-to-last flight on October 28, said Tjahjono.\n\nOn the October 28 flight, from Bali to Jakarta, the pilot's and copilot's sensors disagreed. The 2-month-old plane went into a sudden dive minutes after takeoff, which the pilots were able to recover from. They decided to fly to Jakarta at a lower-than-normal altitude.\n\n\nWorkers load up recovered debris and belongings believed to be from crashed Lion Air flight JT610. /VCG Photo\n\nThe next day, during the deadly crash, the plane hit the water at very high speed just 13 minutes after takeoff from Jakarta. Its flight crew had requested permission to return to the airport several minutes after taking off.\n\n\"The point is that after the AOA sensor is replaced, the problem is not solved but the problem might even increase. Is this fatal? NTSC (National Transportation Safety Committee) wants to explore this,\" he said.\n\nWarnings must not be ignored\n\nAirline safety experts said the aircraft usually have a backup system that responds to sensor faults, and pilots are trained to handle a plane safely if those sensors fail.\n\nThere are audio signals and physical warnings that can alert the pilot to malfunctioning equipment or other dangers, said Todd Curtis, director of the Airsafe.com Foundation.\n\n\"They should have been completely engaged in what was going on inside that cockpit, and any kind of warning that came up, they would have been wise to pay attention to it,\" Curtis said.\n\n\nRelatives of the victims of the Lion Air crash sprinkle flowers at the crash site. /AP Photo\n\nInvestigators are likely focused on how a single sensor's failure resulted in a faulty command that didn't take into account information from a second sensor, said John Cox, CEO of Safety Operating Systems.\n\n\"We don't know what the crew knew and didn't know yet,\" Cox said, adding \"We will.\"\n\nThe Boeing 737 MAX has three such AOA sensors, Reuters quoted an informed source.\n\nFAA says sensor problem detected in 246 Boeing 737 MAX airplanes worldwide\n\nThe US Federal Aviation Administration (FAA) on Wednesday issued an emergency airworthiness directive on 246 Boeing 737 Max airplanes worldwide, of which 45 airplanes in the US are operated by Southwest Air Co, United Airlines and American Airlines Group Inc.\n\nIt warned airlines that erroneously input from an AOA sensor could cause \"repeated nose-down trim commands of the horizontal stabilizer.\" If this condition is not addressed, it could lead the flight crew to have difficulty controlling the airplane.\n\n\nA Boeing 737 MAX parked outside the hangar. /VCG Photo\n\n\"We are issuing this airworthiness directive because we evaluated all the relevant information and determined the unsafe condition is likely to exist or develop in other products of the same type design,\" said the FAA.\n\nThe directive dictates that operators \"revise the airplane flight manual to give the flight crew horizontal stabilizer trim procedures to follow under some conditions.\"\n\nBoeing 737 Max jet is the latest version of the US aircraft maker, which has been in service for just over a year. And the Lion Air crash was the first involving the new jet.\n\n(With inputs from AP)", - "title": "Indonesian officials: Problems with sensor found on crashed Lion Air jet", - "url": "https://news.cgtn.com/news/3d3d674e3163444e30457a6333566d54/share_p.html" - }, - { - "authors": ["Alan Levin", "Julie Johnsson", "Harry Suhartono"], - "cloudinary_id": "reports/www.insurancejournal.com/app/uploads/2018/11/Bloomberg-Graphic_Top-Boeing-Max-Customers_08Nov2018-580x517.png", - "date_published": "2018-11-08", - "description": "A Boeing Co. warning to 737 Max operators around the globe provides the first clues about how bad data from an airflow sensor might have contributed to", - "epoch_date_submitted": 1559347200, - "image_url": "https://www.insurancejournal.com/app/uploads/2018/11/Bloomberg-Graphic_Top-Boeing-Max-Customers_08Nov2018-580x517.png", - "inputs_outputs": null, - "language": "en", - "report_number": 378, - "source_domain": "insurancejournal.com", - "submitters": ["Catherine Olsson"], - "tags": [], - "text": "A Boeing Co. warning to 737 Max operators around the globe provides the first clues about how bad data from an airflow sensor might have contributed to the deadly crash of an Indonesian airliner last week.\n\nThe bulletin and statements by Indonesian investigators suggest that the pilots on the Lion Air 737 Max 8 were battling the plane as its computers commanded a steep dive during its final moments of flight.\n\nBoeing cautioned that the so-called angle-of-attack sensor can provide false readings in limited circumstances — such as when a plane’s autopilot is switched off — that cause the 737 Max to pitch nose downward. The sensor malfunction can essentially trick the plane into pointing its nose down to gain the speed it thinks it needs to keep flying.\n\nThe Boeing directive doesn’t call for operators to conduct new inspections or take other action. It merely stressed that pilots should follow procedures in the flight manual when encountering erroneous data. Following the protocol should be routine for pilots, though may be more challenging in the heat of the moment when equipment is malfunctioning and alarms are sounding.\n\nAmerican aviation regulators followed by issuing an emergency order Wednesday requiring that airlines follow Boeing’s instructions and add information to pilot manuals showing how to diagnose the problem and respond. Carriers will have three days to update their manuals under the order, issued by the Federal Aviation Administration, according to an emailed statement.\n\nThe FAA said the problem “could cause the flight crew to have difficulty controlling the airplane, and lead to excessive nose-down attitude, significant altitude loss, and possible impact with terrain.”\n\nThe sensor on the Lion Air jet been replaced the day before after it failed on a previous trip, the Indonesia National Transportation Safety Committee said in a briefing Wednesday. The malfunction can cause the plane’s computers to erroneously detect a mid-flight stall in airflow, causing the aircraft to abruptly dive to regain the speed it needs to keep flying.\n\nOn a previous flight from Bali to Jakarta, the angle-of-attack sensor feeding the captain’s flight displays registered a 20-degree difference from the device on the copilot’s side of the cockpit, the committee said. Pilots on that flight were able to compensate.\n\nAn angle-of-attack sensor that had been removed before that previous flight has been brought to the investigators and will be examined in the U.S., the Indonesian officials said.\n\nIt’s still possible the FAA may order Boeing to redesign the equipment or software as investigators piece together details of the Oct. 29 crash, which killed 189 people. The agency said it “will take further appropriate actions depending on the results of the investigation.”\n\nFresh Questions\n\nThe new information about Lion Air Flight 610 raises multiple questions investigators will want to examine on the pilots’ actions, how flight crews were trained and whether the maintenance performed on the system was adequate, said Roger Cox, a former NTSB investigator.\n\n“I would definitely be looking at the man-machine interface and how pilots respond,” said Cox, a former airline pilot who flew earlier versions of the 737 and specialized at the NTSB in cockpit actions.\n\nOne of the puzzling things about the accident is that the plane was flying in clear skies during daylight, so pilots should have been able to handle the problems they faced with airspeed and erroneous sensors, Cox said. However, in rare instances, accidents have been caused by what investigators call a “startle factor.”\n\n“If you don’t take the appropriate action because you’re surprised, you can make a serious error,” he said.\n\nThe pilots union at Southwest Airlines Co., the biggest customer of the 737 Max, hasn’t received any reports from its members of problems with faulty sensor readings, said Jon Weaks, president of the Southwest Airlines Pilots Association.\n\nThe airline confirmed that it hasn’t experienced any of the sensor troubles and said its 26 Max remain operational and no schedule disruption is expected.\n\nUnited Technologies Corp. supplies the angle-of-attack sensors and indicator for the 737 Max, according to Airframers.net. The company didn’t respond to requests for comment. Honeywell International Inc. provides the air data inertial reference unit.\n\nThe Lion Air jetliner plunged into the Java Sea minutes after takeoff from Jakarta airport, nosing downward so suddenly that it may have hit speeds of 600 miles an hour before slamming into the water.\n\nMoments earlier, the pilots radioed a request to return to Jakarta, but never turned back toward the airport, according to Indonesia’s safety commission and flight-tracking data. The agency said the pilots were dealing with an erroneous airspeed indication.\n\nBoeing, which is headquartered in Chicago, said it is cooperating fully and providing technical assistance as the investigation continues. Boeing’s shares rose 1.5 percent to $372.02 at", - "title": "New Clues to Lion Air 737 Max Crash Revealed in Boeing, FAA Warnings", - "url": "https://www.insurancejournal.com/news/international/2018/11/08/507060.htm" - }, - { - "authors": ["Australian Broadcasting Corporation"], - "cloudinary_id": "reports/www.abc.net.au/news/image/10464216-16x9-700x394.jpg", - "date_published": "2018-11-08", - "description": "A crucial sensor that is the subject of a Boeing safety bulletin was replaced on a Lion Air jet the day before it plunged into the Java Sea and possibly worsened other problems with the plane, Indonesian investigators say.", - "epoch_date_submitted": 1559347200, - "image_url": "https://www.abc.net.au/news/image/10464216-16x9-700x394.jpg", - "inputs_outputs": null, - "language": "en", - "report_number": 377, - "source_domain": "abc.net.au", - "submitters": ["Catherine Olsson"], - "tags": [], - "text": "Lion Air plane had angle of attack sensor replaced prior to crash; Boeing issues safety reminder\n\nUpdated\n\nA crucial sensor that is the subject of a Boeing safety bulletin was replaced on a Lion Air jet the day before it plunged into the Java Sea and possibly worsened other problems with the plane, Indonesian investigators have revealed.\n\nKey points: Boeing issues safety bulletin reminding pilots how to handle erroneous data from key sensor\n\nThe angle of attack sensors were replaced on the plane's second-to-last flight\n\nChairman of Indonesia's NTSC says airspeed indicator malfunctions were intertwined with the sensor issue\n\nIndonesia's National Transportation Safety Committee (NTSC) said it had agreed with Boeing on procedures that the airplane manufacturer should distribute globally on how flight crews can deal with \"angle of attack\" sensor problems following the October 29 crash that killed all 189 people on board.\n\nExperts say the angle of attack is a crucial parameter that helps the aircraft's computers understand whether its nose is too high relative to the current of air.\n\nThe sensor keeps track of the angle of the aircraft nose relative to oncoming air to prevent the plane from stalling and diving.\n\nBut a Boeing statement said a safety bulletin, sent to airlines this week, directs flight crews to existing guidelines on how they should respond to erroneous \"angle of attack\" data.\n\nIt was not immediately clear whether Boeing was planning to update the guidelines, though comments from Indonesian officials indicate they expect so.\n\nAirspeed indicator malfunction and sensor issue linked\n\nTransport safety committee chairman Soerjanto Tjahjono said airspeed indicator malfunctions on the jet's last four flights, which were revealed by an analysis of the flight data recorder, were intertwined with the sensor issue.\n\n\"The point is that after the AOA [sensor] is replaced the problem is not solved, but the problem might even increase. Is this fatal? NTSC wants to explore this,\" he said.\n\nLion Air's first two attempts to address the airspeed indicator problem did not work, and for the Boeing 737 MAX 8 plane's second-to-last flight on October 28, the angle of attack sensors were replaced, Mr Tjahjono said.\n\nOn that flight, from Bali to Jakarta, the pilot's and co-pilot's sensors disagreed.\n\nThe two-month-old plane went into a sudden dive minutes after take-off, which the pilots were able to recover from. They decided to fly on to Jakarta at a lower-than-normal altitude.\n\nIndonesian investigators said their flight procedure recommendations to Boeing were based on how the flight crew responded to problems on the Bali to Jakarta flight.\n\n\"The draft of what will be conveyed by Boeing this morning has been presented to us,\" air accident investigator Nurcahyo Utomo said.\n\n\"There are some things that we ask for explanation and some that we ask to be removed, and there has been an agreement between NTSC and Boeing to release a new procedure to all Boeing 737 MAX users in the world.\"\n\nIndonesia's search and rescue agency extended the search effort on Wednesday for a second time, saying it would continue until Sunday.\n\nBody parts are still being recovered and searchers continue to hunt for the cockpit voice recorder.\n\nThe plane hit the water at very high speed just 13 minutes after take-off from Jakarta. Its flight crew had requested permission to return to the airport several minutes after taking off.\n\nThe Lion Air crash is the worst airline disaster in Indonesia since 1997, when 234 people died on a Garuda flight near Medan.\n\nIn December 2014, an AirAsia flight from Surabaya to Singapore plunged into the sea, killing all 162 on board.\n\nLion Air is one of Indonesia's youngest airlines but has grown rapidly, flying to dozens of domestic and international destinations. It has been expanding aggressively in South-East Asia, a fast-growing region of more than 600 million people.\n\nAP/Reuters\n\nTopics: disasters-and-accidents, air-and-space, indonesia, asia\n\nFirst posted", - "title": "Lion Air plane had angle of attack sensor replaced prior to crash; Boeing issues safety reminder", - "url": "https://www.abc.net.au/news/2018-11-08/lion-air-flight-had-crucial-sensor-replaced-prior-to-fatal-crash/10475468" - }, - { - "authors": ["Grant Bradley"], - "cloudinary_id": "reports/www.nzherald.co.nz/resizer/yF7-HI8O2KRMMtki3GBhsuDKm-w=/1200x0/smart/filters:quality(70)/arc-anglerfish-syd-prod-nzme.s3.amazonaws.com/public/TJU32NCPOJFSVJO4SLPNBPNMQ4.jpg", - "date_published": "2018-10-30", - "description": "Faulty sensing equipment on planes has been linked to several crashes", - "epoch_date_submitted": 1559347200, - "image_url": "https://www.nzherald.co.nz/resizer/yF7-HI8O2KRMMtki3GBhsuDKm-w=/1200x0/smart/filters:quality(70)/arc-anglerfish-syd-prod-nzme.s3.amazonaws.com/public/TJU32NCPOJFSVJO4SLPNBPNMQ4.jpg", - "inputs_outputs": null, - "language": "en", - "report_number": 376, - "source_domain": "nzherald.co.nz", - "submitters": ["Catherine Olsson"], - "tags": [], - "text": "Possible faulty equipment that led to the crash of Lion Air Flight 610 was the same sort of sensing gear that contributed to the crash of an Air New Zealand A320 off Perpignan 10 years ago.\n\nA failure of air flow monitoring pitot tubes on board the Lion Air Boeing 737 has been cited as a possible contributor to yesterday's crash off Indonesia which killed 189 people.\n\nA report in the New York Times says the erratic flight path before the high speed plunge indicated a problem with the pressure sensitive instruments near the nose of the plane.\n\n\"The erratic flight path makes us suspect a problem with the pitot-static system,\" said Gerry Soejatman, an Indonesian aviation expert.\n\nData from the flight indicated an ''erratic climb and ground speed problem\", leading him to suspect a problem with the instruments had also been an issue then.\n\nAdvertisement\n\nSeveral plane crashes have been blamed on blockages or other problems with pitot tubes which resulted in erroneous speed or altitude readings, the Times reported.\n\nIn November 2008 an Airbus A320 that had been leased by Air New Zealand to a European carrier crashed, killing two German pilots and five New Zealanders on board.\n\nThe plane had been on a routine test flight, prior to being handed back to Air New Zealand by XL Airways Germany, a charter company that had been leasing it for the previous two years.\n\nAn inquiry found that the crash was triggered by a test that was conducted at low speed and low altitude, throwing the plane into a stall from which it failed to recover.\n\nThe ill-fated manoeuvre arose especially from makeshift preparations for the exercise and poor coordination between the German and New Zealand crew on board, the Bureau d'Enquetes et d'Analyses (BEA) said after a 22-month inquiry.\n\nBut compounding their mistake was a malfunction in two out of three external probes that feed the A320's complex computerised flight system with vital data about air flow.\n\nThe plane had been repainted and rinsed by a French maintenance company three days before the test, the investigation found. Water entered these so-called angle of attack (AOA) sensors, causing them to freeze and thus skewing the avionics.\n\nA year later, in 2009, a Rio-to-Paris Air France flight disappeared over the Atlantic with the loss of all 228 people on board. An inquiry found that the plane flew into a thunderstorm, which froze the crucial speed sensors, resulting in the pilots flying blind and leading to a mid-air stall.\n\nIndonesian actress Conchita Caroline had concerns about the doomed Lion Air plane after flying on it the day before. Photo / Supplied\n\nToday a passenger who was on the ill-fated Lion Air aircraft a day before the crash outlined concerns about the plane.\n\nIndonesian actress and TV presenter Conchita Caroline said her flight from Bali to Jakarta struck problems.\n\nAs the plane readied for takeoff, an engine seemed to die several times, the air conditioning was faulty and the floor beneath her felt hot to touch.\n\nLion Air's chief executive Edward Surat said yesterday there was a report of a technical issue from that fight but that had been resolved ''according to procedure\" before Flight 610 took off.\n\nFollowing the crash Boeing's stock price fell by almost 7 per cent. Lion Air is an important customer for its 737s.", - "title": "Possible fault in Lion Air plane similar to a cause of Air New Zealand Airbus A320 France crash", - "url": "https://www.nzherald.co.nz/business/news/article.cfm?c_id=3&objectid=12151363" - }, - { - "authors": ["USA Today Editorial Board"], - "cloudinary_id": "reports/www.gannett-cdn.com/presto/2018/12/04/USAT/dfc4ad2e-d645-4d86-94ad-02c1f63b6f74-Boeing.ourview.JPG?crop=1161,657,x0,y0&width=3200&height=1680&fit=bounds", - "date_published": "2018-12-05", - "description": "Why did the 737 Max plunge into the sea in Indonesia, killing all 189 on board? Pilots must know about every change on the jets they fly: Our view", - "epoch_date_submitted": 1559347200, - "image_url": "https://www.gannett-cdn.com/presto/2018/12/04/USAT/dfc4ad2e-d645-4d86-94ad-02c1f63b6f74-Boeing.ourview.JPG?crop=1161,657,x0,y0&width=3200&height=1680&fit=bounds", - "inputs_outputs": null, - "language": "en", - "report_number": 375, - "source_domain": "usatoday.com", - "submitters": ["Catherine Olsson"], - "tags": [], - "text": "Why did the 737 Max plunge into the sea in Indonesia, killing all 189 on board? Pilots must know about every change on the jets they fly: Our view\n\nIndonesians recover a plastic box containing the data recorder of Lion Air Flight 610 on Nov. 1, 2018. (Photo: Pradita Utama, epa-EFE)\n\n“Know your airplane, fly your airplane” is a bedrock principle for pilots.\n\nBut pilots can’t know their airplane if the maker fails to disclose a new emergency feature in its flight operations manual, as happened with the Boeing 737 Max.\n\nOn Oct. 29, the feature, known by the acronym MCAS, relentlessly pushed down the nose of Indonesia’s Lion Air Flight 610 more than 20 times before the jet plunged into the Java Sea, killing all 189 on board.\n\nPilots struggled against the new system, which is designed to prevent a stall by automatically pushing the nose down. Tragically, on Flight 610 it was apparently triggered by a sensor delivering a false reading. There was no stall.\n\nThe 737 Max, Boeing's newest version of the workhorse 737 series, is flown by airlines around the world, including American, Southwest and United. Hundreds more are on order. Since the Lion Air crash, American and Southwest pilots have complained about Boeing’s failure to communicate.\n\nBOEING: Safety will always be a core value\n\n“The key to any emergency is identifying the system that is betraying you or failed,” says Dennis Tajer, a veteran 737 captain and spokesman for the Allied Pilots Association, which represents pilots at American.\n\n“We did not know about the system\" until after the Lion Air crash. It was not on previous versions of the 737. Jon Weaks, a Southwest captain and president of Southwest Airlines Pilots Association, put it simply: \"We should have known the system existed.\"\n\nWith all the safety built into today’s jetliners, it typically takes a cascading series of events to bring down a plane. Based on a preliminary report released last week by Indonesian authorities and questions raised by safety experts, the final flight of Lion Air 610 is a textbook example of multiple failures.\n\nInvestigators will need to get to the bottom of several issues involving:\n\n►Boeing. A potential design or manufacturing flaw might have allowed a single sensor, with an erroneous reading, to trigger MCAS — which stands for Maneuvering Characteristics Augmentation System — and the nose-down movements. Two sensors on the jet were measuring what’s known as angle of attack, “one giving you an accurate reading, one inaccurate,” says former National Transportation Safety Board Chair Mark Rosenker. “The problem was the inaccurate one appeared to take over.” He questioned why a single malfunctioning sensor could create a situation potentially leading to a catastrophic failure. The answer might be that it shouldn't.\n\nWithin days of the crash, Boeing issued a bulletin telling 737 Max pilots to deal with erroneous sensor data and nose-down movements by turning off the automatic system, in accordance with “existing procedures.” A Federal Aviation Administration directive followed, warning that erroneous readings could cause “difficulty controlling the airplane” and “possible impact with terrain.”\n\n►Lion Air. The day before the crash, Lion Air maintenance replaced the sensor on the same plane. But pilots got erroneous readings on that flight and experienced a nearly identical problem to the one on Flight 610. Was the sensor installed properly by company crews? Based on the serious problems on Oct. 28, should the plane have been grounded before the fatal flight?\n\n►Flight 610 pilots. On the Oct. 28 flight, pilots initially reacted to the nose-down movements the same way as pilots on Flight 610 the next day. When that didn’t work, they used two switches to cut off the system — a standard emergency procedure. It is baffling that the pilots on Flight 610 failed to do the same thing. Perhaps faced by an inaccurate reading from a faulty sensor and a system they likely knew nothing about, they were confused in the emergency. Perhaps they were not well-trained. For now, that remains a mystery.\n\nThe causes of the crash will be determined by Indonesian authorities and the U.S. National Transportation Safety Board, which has an interest in keeping American fleets safe.\n\nBoeing issued a statement asserting, “We are confident in the safety of the 737 MAX.\" A company spokesman added that the “function performed by MCAS is referenced” in the flight manual, and “existing procedures” to deal with it are documented. The chairman of the United Airlines branch of the Airline Pilots Association echoed that statement, breaking with two other unions and his own union leadership.\n\nBackup systems, what the industry calls redundancy, are designed to keep planes in the air if one component fails. The ultimate safety backups are the pilots — who deserve to know about every change on the aircraft they fly.\n\nUSA TODAY's editorial opinions are decided by its Editorial Board, separate from the news staff. Most editorials are coupl", - "title": "What we've got here is a failure to communicate", - "url": "https://www.usatoday.com/story/opinion/2018/12/04/lion-air-what-weve-got-here-failure-communicate-editorials-debates/2192044002/" - }, - { - "authors": ["Dominic Gates"], - "cloudinary_id": "reports/static.seattletimes.com/wp-content/uploads/2018/11/11272018_Black-box-data_121412-1200x630.jpg", - "date_published": "2018-11-27", - "description": "Data from the fatal Oct. 29 flight that killed 189 people, and from the prior day's flight of the same jet, raises questions about three factors that seem to have...", - "epoch_date_submitted": 1559347200, - "image_url": "https://static.seattletimes.com/wp-content/uploads/2018/11/11272018_Black-box-data_121412-1200x630.jpg", - "inputs_outputs": null, - "language": "en", - "report_number": 374, - "source_domain": "seattletimes.com", - "submitters": ["Catherine Olsson"], - "tags": [], - "text": "Data from the fatal Oct. 29 flight that killed 189 people, and from the prior day's flight of the same jet, raises questions about three factors that seem to have contributed to the crash.\n\nA key instrument reading on Lion Air flight JT610 was faulty even as the pilots taxied out for takeoff. As soon as the Boeing 737 MAX was airborne, the captain’s control column began to shake as a stall warning.\n\nAnd from the moment they retracted the wing flaps at about 3,000 feet, the two pilots struggled — in a 10-minute tug of war — against a new anti-stall flight-control system that relentlessly pushed the jet’s nose down 26 times before they lost control.\n\nThough the pilots responded to each nose-down movement by pulling the nose up again, mysteriously they didn’t do what the pilots on the previous day’s flight had done: simply switched off that flight-control system.\n\nThe detail is revealed in the data from the so-called “black box” flight recorder (it’s actually orange in color) from the fatal Oct. 29 flight that killed 189 people and the prior day’s flight of the same jet, presented last Thursday to the Indonesian Parliament by the country’s National Transportation Safety Committee (NTSC).\n\nThis data is the major basis for the preliminary crash-investigation report that was made public Wednesday in Indonesia, Tuesday evening in Seattle.\n\nThe flight-recorder data is presented as a series of line graphs that give a clear picture of what was going on with the aircraft systems as the plane taxied on the ground, took off and flew for just 11 minutes.\n\nThe data points to three factors that seem to have contributed to the disaster:\n\nA potential design flaw in Boeing’s new anti-stall addition to the MAX’s flight-control system and a lack of communication to airlines about the system.\n\nThe baffling failure of the Lion Air pilots to recognize what was happening and execute a standard procedure to shut off the faulty system.\n\nAnd a Lion Air maintenance shortfall that allowed the plane to fly repeatedly without fixing the key sensor that was feeding false information to the flight computer on previous flights.\n\nAnti-stall system triggered\n\nPeter Lemme, a former Boeing flight-controls engineer who is now an avionics and satellite-communications consultant, analyzed the graphs minute by minute.\n\nHe said the data shows Boeing’s new system — called MCAS (Maneuvering Characteristics Augmentation System) — “was triggered persistently” as soon as the wing flaps retracted.\n\nThe data confirms that a sensor that measures the plane’s angle of attack, the angle between the wings and the air flow, was feeding a faulty reading to the flight computer. The two angle-of-attack sensors on either side of the jet’s nose differed by about 20 degrees in their measurements even during the ground taxi phase when the plane’s pitch was level. One of those readings was clearly completely wrong.\n\nOn any given flight, the flight computer takes data from only one of the angle-of-attack (AOA) sensors, apparently for simplicity of design. In this case, the computer interpreted the AOA reading as much too high an angle, suggesting an imminent stall that required MCAS to kick in and save the airplane.\n\nWhen the MCAS system pushed the nose down, the captain repeatedly pulled it back up, probably by using thumb switches on the control column. But each time, the MCAS system, as designed, kicked in to swivel the horizontal tail and push the nose back down again.\n\nThe data shows that after this cycle repeated 21 times, the captain ceded control to the first officer and MCAS then pushed the nose down twice more, this time without a pilot response.\n\nAfter a few more cycles of this struggle, with the horizontal tail now close to the limit of its movement, the captain resumed control and pulled back on the control column with high force.\n\nIt was too late. The plane dived into the sea at more than 500 miles per hour.\n\nPrevious crew handled similar situation\n\nRemarkably, the corresponding black-box-data charts from the same plane’s flight the previous day show that the pilots on that earlier flight encountered more or less exactly the same situation.\n\nAgain the AOA sensors were out of sync from the start. Again, the captain’s control column began shaking, a stall warning, at the moment of takeoff. Again, MCAS kicked in to push the nose down as soon as the flaps retracted.\n\nInitially that crew reacted like the pilots of JT610, but after a dozen cycles of the nose going down and pushing it back up, they turned off MCAS using two standard cutoff switches on the control pedestal “within minutes of experiencing the automatic nose down” movements, according to the NTSC preliminary investigation report.\n\nThere were no further uncommanded nose-down movements. For the rest of the flight, they controlled the jet’s pitch manually and everything was normal. The jet continued to its destination and landed safely.\n\nBecause the cockpit voice recorder has not yet been recovered from the sea bed, it’s a myste", - "title": "Pilots struggled against Boeing’s 737 MAX control system on doomed Lion Air flight", - "url": "https://www.seattletimes.com/business/boeing-aerospace/black-box-data-reveals-lion-air-pilots-struggle-against-boeings-737-max-flight-control-system/" - }, - { - "authors": ["Bloomberg"], - "cloudinary_id": "reports/www.businesstimes.com.sg/sites/default/files/styles/article_img_retina/public/image/2018/11/28/gn-2711-lionair_0.jpg?itok=4QuG887p", - "date_published": "2018-11-28", - "description": "[JAKARTA] A malfunctioning sensor at the centre of the investigation into the Oct 29 crash of a Lion Air jetliner into the Java Sea wasn't repaired before the fatal flight even though it had failed on the plane's previous trip, according to a preliminary investigative report. . Read more at The Business Times.", - "epoch_date_submitted": 1559347200, - "image_url": "https://www.businesstimes.com.sg/sites/default/files/styles/article_img_retina/public/image/2018/11/28/gn-2711-lionair_0.jpg?itok=4QuG887p", - "inputs_outputs": null, - "language": "en", - "report_number": 373, - "source_domain": "businesstimes.com.sg", - "submitters": ["Catherine Olsson"], - "tags": [], - "text": "A malfunctioning sensor at the centre of the investigation into the Oct 29 crash of a Lion Air jetliner into the Java Sea wasn't repaired before the fatal flight even though it had failed on the plane's previous trip, according to a preliminary investigative report.\n\n[JAKARTA] A malfunctioning sensor at the centre of the investigation into the Oct 29 crash of a Lion Air jetliner into the Java Sea wasn't repaired before the fatal flight even though it had failed on the plane's previous trip, according to a preliminary investigative report.\n\nA mechanic worked on other sensors and equipment during a night shift before the early morning departure, but not on the so-called angle-of-attack vane, according to Indonesia's National Transportation Safety Committee. Portions of the committee's report were viewed by Bloomberg News before a scheduled release on Wednesday.\n\nThe report doesn't offer a cause for the accident but provides the most detailed look so far into the chaotic minutes before the crash and into the steps that were taken to address malfunctions that occurred on the plane the previous night. On both flights, pilots reported that they had difficulty figuring out basic information such as speed and altitude.\n\nThe pilots on Flight 610, which plunged into the Java Sea more than 11 minutes after it took off from Jakarta, appeared not to understand what was happening to them as they radioed air traffic controllers asking for their altitude and speed. They said they had an unspecified \"flight control problem,\" according to the report. Lion Air spokesman Danang Prihantoro didn't answer calls seeking comments when tried on his mobile phone on Wednesday.\n\nsentifi.com Market voices on:\n\nEveryone aboard died in the crash, although there are conflicting counts for the death toll. A weight and balance calculation lists 188 people - two pilots, five flight attendants and 181 passengers. Another listing known as a voyage report lists an extra flight attendant for a total of 189 people.\n\nThe Boeing Co 737 Max 8's angle-of-attack sensor, which measures how high or low the plane's nose was pointed relative to the oncoming air, had malfunctioned on the previous flight as well as in the minutes before the crash, according to the report. The sensor erroneously concluded the nose was pointed too high and the aircraft was in danger of losing lift, prompting a stall warning in the cockpit and triggering safety software that attempted to put them into a dive.\n\nThe two sets of pilots reacted differently to the multiple errors messages and malfunctions. On the previous flight, the pilots were able to shut off the motor that was trying to push down the nose relatively soon after taking off.\n\nFor reasons that haven't been explained, the pilots on Flight 610 didn't take that step - which is part of a long-standing emergency procedure. The plane's crash-proof cockpit recorder hasn't been recovered, so investigators don't have much insight into what they were thinking as they responded to the emergency.\n\nBoeing declined to comment on the preliminary report. It is working as a technical adviser to the Indonesian investigation.\n\n\"We will analyze any additional information as it becomes available,\" the aircraft maker said in a statement on Tuesday. \"We are taking every measure to fully understand all aspects of this accident, working closely with the investigation team and the relevant government authorities.\"\n\nEarlier this month the manufacturer issued a bulletin to operators of the Max reminding them that such a cascade of failures could be addressed by an existing emergency procedure. The manufacturer has said it's confident in the safety of the latest version of its 737 model.\n\nEARLIER FLIGHT\n\nOn the Oct 28 flight that landed safely in Jakarta, the captain told investigators that he scanned cockpit instruments and determined that the copilot's readings matched a third standby system and were accurate. He turned over control of the plane to the copilot.\n\nBy contrast, the captain on the flight that crashed radioed a controller about a minute before the plane disappeared from radar to say that all of the plane's altitude gauges were different and they couldn't determine how high they were.\n\nInvestigators will focus on how the airline performed maintenance on the plane.\n\nThe captain on the Oct 28 flight reported problems with speed and altitude readings and with a system called Feel Differential Pressure, which controls the force pilots need to push or pull the control column that raises and lowers the nose, according to the report. There was no indication that the angle of attack sensor was malfunctioning.\n\nAn angle of attack sensor had been replaced and tested just before the Oct 28 flight. According to flight data from the plane, the sensor on the left was reading about 20 degrees differently from the one on the right.\n\nOn both flights, a device known as a stick shaker - a warning that the wings are about to lose lift, which vibrates the control column", - "title": "Faulty Lion Air sensor wasn’t fixed before crash, preliminary report finds", - "url": "https://www.businesstimes.com.sg/transport/faulty-lion-air-sensor-wasn%E2%80%99t-fixed-before-crash-preliminary-report-finds" - }, - { - "authors": ["James Glanz", "Muktita Suhartono", "Hannah Beech"], - "cloudinary_id": "reports/static01.nyt.com/images/2018/11/28/world/28lionair1-promo/28lionair1-promo-facebookJumbo-v2.jpg", - "date_published": "2018-11-28", - "description": "Information from Lion Air Flight 610, which crashed off Indonesia last month, supports the theory that a new Boeing anti-stall system played a role.", - "epoch_date_submitted": 1559347200, - "image_url": "https://static01.nyt.com/images/2018/11/28/world/28lionair1-promo/28lionair1-promo-facebookJumbo-v2.jpg", - "inputs_outputs": null, - "language": "en", - "report_number": 372, - "source_domain": "nytimes.com", - "submitters": ["Catherine Olsson"], - "tags": [], - "text": "Investigators have yet to recover the cockpit voice recorder, which could provide further insight into the steps taken by the pilots and whether they followed the correct procedures.\n\nDespite Boeing’s insistence that the proper procedures were in the handbook, also called the emergency checklist, pilots have said since the accident that Boeing had not been clear about one potentially vital difference between the system on the new 737s and the older models. In the older versions, pilots could help address the problem of the nose being forced down improperly — a situation known as “runaway stabilizer trim” — by pulling back on the control column in front of them, the pilots say.\n\nIn the latest 737 generation, called the Max, that measure does not work, they said, citing information they have received since the crash. The pilots on Lion Air Flight 610 appear to have forcefully pulled back on their control columns to no avail, before the final dive, according to the information from the flight data recorder.\n\nCapt. Dennis Tajer, spokesman for the American Airlines pilot union and a 737 pilot, said he could not comment on any aspect of the investigation. But, he said, “in the previous model of the 737, pulling back on the control column, Boeing says will stop a stabilizer runaway.”\n\nInformation provided to American Airlines from Boeing since the crash, Captain Tajer said, “specifically says that pulling back on the control column in the Max will not stop the runaway if M.C.A.S. is triggered. That is an important difference to know.”\n\nBoeing said in its statement on Tuesday that the existing procedures covered the latest 737 model.\n\nBulletins from Boeing and the Federal Aviation Administration of the United States since the crash indicate that pilots could overcome an incorrectly activated M.C.A.S. with a series of steps. First, they would have had to activate switches on the outside of the control columns in front of both the pilot and co-pilot. Those switches are for electrically controlling the trim — the angle of the stabilizers on the plane’s tail. The pilot of Flight 610 appears to have done that repeatedly to bring the nose up, but the M.C.A.S. reactivated each time, as it was designed to do, forcing the nose back down, and the pilot had to repeat the process again and again.", - "title": "In Indonesia Lion Air Crash, Black Box Data Reveal Pilots’ Struggle to Regain Control", - "url": "https://www.nytimes.com/2018/11/27/world/asia/indonesia-lion-air-crash-.html" - } - ], - "title": "Crashes with Maneuvering Characteristics Augmentation System (MCAS)", - "tsne": { - "x": 0.11154986524731458, - "y": 0.6203396517870035 - } - } - } -} diff --git a/site/gatsby-site/cypress/fixtures/incidents/fullIncident50.json b/site/gatsby-site/cypress/fixtures/incidents/fullIncident50.json deleted file mode 100644 index 0587fba0fc..0000000000 --- a/site/gatsby-site/cypress/fixtures/incidents/fullIncident50.json +++ /dev/null @@ -1,958 +0,0 @@ -{ - "data": { - "incident": { - "AllegedDeployerOfAISystem": [ - { - "__typename": "Entity", - "entity_id": "the-dao", - "name": "The DAO" - } - ], - "AllegedDeveloperOfAISystem": [ - { - "__typename": "Entity", - "entity_id": "the-dao", - "name": "The DAO" - } - ], - "AllegedHarmedOrNearlyHarmedParties": [ - { - "__typename": "Entity", - "entity_id": "dao-token-holders", - "name": "DAO Token Holders" - } - ], - "__typename": "Incident", - "date": "2016-06-17", - "description": "On June 18, 2016, an attacker successfully exploited a vulnerability in The Decentralized Autonomous Organization (The DAO) on the Ethereum blockchain to steal 3.7M Ether valued at $70M.", - "editor_dissimilar_incidents": [], - "editor_notes": null, - "editor_similar_incidents": [], - "editors": [ - { - "__typename": "User", - "first_name": "Sean", - "last_name": "McGregor", - "userId": "619b47ea5eed5334edfa3bbc" - } - ], - "embedding": { - "__typename": "IncidentEmbedding", - "from_reports": [ - 905, - 903, - 902, - 901, - 900, - 899, - 898, - 897, - 896, - 893, - 892, - 889, - 888, - 887, - 886, - 885, - 884, - 883, - 881, - 880, - 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} - } -} diff --git a/site/gatsby-site/cypress/fixtures/incidents/incident.json b/site/gatsby-site/cypress/fixtures/incidents/incident.json deleted file mode 100644 index 19868022fe..0000000000 --- a/site/gatsby-site/cypress/fixtures/incidents/incident.json +++ /dev/null @@ -1,294 +0,0 @@ -{ - "data": { - "incident": { - "AllegedDeployerOfAISystem": [ - { - "entity_id": "youtube", - "name": "YouTube" - } - ], - "AllegedDeveloperOfAISystem": [ - { - "entity_id": "youtube", - "name": "YouTube" - } - ], - "AllegedHarmedOrNearlyHarmedParties": [ - { - "entity_id": "children", - "name": "Children" - } - ], - "__typename": "Incident", - "date": "2015-05-19", - "description": "YouTube’s content filtering and recommendation algorithms exposed children to disturbing and inappropriate videos.", - "editor_notes": "This incident was originally submitted by Sean McGregor.", - "editor_dissimilar_incidents": [], - "editor_similar_incidents": [], - "flagged_dissimilar_incidents": [], - "epoch_date_modified": 1589385600, - 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], - "title": "The DAO Hack" - } - } -} diff --git a/site/gatsby-site/cypress/fixtures/incidents/updateOneIncident.json b/site/gatsby-site/cypress/fixtures/incidents/updateOneIncident.json deleted file mode 100644 index dc203924d2..0000000000 --- a/site/gatsby-site/cypress/fixtures/incidents/updateOneIncident.json +++ /dev/null @@ -1,82 +0,0 @@ -{ - "data": { - "updateOneIncident": { - "__typename": "Incident", - "title": "Test Incident", - "description": "This is a test description", - "date": "2015-05-19", - "nlp_similar_incidents": [], - "incident_id": 1, - "editors": [], - "editor_notes": "This is a test", - "embedding": { - "from_reports": [1, 2, 3], - "vector": [-0.023964308202266693, 0.05607481673359871, -0.00004077623088960536] - }, - "reports": [ - { - "__typename": "Report", - "report_number": 1 - }, - { - "__typename": "Report", - "report_number": 2 - }, - { - "__typename": "Report", - "report_number": 3 - }, - { - "__typename": "Report", - "report_number": 4 - }, - { - "__typename": "Report", - 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"report_number": 10 - }, - { - "__typename": "Report", - "report_number": 12 - } - ], - "title": "Google’s YouTube Kids App Presents Inappropriate Content" - } - } -} \ No newline at end of file diff --git a/site/gatsby-site/cypress/fixtures/reports/flagged.json b/site/gatsby-site/cypress/fixtures/reports/flagged.json deleted file mode 100644 index f68ddbb23e..0000000000 --- a/site/gatsby-site/cypress/fixtures/reports/flagged.json +++ /dev/null @@ -1,40 +0,0 @@ -{ - "data": { - "updateOneReport": { - "__typename": "Report", - "_id": "5d34b8c29ced494f010ed471", - "authors": ["Alain Sherter"], - "date_downloaded": "2019-04-13", - "date_published": "2014-09-26", - "flag": true, - "image_url": "https://cbsnews2.cbsistatic.com/hub/i/r/2014/09/25/aa2708c1-7e84-4573-8f79-add177a690cd/thumbnail/1200x630/2505bd91ab74f8e2ce3e7c6f8a5d77f3/starbucks.jpg", - "report_number": 1, - "submitters": ["Catherine Olsson"], - "tags": [], - "text": "Liberte Locke, a 32-year-old \"barista\" at a Starbucks (SBUX) in New York City, is fed up.\n\"Starbucks' attitude is that there's always someone else who can do the job,\" she said in running through her complaints about life at the java giant.\nIf that isn't necessarily the consensus among Starbucks workers, interviews with nine current and former baristas at the company make clear it's not an isolated opinion, either. Even those who say they like their job paint a picture of a business that underpays front-line workers, enforces work rules arbitrarily, and too often fails to strike a balance between corporate goals and employee needs.\nOf course, such complaints are nothing new in retail, where low pay and erratic schedules are the norm. But by its own account, Starbucks is no ordinary company and is ostensibly a far cry from the fast-food outlets now facing a nationwide uprising by employees tired of working for peanuts.\nThat's evident in the company's recruitment pitch. Starbucks invites job-seekers to \"become a part of something bigger and inspire positive change in the world,\" describing it as a chance to discover a \"deep sense of purpose.\"\nDamage control\nThat image suffered a serious blow last month after The New York Times vividly chronicled a Starbucks worker struggling with the company's scheduling practices. The story, which centered on a 22-year-old barista and single mother, amounted to a public relations nightmare for Starbucks. Perhaps not coincidentally, within days of the story's publication top executives were promising reform.\nIn a memo to employees earlier this month, for instance, Chief Operating Officer Troy Alstead vowed to \"transform the U.S. partner experience,\" referring to Starbucks' more than 130,000 baristas. Inviting worker feedback, he said Starbucks will examine its approach to employee pay, revisit its dress code, make it easier for people to ask for time off, and consider other changes aimed at helping baristas balance work and their personal lives.\nAmong other changes, the company said it would end the practice of \"clopening,\" when an employee responsible for closing a store late at night is also assigned to open it early in the morning.\n\"We recognize that we can do more for our partners who wear the apron every day,\" he wrote.\nSome baristas did not feel this August memo from Starbucks went far enough in proposing ways to improve work conditions, so they marked it up with their own ideas. CBS\nAlthough Starbucks workers welcome this pledge to respect the apron, they fear the company is more intent on dousing the PR flames than on genuinely improving employees' experience. After the retailer last month sent an email to workers outlining possible solutions to the kind of scheduling problems and related issues detailed by the Times, a group of baristas gave the proposal a C- and posted online a marked-up version of the memo listing their own demands (see image above).\n\"We hope you're ready for a commitment to give us schedules that don't mess with taking care of kids, going to school or holding onto that second job we need because Sbux wages don't make ends meet,\" wrote the baristas, who are working with a union-backed labor group, the Center for Popular Democracy.\nRetail jungle\nDespite the recent media focus on Starbucks, the company's labor practices are generally no worse than those of many large retailers. In some ways they're better, with the company offering health care to part-time, as well as full-time, workers; unusually generous 401(k) matching contributions; annual stock grants to employees; and tuition reimbursement.\nStarbucks highlights such benefits as an example of its commitment to employees. \"Sharing success with one another has been core to the company's heritage for more than 40 years,\" Alstead said in the September memo.\nMeanwhile, some baristas say they enjoy their work and feel valued by Starbucks. \"It's a decent place to work, and my manager and co-workers are great,\" said one employee who asked not to be identified.\nBut other current and former workers claim Starbucks has changed in recent years, saying that corporate leaders' intense focus on slashing costs has short-circuited its professed commitment to workers. Mostly, they say Starbucks doesn't listen to employees and even punishes those who identify problems.\n\"The biggest problem is that baristas don't have a voice,\" said Sarah Madden, a former Starbucks barista who left the company this spring after two years with the coffee vendor. \"They can't speak to issues that they know exist. Workers know how to fix them, but when [they] speak up there are serious repercussions -- your hours get cut, you're transferred to another store or isolated from other people.\"\nEmployees interviewed for this article said one result of Starbucks' cost-containment push is that stores are frequently understaffed, hurting customer service and forcing managers to scramble to find staff. That problem is common across the big-box s", - "title": "For some Starbucks workers, job leaves bitter taste", - "description": "Description For some Starbucks workers, job leaves bitter taste", - "url": "https://www.cbsnews.com/news/for-some-starbucks-workers-job-leaves-bitter-taste/", - "editor_notes": "", - "plain_text": "Liberte Locke, a 32-year-old \"barista\" at a Starbucks (SBUX) in New York City, is fed up.\n\"Starbucks' attitude is that there's always someone else who can do the job,\" she said in running through her complaints about life at the java giant.\nIf that isn't necessarily the consensus among Starbucks workers, interviews with nine current and former baristas at the company make clear it's not an isolated opinion, either. Even those who say they like their job paint a picture of a business that underpays front-line workers, enforces work rules arbitrarily, and too often fails to strike a balance between corporate goals and employee needs.\nOf course, such complaints are nothing new in retail, where low pay and erratic schedules are the norm. But by its own account, Starbucks is no ordinary company and is ostensibly a far cry from the fast-food outlets now facing a nationwide uprising by employees tired of working for peanuts.\nThat's evident in the company's recruitment pitch. Starbucks invites job-seekers to \"become a part of something bigger and inspire positive change in the world,\" describing it as a chance to discover a \"deep sense of purpose.\"\nDamage control\nThat image suffered a serious blow last month after The New York Times vividly chronicled a Starbucks worker struggling with the company's scheduling practices. The story, which centered on a 22-year-old barista and single mother, amounted to a public relations nightmare for Starbucks. Perhaps not coincidentally, within days of the story's publication top executives were promising reform.\nIn a memo to employees earlier this month, for instance, Chief Operating Officer Troy Alstead vowed to \"transform the U.S. partner experience,\" referring to Starbucks' more than 130,000 baristas. Inviting worker feedback, he said Starbucks will examine its approach to employee pay, revisit its dress code, make it easier for people to ask for time off, and consider other changes aimed at helping baristas balance work and their personal lives.\nAmong other changes, the company said it would end the practice of \"clopening,\" when an employee responsible for closing a store late at night is also assigned to open it early in the morning.\n\"We recognize that we can do more for our partners who wear the apron every day,\" he wrote.\nSome baristas did not feel this August memo from Starbucks went far enough in proposing ways to improve work conditions, so they marked it up with their own ideas. CBS\nAlthough Starbucks workers welcome this pledge to respect the apron, they fear the company is more intent on dousing the PR flames than on genuinely improving employees' experience. After the retailer last month sent an email to workers outlining possible solutions to the kind of scheduling problems and related issues detailed by the Times, a group of baristas gave the proposal a C- and posted online a marked-up version of the memo listing their own demands (see image above).\n\"We hope you're ready for a commitment to give us schedules that don't mess with taking care of kids, going to school or holding onto that second job we need because Sbux wages don't make ends meet,\" wrote the baristas, who are working with a union-backed labor group, the Center for Popular Democracy.\nRetail jungle\nDespite the recent media focus on Starbucks, the company's labor practices are generally no worse than those of many large retailers. In some ways they're better, with the company offering health care to part-time, as well as full-time, workers; unusually generous 401(k) matching contributions; annual stock grants to employees; and tuition reimbursement.\nStarbucks highlights such benefits as an example of its commitment to employees. \"Sharing success with one another has been core to the company's heritage for more than 40 years,\" Alstead said in the September memo.\nMeanwhile, some baristas say they enjoy their work and feel valued by Starbucks. \"It's a decent place to work, and my manager and co-workers are great,\" said one employee who asked not to be identified.\nBut other current and former workers claim Starbucks has changed in recent years, saying that corporate leaders' intense focus on slashing costs has short-circuited its professed commitment to workers. Mostly, they say Starbucks doesn't listen to employees and even punishes those who identify problems.\n\"The biggest problem is that baristas don't have a voice,\" said Sarah Madden, a former Starbucks barista who left the company this spring after two years with the coffee vendor. \"They can't speak to issues that they know exist. Workers know how to fix them, but when [they] speak up there are serious repercussions -- your hours get cut, you're transferred to another store or isolated from other people.\"\nEmployees interviewed for this article said one result of Starbucks' cost-containment push is that stores are frequently understaffed, hurting customer service and forcing managers to scramble to find staff. That problem is common across the big-box s", - "embedding": { - "from_text_hash": "", - "vector": [] - }, - "user": { - "userId": "642188372947d07020c1319d" - }, - "is_incident_report": true, - "source_domain": "cbsnews.com", - "cloudinary_id": "reports/cbsnews2.cbsistatic.com/hub/i/r/2014/09/25/aa2708c1-7e84-4573-8f79-add177a690cd/thumbnail/1200x630/2505bd91ab74f8e2ce3e7c6f8a5d77f3/starbucks.jpg", - "epoch_date_submitted": 1559347200, - "epoch_date_published": 1559347200, - "epoch_date_modified": 1559347200, - "epoch_date_downloaded": 1559347200, - "date_submitted": "2019-06-01", - "date_modified": "2019-06-02", - "language": "en", - "quiet": false - } - } -} diff --git a/site/gatsby-site/cypress/fixtures/reports/issueWithTranslations.json b/site/gatsby-site/cypress/fixtures/reports/issueWithTranslations.json deleted file mode 100644 index a304a6f318..0000000000 --- a/site/gatsby-site/cypress/fixtures/reports/issueWithTranslations.json +++ /dev/null @@ -1,50 +0,0 @@ -{ - "data": { - "report": { - "authors": [ - "Marco Acevedo" - ], - "date_downloaded": "2019-04-13", - "date_published": "2015-07-11", - "flag": null, - "image_url": "https://assets.change.org/photos/0/yb/id/eYyBIdJOMHpqcty-1600x900-noPad.jpg?1523726975", - "report_number": 10, - "submitters": [ - "Roman Yampolskiy" - ], - "tags": [ - "Test Tag" - ], - "text": "## Video still of a reproduced version of Minnie Mouse\n\nWhich appeared on the now-suspended Simple Fun channel Simple Fun.", - "plain_text": "Video still of a reproduced version of Minnie Mouse\n\nWhich appeared on the now-suspended Simple Fun channel Simple Fun.", - "title": "Remove YouTube Kids app until it eliminates its inappropriate content", - "description": "Description Remove YouTube Kids app until it eliminates its inappropriate content", - "url": "https://www.change.org/p/remove-youtube-kids-app-until-it-eliminates-its-inappropriate-content", - "editor_notes": "", - "language": "en", - "translations_es": { - "__typename": "ReportTranslation", - "title": "Este es el Título en español", - "text": "Este es un texto de prueba que tiene un largo mayor a ochenta caracteres (en español)" - }, - "translations_en": { - "__typename": "ReportTranslation", - "title": "", - "text": "" - }, - "translations_fr": { - "__typename": "ReportTranslation", - "title": "C'est le Titre en français", - "text": "Il s'agit d'un texte de test de plus de quatre-vingts caractères - lorem ipsum (en français)" - }, - "translations_ja": { - "__typename": "ReportTranslation", - "title": "これは日本語でのタイトルです", - "text": "解サオライ協立なーづ民手ぶみドに即記朝ぐ奥置ぱで地更トるあて栄厚ぜづを祭屋ん来派どてゃ読速ヘ誌約カタシネ原39業理る。外ヒヱフ社第むせゆ由更混ソエ夕野しりすよ顔飛リの兆基う公言や置17謝后嘘5供フキヌア星集ヘラ辞勘壇崇さびわ。（日本語で）" - }, - "date_modified": "2023-01-01", - "epoch_date_modified": 1672531200, - "is_incident_report": false - } - } -} \ No newline at end of file diff --git a/site/gatsby-site/cypress/fixtures/reports/probablyRelatedReports.json b/site/gatsby-site/cypress/fixtures/reports/probablyRelatedReports.json deleted file mode 100644 index 8ffaaccfce..0000000000 --- a/site/gatsby-site/cypress/fixtures/reports/probablyRelatedReports.json +++ /dev/null @@ -1,24 +0,0 @@ -{ - "data": { - "reports": [ - { - "__typename": "Report", - "report_number": 1, - "title": "Report 1 test title", - "url": "https://www.bbc.com/news/world-us-canada-46468719" - }, - { - "__typename": "Report", - "report_number": 2, - "title": "Report 2 test title", - "url": "https://www.bbc.com/news/world-us-canada-46468719" - }, - { - "__typename": "Report", - "report_number": 3, - "title": "Report 3 test title", - "url": "https://www.bbc.com/news/world-us-canada-46468719" - } - ] - } -} \ No newline at end of file diff --git a/site/gatsby-site/cypress/fixtures/reports/report.json b/site/gatsby-site/cypress/fixtures/reports/report.json deleted file mode 100644 index 1ae223f554..0000000000 --- a/site/gatsby-site/cypress/fixtures/reports/report.json +++ /dev/null @@ -1,37 +0,0 @@ -{ - "data": { - "report": { - "authors": ["Marco Acevedo"], - "cloudinary_id": "reports/assets.change.org/photos/0/yb/id/eYyBIdJOMHpqcty-1600x900-noPad.jpg?1523726975", - "date_downloaded": "2019-04-13", - "date_modified": "2020-06-14", - "date_published": "2015-07-11", - "date_submitted": "2019-06-01", - "editor_notes": "", - "embedding": { - "from_text_hash": "12256b4f3816b968a2224017eac1bf7d5c6b98e5", - "vector": [-0.09368586540222168, 0.02987837791442871, 0.03945714980363846] - }, - "epoch_date_downloaded": 1555113600, - "epoch_date_modified": 1592092800, - "epoch_date_published": 1436572800, - "epoch_date_submitted": 1559347200, - "flag": null, - "image_url": "https://assets.change.org/photos/0/yb/id/eYyBIdJOMHpqcty-1600x900-noPad.jpg?1523726975", - "is_incident_report": true, - "language": "en", - "plain_text": "Videos filled with profanity, sexually explicit material, alcohol, smoking, and drug references - this is what parents are finding on Google’s YouTube Kids app. That’s right - its kids app. Now, parents across the country are calling on Google to remove the app until it can guarantee the total elimination of this inappropriate content.\n\nWhen my neighbors told me about the horrible adult content popping up on the Youtube Kids app, I thought there must be a mistake. Why would Google market an app as “a family-friendly place to explore” and not have proper safeguards in place? Unfortunately, it turned out to be true. And I’ve since learned of the numerous complaints filed to the Federal Trade Commission about this very problem.\n\nEven worse, Google’s response has been laughable. They tell parents to simply flag inappropriate material or set new filters. As a father of two, it makes me angry when a large company like Google doesn’t take responsibility for its kids’ products. Parents are being sold on an app built for kids 5 and under that is supposed to keep them safe from adult content. Parents like myself are joining forces to hold Google accountable.\n\nTell Google to remove the YouTube Kids app until it can live up to its marketing.\n\nThe solution is simple: only allow content pre-approved for ages 5 and under to appear on the app, and don’t allow ads clearly meant for adults. Unless it can live up to expectations, the app should be removed.\n\nParents are not the only ones outraged. The media has blasted Google’s app, calling it “the most anti-family idea ever to come out of Silicon Valley,\" and reporting that it “ignores basic protections for children.”\n\nWith your support, we can get Google to remove YouTube Kids until the proper protections are in place.\n\nThese are examples of videos encountered on YouTube Kids:\n\nA graphic lecture discussing hardcore pornography by Cindy Gallop:\n\nhttps://www.youtube.com/watch?v=EgtcEq7jpAk\n\nHow to make chlorine gas with household products (chemical weapon used in Syria):\n\nhttps://www.youtube.com/watch?v=DF2CXHvh8uI\n\nHow to tie a noose:\n\nhttps://www.youtube.com/watch?v=TpAA2itjI34\n\nHow to throw knives:\n\nhttps://www.youtube.com/watch?v=NGgzn1haQ-E\n\nA guy tasting battery acid:\n\nhttps://www.youtube.com/watch?v=gif-OWNjJSw\n\nHow to use a chainsaw:\n\nhttps://www.youtube.com/watch?v=Kk28thdgCEU\n\nA “Sesame Street” episode dubbed with long strings of expletives:\n\nhttps://www.youtube.com/watch?v=kVkqzE-iiEY\n\nReferences to pedophilia in a homemade video reviewing a “My Little Pony” episode:\n\nhttps://www.youtube.com/watch?v=7K9uH4d-HnU\n\nA DIY video on conducting illegal piracy, featuring pictures of marijuana leaves:\n\nhttps://www.youtube.com/watch?v=dZDF5uqORA0", - "report_number": 10, - "source_domain": "change.org", - "submitters": ["Roman Yampolskiy"], - "tags": [], - "text": "Videos filled with profanity, sexually explicit material, alcohol, smoking, and drug references - this is what parents are finding on Google’s YouTube Kids app. That’s right - its kids app. Now, parents across the country are calling on Google to remove the app until it can guarantee the total elimination of this inappropriate content.\n\nWhen my neighbors told me about the horrible adult content popping up on the Youtube Kids app, I thought there must be a mistake. Why would Google market an app as “a family-friendly place to explore” and not have proper safeguards in place? Unfortunately, it turned out to be true. And I’ve since learned of the numerous complaints filed to the Federal Trade Commission about this very problem.\n\nEven worse, Google’s response has been laughable. They tell parents to simply flag inappropriate material or set new filters. As a father of two, it makes me angry when a large company like Google doesn’t take responsibility for its kids’ products. Parents are being sold on an app built for kids 5 and under that is supposed to keep them safe from adult content. Parents like myself are joining forces to hold Google accountable.\n\nTell Google to remove the YouTube Kids app until it can live up to its marketing.\n\nThe solution is simple: only allow content pre-approved for ages 5 and under to appear on the app, and don’t allow ads clearly meant for adults. Unless it can live up to expectations, the app should be removed.\n\nParents are not the only ones outraged. The media has blasted Google’s app, calling it “the most anti-family idea ever to come out of Silicon Valley,\" and reporting that it “ignores basic protections for children.”\n\nWith your support, we can get Google to remove YouTube Kids until the proper protections are in place.\n\nThese are examples of videos encountered on YouTube Kids:\n\nA graphic lecture discussing hardcore pornography by Cindy Gallop:\n\nhttps://www.youtube.com/watch?v=EgtcEq7jpAk\n\nHow to make chlorine gas with household products (chemical weapon used in Syria):\n\nhttps://www.youtube.com/watch?v=DF2CXHvh8uI\n\nHow to tie a noose:\n\nhttps://www.youtube.com/watch?v=TpAA2itjI34\n\nHow to throw knives:\n\nhttps://www.youtube.com/watch?v=NGgzn1haQ-E\n\nA guy tasting battery acid:\n\nhttps://www.youtube.com/watch?v=gif-OWNjJSw\n\nHow to use a chainsaw:\n\nhttps://www.youtube.com/watch?v=Kk28thdgCEU\n\nA “Sesame Street” episode dubbed with long strings of expletives:\n\nhttps://www.youtube.com/watch?v=kVkqzE-iiEY\n\nReferences to pedophilia in a homemade video reviewing a “My Little Pony” episode:\n\nhttps://www.youtube.com/watch?v=7K9uH4d-HnU\n\nA DIY video on conducting illegal piracy, featuring pictures of marijuana leaves:\n\nhttps://www.youtube.com/watch?v=dZDF5uqORA0", - "title": "Remove YouTube Kids app until it eliminates its inappropriate content", - "description": "Description Remove YouTube Kids app until it eliminates its inappropriate content", - "url": "https://www.change.org/p/remove-youtube-kids-app-until-it-eliminates-its-inappropriate-content", - "user": { - "userId": "63320ce63ec803072c9f529c" - } - } - } -} diff --git a/site/gatsby-site/cypress/fixtures/reports/reportSiblings.json b/site/gatsby-site/cypress/fixtures/reports/reportSiblings.json deleted file mode 100644 index 0c87479336..0000000000 --- a/site/gatsby-site/cypress/fixtures/reports/reportSiblings.json +++ /dev/null @@ -1,26 +0,0 @@ -{ - "data": { - "reports": [ - { - "report_number": 1, - "embedding": { - "from_text_hash": "2c8b97b95507aa6b23a29e6c80e69cac1de41e11", - "vector": [ - -0.09760236740112305, 0.018021853640675545, 0.10105229914188385, 0.09371653199195862, - -0.10924399644136429, -0.03652585297822952, -0.037053391337394714 - ] - } - }, - { - "report_number": 2, - "embedding": { - "from_text_hash": "2c8b97b95507aa6b23a29e6c80e69cac1de41e11", - "vector": [ - -0.09760236740112305, 0.018021853640675545, 0.10105229914188385, 0.09371653199195862, - -0.10924399644136429, -0.03652585297822952, -0.037053391337394714 - ] - } - } - ] - } -} diff --git a/site/gatsby-site/cypress/fixtures/reports/reportWithTranslations.json b/site/gatsby-site/cypress/fixtures/reports/reportWithTranslations.json deleted file mode 100644 index 9cdcfb4a55..0000000000 --- a/site/gatsby-site/cypress/fixtures/reports/reportWithTranslations.json +++ /dev/null @@ -1,44 +0,0 @@ -{ - "data": { - "report": { - "authors": ["Marco Acevedo"], - "date_downloaded": "2019-04-13", - "date_published": "2015-07-11", - "flag": null, - "image_url": "https://assets.change.org/photos/0/yb/id/eYyBIdJOMHpqcty-1600x900-noPad.jpg?1523726975", - "report_number": 10, - "submitters": ["Roman Yampolskiy"], - "tags": ["Test Tag"], - "text": "## Video still of a reproduced version of Minnie Mouse\n\nWhich appeared on the now-suspended Simple Fun channel Simple Fun.", - "plain_text": "Video still of a reproduced version of Minnie Mouse\n\nWhich appeared on the now-suspended Simple Fun channel Simple Fun.", - "title": "Remove YouTube Kids app until it eliminates its inappropriate content", - "description": "Description YouTube Kids app until it eliminates its inappropriate content", - "url": "https://www.change.org/p/remove-youtube-kids-app-until-it-eliminates-its-inappropriate-content", - "editor_notes": "", - "language": "en", - "translations_es": { - "__typename": "ReportTranslation", - "text": "Este es un texto de prueba que tiene un largo mayor a ochenta caracteres (en español)", - "title": "Este es el Título en español" - }, - "translations_en": { - "__typename": "ReportTranslation", - "text": "", - "title": "" - }, - "translations_fr": { - "__typename": "ReportTranslation", - "text": "Il s'agit d'un texte de test de plus de quatre-vingts caractères - lorem ipsum (en français)", - "title": "C'est le Titre en français" - }, - "translations_ja": { - "__typename": "ReportTranslation", - "text": "解サオライ協立なーづ民手ぶみドに即記朝ぐ奥置ぱで地更トるあて栄厚ぜづを祭屋ん来派どてゃ読速ヘ誌約カタシネ原39業理る。外ヒヱフ社第むせゆ由更混ソエ夕野しりすよ顔飛リの兆基う公言や置17謝后嘘5供フキヌア星集ヘラ辞勘壇崇さびわ。（日本語で）", - "title": "これは日本語でのタイトルです" - }, - "date_modified": "2023-01-01", - "epoch_date_modified": 1672531200, - "is_incident_report": true - } - } -} diff --git a/site/gatsby-site/cypress/fixtures/reports/reports.json b/site/gatsby-site/cypress/fixtures/reports/reports.json deleted file mode 100644 index 754ce699a1..0000000000 --- a/site/gatsby-site/cypress/fixtures/reports/reports.json +++ /dev/null @@ -1,139661 +0,0 @@ -{ - "data": { - "incidents": [ - { - "AllegedDeployerOfAISystem": [ - { - "__typename": "Entity", - "entity_id": "youtube", - "name": "YouTube" - } - ], - "AllegedDeveloperOfAISystem": [ - { - "__typename": "Entity", - "entity_id": "youtube", - "name": "YouTube" - } - ], - "AllegedHarmedOrNearlyHarmedParties": [ - { - "__typename": "Entity", - "entity_id": "children", - "name": "Children" - } - ], - "__typename": "Incident", - 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"reports": [ - { - "__typename": "Report", - "authors": ["Tencent Keen Security Lab"], - "epoch_date_downloaded": 1644883200, - "epoch_date_modified": 1644883200, - "epoch_date_submitted": 1644883200, - "flag": null, - "report_number": 1519, - "source_domain": "keenlab.tencent.com", - "submitters": ["Anonymous"], - "title": "Tencent Keen Security Lab: Experimental Security Research of Tesla Autopilot", - "url": "https://keenlab.tencent.com/en/2019/03/29/Tencent-Keen-Security-Lab-Experimental-Security-Research-of-Tesla-Autopilot/" - }, - { - "__typename": "Report", - "authors": ["Evan Ackerman"], - "epoch_date_downloaded": 1644883200, - "epoch_date_modified": 1644883200, - "epoch_date_submitted": 1644883200, - "flag": null, - "report_number": 1518, - "source_domain": "spectrum.ieee.org", - "submitters": ["Anonymous"], - "title": "Three Small Stickers in Intersection Can Cause Tesla Autopilot to Swerve Into Wrong Lane", - "url": "https://spectrum.ieee.org/three-small-stickers-on-road-can-steer-tesla-autopilot-into-oncoming-lane" - 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"submitters": ["Sonali Pednekar (CSET)"], - "title": "Taser manufacturer drops proposal to put armed drones in schools after mass resignation from ethics board", - "url": "https://www.abc.net.au/news/2022-06-06/ethics-panel-members-resign-axon-taser-drones-mass-shootings/101129270" - } - ] - }, - { - "AllegedDeployerOfAISystem": [ - { - "__typename": "Entity", - "entity_id": "cruise", - "name": "Cruise" - } - ], - "AllegedDeveloperOfAISystem": [ - { - "__typename": "Entity", - "entity_id": "cruise", - "name": "Cruise" - } - ], - "AllegedHarmedOrNearlyHarmedParties": [ - { - "__typename": "Entity", - "entity_id": "san-francisco-traffic-participants", - "name": "San Francisco traffic participants" - }, - { - "__typename": "Entity", - "entity_id": "san-francisco-public", - "name": "San Francisco public" - } - ], - "__typename": "Incident", - "editor_dissimilar_incidents": [], - "editor_similar_incidents": [], - "embedding": { - "__typename": "IncidentEmbedding", - "from_reports": [1799, 1798, 1797, 1796], - 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"authors": ["Tom Simonite"], - "epoch_date_downloaded": 1669161600, - "epoch_date_modified": 1669248000, - "epoch_date_submitted": 1669161600, - "flag": null, - "report_number": 2276, - "source_domain": "wired.com", - "submitters": ["Anonymous"], - "title": "It Began as an AI-Fueled Dungeon Game. 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Ricks"], - "epoch_date_downloaded": 1673740800, - "epoch_date_modified": 1673740800, - "epoch_date_submitted": 1673740800, - "flag": null, - "report_number": 2501, - "source_domain": "washingtonpost.com", - "submitters": ["Khoa Lam"], - "title": " Investigation Finds U.S. Missiles Downed Navy Jet", - "url": "https://www.washingtonpost.com/archive/politics/2004/12/11/investigation-finds-us-missiles-downed-navy-jet/323e76f1-31d5-49c1-a27a-df9f389b0532/" - }, - { - "__typename": "Report", - "authors": ["Reuters"], - "epoch_date_downloaded": 1673740800, - "epoch_date_modified": 1673740800, - "epoch_date_submitted": 1673740800, - "flag": null, - "report_number": 2499, - "source_domain": "reuters.com", - "submitters": ["Khoa Lam"], - "title": "Woman Sues Raytheon Over Iraq Friendly Fire Death", - "url": "https://www.reuters.com/article/sppage023-n06282955-oisbi-idUKN0628295520070207" - } - ] - } - ] - } -} diff --git a/site/gatsby-site/cypress/fixtures/reports/unflagged.json b/site/gatsby-site/cypress/fixtures/reports/unflagged.json deleted file mode 100644 index ac899774f1..0000000000 --- a/site/gatsby-site/cypress/fixtures/reports/unflagged.json +++ /dev/null @@ -1,40 +0,0 @@ -{ - "data": { - "report": { - "__typename": "Report", - "_id": "5d34b8c29ced494f010ed471", - "authors": ["Alain Sherter"], - "date_downloaded": "2019-04-13", - "date_published": "2014-09-26", - "flag": null, - "image_url": "https://cbsnews2.cbsistatic.com/hub/i/r/2014/09/25/aa2708c1-7e84-4573-8f79-add177a690cd/thumbnail/1200x630/2505bd91ab74f8e2ce3e7c6f8a5d77f3/starbucks.jpg", - "report_number": 1, - "submitters": ["Catherine Olsson"], - "tags": [], - "text": "Liberte Locke, a 32-year-old \"barista\" at a Starbucks (SBUX) in New York City, is fed up.\n\"Starbucks' attitude is that there's always someone else who can do the job,\" she said in running through her complaints about life at the java giant.\nIf that isn't necessarily the consensus among Starbucks workers, interviews with nine current and former baristas at the company make clear it's not an isolated opinion, either. Even those who say they like their job paint a picture of a business that underpays front-line workers, enforces work rules arbitrarily, and too often fails to strike a balance between corporate goals and employee needs.\nOf course, such complaints are nothing new in retail, where low pay and erratic schedules are the norm. But by its own account, Starbucks is no ordinary company and is ostensibly a far cry from the fast-food outlets now facing a nationwide uprising by employees tired of working for peanuts.\nThat's evident in the company's recruitment pitch. Starbucks invites job-seekers to \"become a part of something bigger and inspire positive change in the world,\" describing it as a chance to discover a \"deep sense of purpose.\"\nDamage control\nThat image suffered a serious blow last month after The New York Times vividly chronicled a Starbucks worker struggling with the company's scheduling practices. The story, which centered on a 22-year-old barista and single mother, amounted to a public relations nightmare for Starbucks. Perhaps not coincidentally, within days of the story's publication top executives were promising reform.\nIn a memo to employees earlier this month, for instance, Chief Operating Officer Troy Alstead vowed to \"transform the U.S. partner experience,\" referring to Starbucks' more than 130,000 baristas. Inviting worker feedback, he said Starbucks will examine its approach to employee pay, revisit its dress code, make it easier for people to ask for time off, and consider other changes aimed at helping baristas balance work and their personal lives.\nAmong other changes, the company said it would end the practice of \"clopening,\" when an employee responsible for closing a store late at night is also assigned to open it early in the morning.\n\"We recognize that we can do more for our partners who wear the apron every day,\" he wrote.\nSome baristas did not feel this August memo from Starbucks went far enough in proposing ways to improve work conditions, so they marked it up with their own ideas. CBS\nAlthough Starbucks workers welcome this pledge to respect the apron, they fear the company is more intent on dousing the PR flames than on genuinely improving employees' experience. After the retailer last month sent an email to workers outlining possible solutions to the kind of scheduling problems and related issues detailed by the Times, a group of baristas gave the proposal a C- and posted online a marked-up version of the memo listing their own demands (see image above).\n\"We hope you're ready for a commitment to give us schedules that don't mess with taking care of kids, going to school or holding onto that second job we need because Sbux wages don't make ends meet,\" wrote the baristas, who are working with a union-backed labor group, the Center for Popular Democracy.\nRetail jungle\nDespite the recent media focus on Starbucks, the company's labor practices are generally no worse than those of many large retailers. In some ways they're better, with the company offering health care to part-time, as well as full-time, workers; unusually generous 401(k) matching contributions; annual stock grants to employees; and tuition reimbursement.\nStarbucks highlights such benefits as an example of its commitment to employees. \"Sharing success with one another has been core to the company's heritage for more than 40 years,\" Alstead said in the September memo.\nMeanwhile, some baristas say they enjoy their work and feel valued by Starbucks. \"It's a decent place to work, and my manager and co-workers are great,\" said one employee who asked not to be identified.\nBut other current and former workers claim Starbucks has changed in recent years, saying that corporate leaders' intense focus on slashing costs has short-circuited its professed commitment to workers. Mostly, they say Starbucks doesn't listen to employees and even punishes those who identify problems.\n\"The biggest problem is that baristas don't have a voice,\" said Sarah Madden, a former Starbucks barista who left the company this spring after two years with the coffee vendor. \"They can't speak to issues that they know exist. Workers know how to fix them, but when [they] speak up there are serious repercussions -- your hours get cut, you're transferred to another store or isolated from other people.\"\nEmployees interviewed for this article said one result of Starbucks' cost-containment push is that stores are frequently understaffed, hurting customer service and forcing managers to scramble to find staff. That problem is common across the big-box s", - "title": "For some Starbucks workers, job leaves bitter taste", - "description": "Description For some Starbucks workers, job leaves bitter taste", - "url": "https://www.cbsnews.com/news/for-some-starbucks-workers-job-leaves-bitter-taste/", - "editor_notes": "", - "plain_text": "Liberte Locke, a 32-year-old \"barista\" at a Starbucks (SBUX) in New York City, is fed up.\n\"Starbucks' attitude is that there's always someone else who can do the job,\" she said in running through her complaints about life at the java giant.\nIf that isn't necessarily the consensus among Starbucks workers, interviews with nine current and former baristas at the company make clear it's not an isolated opinion, either. Even those who say they like their job paint a picture of a business that underpays front-line workers, enforces work rules arbitrarily, and too often fails to strike a balance between corporate goals and employee needs.\nOf course, such complaints are nothing new in retail, where low pay and erratic schedules are the norm. But by its own account, Starbucks is no ordinary company and is ostensibly a far cry from the fast-food outlets now facing a nationwide uprising by employees tired of working for peanuts.\nThat's evident in the company's recruitment pitch. Starbucks invites job-seekers to \"become a part of something bigger and inspire positive change in the world,\" describing it as a chance to discover a \"deep sense of purpose.\"\nDamage control\nThat image suffered a serious blow last month after The New York Times vividly chronicled a Starbucks worker struggling with the company's scheduling practices. The story, which centered on a 22-year-old barista and single mother, amounted to a public relations nightmare for Starbucks. Perhaps not coincidentally, within days of the story's publication top executives were promising reform.\nIn a memo to employees earlier this month, for instance, Chief Operating Officer Troy Alstead vowed to \"transform the U.S. partner experience,\" referring to Starbucks' more than 130,000 baristas. Inviting worker feedback, he said Starbucks will examine its approach to employee pay, revisit its dress code, make it easier for people to ask for time off, and consider other changes aimed at helping baristas balance work and their personal lives.\nAmong other changes, the company said it would end the practice of \"clopening,\" when an employee responsible for closing a store late at night is also assigned to open it early in the morning.\n\"We recognize that we can do more for our partners who wear the apron every day,\" he wrote.\nSome baristas did not feel this August memo from Starbucks went far enough in proposing ways to improve work conditions, so they marked it up with their own ideas. CBS\nAlthough Starbucks workers welcome this pledge to respect the apron, they fear the company is more intent on dousing the PR flames than on genuinely improving employees' experience. After the retailer last month sent an email to workers outlining possible solutions to the kind of scheduling problems and related issues detailed by the Times, a group of baristas gave the proposal a C- and posted online a marked-up version of the memo listing their own demands (see image above).\n\"We hope you're ready for a commitment to give us schedules that don't mess with taking care of kids, going to school or holding onto that second job we need because Sbux wages don't make ends meet,\" wrote the baristas, who are working with a union-backed labor group, the Center for Popular Democracy.\nRetail jungle\nDespite the recent media focus on Starbucks, the company's labor practices are generally no worse than those of many large retailers. In some ways they're better, with the company offering health care to part-time, as well as full-time, workers; unusually generous 401(k) matching contributions; annual stock grants to employees; and tuition reimbursement.\nStarbucks highlights such benefits as an example of its commitment to employees. \"Sharing success with one another has been core to the company's heritage for more than 40 years,\" Alstead said in the September memo.\nMeanwhile, some baristas say they enjoy their work and feel valued by Starbucks. \"It's a decent place to work, and my manager and co-workers are great,\" said one employee who asked not to be identified.\nBut other current and former workers claim Starbucks has changed in recent years, saying that corporate leaders' intense focus on slashing costs has short-circuited its professed commitment to workers. Mostly, they say Starbucks doesn't listen to employees and even punishes those who identify problems.\n\"The biggest problem is that baristas don't have a voice,\" said Sarah Madden, a former Starbucks barista who left the company this spring after two years with the coffee vendor. \"They can't speak to issues that they know exist. Workers know how to fix them, but when [they] speak up there are serious repercussions -- your hours get cut, you're transferred to another store or isolated from other people.\"\nEmployees interviewed for this article said one result of Starbucks' cost-containment push is that stores are frequently understaffed, hurting customer service and forcing managers to scramble to find staff. That problem is common across the big-box s", - "language": "en", - "embedding": { - "from_text_hash": "", - "vector": [] - }, - "user": { - "userId": "642188372947d07020c1319d" - }, - "is_incident_report": true, - "source_domain": "cbsnews.com", - "cloudinary_id": "reports/cbsnews2.cbsistatic.com/hub/i/r/2014/09/25/aa2708c1-7e84-4573-8f79-add177a690cd/thumbnail/1200x630/2505bd91ab74f8e2ce3e7c6f8a5d77f3/starbucks.jpg", - "epoch_date_submitted": 1559347200, - "epoch_date_published": 1559347200, - "epoch_date_modified": 1559347200, - "epoch_date_downloaded": 1559347200, - "date_submitted": "2019-06-01", - "date_modified": "2019-06-02", - "quiet": false - } - } -} diff --git a/site/gatsby-site/cypress/fixtures/reports/updateOneReport.json b/site/gatsby-site/cypress/fixtures/reports/updateOneReport.json deleted file mode 100644 index f2d3d2328e..0000000000 --- a/site/gatsby-site/cypress/fixtures/reports/updateOneReport.json +++ /dev/null @@ -1,26 +0,0 @@ -{ - "data": { - "updateOneReport": { - "__typename": "Report", - "authors": ["Marco Acevedo"], - "date_downloaded": "2019-04-13", - "date_modified": "2022-08-29", - "date_published": "2015-07-11", - "editor_notes": "", - "epoch_date_downloaded": 1555113600, - "epoch_date_modified": 1661731200, - "epoch_date_published": 1436572800, - "flag": null, - "image_url": "https://assets.change.org/photos/0/yb/id/eYyBIdJOMHpqcty-1600x900-noPad.jpg?1523726975", - "language": "en", - "plain_text": "Videos filled with profanity, sexually explicit material, alcohol, smoking, and drug references - this is what parents are finding on Google’s YouTube Kids app. That’s right - its kids app. Now, parents across the country are calling on Google to remove the app until it can guarantee the total elimination of this inappropriate content.\n\nWhen my neighbors told me about the horrible adult content popping up on the Youtube Kids app, I thought there must be a mistake. Why would Google market an app as “a family-friendly place to explore” and not have proper safeguards in place? Unfortunately, it turned out to be true. And I’ve since learned of the numerous complaints filed to the Federal Trade Commission about this very problem.\n\nEven worse, Google’s response has been laughable. They tell parents to simply flag inappropriate material or set new filters. As a father of two, it makes me angry when a large company like Google doesn’t take responsibility for its kids’ products. Parents are being sold on an app built for kids 5 and under that is supposed to keep them safe from adult content. Parents like myself are joining forces to hold Google accountable.\n\nTell Google to remove the YouTube Kids app until it can live up to its marketing.\n\nThe solution is simple: only allow content pre-approved for ages 5 and under to appear on the app, and don’t allow ads clearly meant for adults. Unless it can live up to expectations, the app should be removed.\n\nParents are not the only ones outraged. The media has blasted Google’s app, calling it “the most anti-family idea ever to come out of Silicon Valley,\" and reporting that it “ignores basic protections for children.”\n\nWith your support, we can get Google to remove YouTube Kids until the proper protections are in place.\n\nThese are examples of videos encountered on YouTube Kids:\n\nA graphic lecture discussing hardcore pornography by Cindy Gallop:\n\nhttps://www.youtube.com/watch?v=EgtcEq7jpAk\n\nHow to make chlorine gas with household products (chemical weapon used in Syria):\n\nhttps://www.youtube.com/watch?v=DF2CXHvh8uI\n\nHow to tie a noose:\n\nhttps://www.youtube.com/watch?v=TpAA2itjI34\n\nHow to throw knives:\n\nhttps://www.youtube.com/watch?v=NGgzn1haQ-E\n\nA guy tasting battery acid:\n\nhttps://www.youtube.com/watch?v=gif-OWNjJSw\n\nHow to use a chainsaw:\n\nhttps://www.youtube.com/watch?v=Kk28thdgCEU\n\nA “Sesame Street” episode dubbed with long strings of expletives:\n\nhttps://www.youtube.com/watch?v=kVkqzE-iiEY\n\nReferences to pedophilia in a homemade video reviewing a “My Little Pony” episode:\n\nhttps://www.youtube.com/watch?v=7K9uH4d-HnU\n\nA DIY video on conducting illegal piracy, featuring pictures of marijuana leaves:\n\nhttps://www.youtube.com/watch?v=dZDF5uqORA0\n", - "report_number": 10, - "submitters": ["Roman Yampolskiy"], - "tags": [], - "text": "Videos filled with profanity, sexually explicit material, alcohol, smoking, and drug references - this is what parents are finding on Google’s YouTube Kids app. That’s right - its kids app. Now, parents across the country are calling on Google to remove the app until it can guarantee the total elimination of this inappropriate content.\n\nWhen my neighbors told me about the horrible adult content popping up on the Youtube Kids app, I thought there must be a mistake. Why would Google market an app as “a family-friendly place to explore” and not have proper safeguards in place? Unfortunately, it turned out to be true. And I’ve since learned of the numerous complaints filed to the Federal Trade Commission about this very problem.\n\nEven worse, Google’s response has been laughable. They tell parents to simply flag inappropriate material or set new filters. As a father of two, it makes me angry when a large company like Google doesn’t take responsibility for its kids’ products. Parents are being sold on an app built for kids 5 and under that is supposed to keep them safe from adult content. Parents like myself are joining forces to hold Google accountable.\n\nTell Google to remove the YouTube Kids app until it can live up to its marketing.\n\nThe solution is simple: only allow content pre-approved for ages 5 and under to appear on the app, and don’t allow ads clearly meant for adults. Unless it can live up to expectations, the app should be removed.\n\nParents are not the only ones outraged. The media has blasted Google’s app, calling it “the most anti-family idea ever to come out of Silicon Valley,\" and reporting that it “ignores basic protections for children.”\n\nWith your support, we can get Google to remove YouTube Kids until the proper protections are in place.\n\nThese are examples of videos encountered on YouTube Kids:\n\nA graphic lecture discussing hardcore pornography by Cindy Gallop:\n\nhttps://www.youtube.com/watch?v=EgtcEq7jpAk\n\nHow to make chlorine gas with household products (chemical weapon used in Syria):\n\nhttps://www.youtube.com/watch?v=DF2CXHvh8uI\n\nHow to tie a noose:\n\nhttps://www.youtube.com/watch?v=TpAA2itjI34\n\nHow to throw knives:\n\nhttps://www.youtube.com/watch?v=NGgzn1haQ-E\n\nA guy tasting battery acid:\n\nhttps://www.youtube.com/watch?v=gif-OWNjJSw\n\nHow to use a chainsaw:\n\nhttps://www.youtube.com/watch?v=Kk28thdgCEU\n\nA “Sesame Street” episode dubbed with long strings of expletives:\n\nhttps://www.youtube.com/watch?v=kVkqzE-iiEY\n\nReferences to pedophilia in a homemade video reviewing a “My Little Pony” episode:\n\nhttps://www.youtube.com/watch?v=7K9uH4d-HnU\n\nA DIY video on conducting illegal piracy, featuring pictures of marijuana leaves:\n\nhttps://www.youtube.com/watch?v=dZDF5uqORA0", - "title": "Remove YouTube Kids app until it eliminates its inappropriate content", - "description": "Description Remove YouTube Kids app until it eliminates its inappropriate content", - "url": "https://www.change.org/p/remove-youtube-kids-app-until-it-eliminates-its-inappropriate-content" - } - } -} diff --git a/site/gatsby-site/cypress/fixtures/reports/updateOneReportTranslation.json b/site/gatsby-site/cypress/fixtures/reports/updateOneReportTranslation.json deleted file mode 100644 index 37906edb16..0000000000 --- a/site/gatsby-site/cypress/fixtures/reports/updateOneReportTranslation.json +++ /dev/null @@ -1,10 +0,0 @@ -{ - "data": { - "updateOneReportTranslation": { - "__typename": "Report", - "report_number": 10, - "text": "Videos filled with profanity, sexually explicit material, alcohol, smoking, and drug references - this is what parents are finding on Google’s YouTube Kids app. That’s right - its kids app. Now, parents across the country are calling on Google to remove the app until it can guarantee the total elimination of this inappropriate content.\n\nWhen my neighbors told me about the horrible adult content popping up on the Youtube Kids app, I thought there must be a mistake. Why would Google market an app as “a family-friendly place to explore” and not have proper safeguards in place? Unfortunately, it turned out to be true. And I’ve since learned of the numerous complaints filed to the Federal Trade Commission about this very problem.\n\nEven worse, Google’s response has been laughable. They tell parents to simply flag inappropriate material or set new filters. As a father of two, it makes me angry when a large company like Google doesn’t take responsibility for its kids’ products. Parents are being sold on an app built for kids 5 and under that is supposed to keep them safe from adult content. Parents like myself are joining forces to hold Google accountable.\n\nTell Google to remove the YouTube Kids app until it can live up to its marketing.\n\nThe solution is simple: only allow content pre-approved for ages 5 and under to appear on the app, and don’t allow ads clearly meant for adults. Unless it can live up to expectations, the app should be removed.\n\nParents are not the only ones outraged. The media has blasted Google’s app, calling it “the most anti-family idea ever to come out of Silicon Valley,\" and reporting that it “ignores basic protections for children.”\n\nWith your support, we can get Google to remove YouTube Kids until the proper protections are in place.\n\nThese are examples of videos encountered on YouTube Kids:\n\nA graphic lecture discussing hardcore pornography by Cindy Gallop:\n\nhttps://www.youtube.com/watch?v=EgtcEq7jpAk\n\nHow to make chlorine gas with household products (chemical weapon used in Syria):\n\nhttps://www.youtube.com/watch?v=DF2CXHvh8uI\n\nHow to tie a noose:\n\nhttps://www.youtube.com/watch?v=TpAA2itjI34\n\nHow to throw knives:\n\nhttps://www.youtube.com/watch?v=NGgzn1haQ-E\n\nA guy tasting battery acid:\n\nhttps://www.youtube.com/watch?v=gif-OWNjJSw\n\nHow to use a chainsaw:\n\nhttps://www.youtube.com/watch?v=Kk28thdgCEU\n\nA “Sesame Street” episode dubbed with long strings of expletives:\n\nhttps://www.youtube.com/watch?v=kVkqzE-iiEY\n\nReferences to pedophilia in a homemade video reviewing a “My Little Pony” episode:\n\nhttps://www.youtube.com/watch?v=7K9uH4d-HnU\n\nA DIY video on conducting illegal piracy, featuring pictures of marijuana leaves:\n\nhttps://www.youtube.com/watch?v=dZDF5uqORA0", - "title": "Remove YouTube Kids app until it eliminates its inappropriate content" - } - } -} \ No newline at end of file diff --git a/site/gatsby-site/cypress/fixtures/submissions/quickadds.json b/site/gatsby-site/cypress/fixtures/submissions/quickadds.json deleted file mode 100644 index 04dc9b0652..0000000000 --- a/site/gatsby-site/cypress/fixtures/submissions/quickadds.json +++ /dev/null @@ -1,475 +0,0 @@ -{ - "data": { - "quickadds": [ - { - "__typename": "Quickadd", - "_id": "5f8cce8c5e48da1847ef9ce4", - "date_submitted": "2020-10-18", - "source_domain": "www.brusselstimes.com", - "url": "https://www.brusselstimes.com/all-news/belgium-all-news/politics/106320/xr-belgium-posts-deepfake-of-belgian-premier-linking-covid-19-with-climate-crisis/" - }, - { - "__typename": "Quickadd", - "_id": "5fc6e045a476d2d8102ce5a2", - "date_submitted": "2020-12-02", - "source_domain": "arstechnica.com", - "url": "https://arstechnica.com/information-technology/2020/12/amazon-to-roll-out-tools-to-monitor-factory-workers-and-machines/" - 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Ut enim ad minim veniam", - "url": "https://skylightcyber.com/2019/07/18/cylance-i-kill-you/", - "editor_notes": "", - "nlp_similar_incidents": [], - "editor_dissimilar_incidents": [], - "editor_similar_incidents": [], - "tags": [], - "developers": [], - "deployers": [], - "harmed_parties": [], - "incident_editors": [] - - } - ] - } -} \ No newline at end of file diff --git a/site/gatsby-site/cypress/fixtures/subscriptions/empty-subscriptions.json b/site/gatsby-site/cypress/fixtures/subscriptions/empty-subscriptions.json deleted file mode 100644 index 027e2860dc..0000000000 --- a/site/gatsby-site/cypress/fixtures/subscriptions/empty-subscriptions.json +++ /dev/null @@ -1,6 +0,0 @@ -{ - "data": { - "subscriptions": [ - ] - } -} diff --git a/site/gatsby-site/cypress/fixtures/subscriptions/subscriptions-to-deleted-incidents.json b/site/gatsby-site/cypress/fixtures/subscriptions/subscriptions-to-deleted-incidents.json deleted file mode 100644 index e9616d4d30..0000000000 --- a/site/gatsby-site/cypress/fixtures/subscriptions/subscriptions-to-deleted-incidents.json +++ /dev/null @@ -1,63 +0,0 @@ -{ - "data": { - "subscriptions": [ - { - "_id": "6335e4ce290efe73c71b2e50", - "incident_id": null, - "entityId": null, - "type": "new-incidents" - }, - { - "_id": "6335e4ce290efe73c71b2e55", - "incident_id": { - "incident_id": 11, - "title": "Northpointe Risk Models" - }, - "entityId": null, - "type": "incident" - }, - { - "_id": "6335fcd0290efe73c71b2e60", - "incident_id": { - "incident_id": 10, - "title": "Kronos’s Algorithm Allegedly Created Scheduling and Financial Issues for Starbucks Employees" - }, - "entityId": null, - "type": "incident" - }, - { - "_id": "63361ade290efe73c71b2e61", - "incident_id": { - "incident_id": 12, - "title": "Common Biases of Vector Embeddings" - }, - "entityId": null, - "type": "incident" - }, - { - "_id": "63361ade290efe73c71b2e61", - "incident_id": null, - "entityId": null, - "type": "incident" - }, - { - "_id": "63361ade290efe73c71b2e70", - "incident_id": null, - "entityId": { - "entity_id": "google", - "name": "Google" - }, - "type": "entity" - }, - { - "_id": "63361ade290efe73c71b2e71", - "incident_id": null, - "entityId": { - "entity_id": "uk-home-office", - "name": "UK Home Office" - }, - "type": "entity" - } - ] - } -} diff --git a/site/gatsby-site/cypress/fixtures/subscriptions/subscriptions.json b/site/gatsby-site/cypress/fixtures/subscriptions/subscriptions.json deleted file mode 100644 index 63bcd9bd69..0000000000 --- a/site/gatsby-site/cypress/fixtures/subscriptions/subscriptions.json +++ /dev/null @@ -1,57 +0,0 @@ -{ - "data": { - "subscriptions": [ - { - "_id": "6335e4ce290efe73c71b2e50", - "incident_id": null, - "entityId": null, - "type": "new-incidents" - }, - { - "_id": "6335e4ce290efe73c71b2e55", - "incident_id": { - "incident_id": 11, - "title": "Northpointe Risk Models" - }, - "entityId": null, - "type": "incident" - }, - { - "_id": "6335fcd0290efe73c71b2e60", - "incident_id": { - "incident_id": 10, - "title": "Kronos’s Algorithm Allegedly Created Scheduling and Financial Issues for Starbucks Employees" - }, - "entityId": null, - "type": "incident" - }, - { - "_id": "63361ade290efe73c71b2e61", - "incident_id": { - "incident_id": 12, - "title": "Common Biases of Vector Embeddings" - }, - "entityId": null, - "type": "incident" - }, - { - "_id": "63361ade290efe73c71b2e70", - "incident_id": null, - "entityId": { - "entity_id": "google", - "name": "Google" - }, - "type": "entity" - }, - { - "_id": "63361ade290efe73c71b2e71", - "incident_id": null, - "entityId": { - "entity_id": "uk-home-office", - "name": "UK Home Office" - }, - "type": "entity" - } - ] - } -} diff --git a/site/gatsby-site/cypress/fixtures/taxa/CSETv1_Annotator-3.json b/site/gatsby-site/cypress/fixtures/taxa/CSETv1_Annotator-3.json deleted file mode 100644 index a1fe5feeb0..0000000000 --- a/site/gatsby-site/cypress/fixtures/taxa/CSETv1_Annotator-3.json +++ /dev/null @@ -1,1574 +0,0 @@ -{ - "_id": { - "$oid": "64c27aef6bd9217f29557f17" - }, - "namespace": "CSETv1_Annotator-3", - "weight": { - "$numberInt": "70" - }, - "complete_entities": true, - "description": "# What is the CSET Taxonomy?\n\nThe CSET AI Harm Taxonomy for AIID is the second edition of the \nCSET incident taxonomy. It characterizes the harms, entities and \ntechnologies involved in AI incidents and the circumstances of \ntheir occurrence. Every incident is independently classified by \ntwo CSET annotators. Annotations are peer reviewed and finally \nrandomly selected for quality control ahead of publication. \nDespite this rigorous process, mistakes do happen, and readers \nare invited to report any errors they might discover while \nbrowsing.", - "dummy_fields": [ - { - "field_number": "1", - "short_name": "Metadata" - }, - { - "field_number": "2", - "short_name": "Incident Domain" - }, - { - "field_number": "3", - "short_name": "AI Tangible Harm Assessment" - }, - { - "field_number": "4", - "short_name": "Special Interest Intangible Harm" - }, - { - "field_number": "5", - "short_name": "AI Special Interest Intangible Harm Assessment" - }, - { - "field_number": "6", - "short_name": "Environmental and Temporal Characteristics" - }, - { - "field_number": "7", - "short_name": "Characterizing Entities and the Harm" - }, - { - "field_number": "8", - "short_name": "Tangible Harm Quantities " - }, - { - "field_number": "9", - "short_name": "Information about AI System" - }, - { - "field_number": "10", - "short_name": "AI Functionality and Techniques" - } - ], - "field_list": [ - { - "field_number": "1.1", - "short_name": "Incident Number", - "long_name": "The number of the incident in the AI Incident Database.", - "short_description": "The number of the incident in the AI Incident Database.", - "long_description": "The number of the incident in the AI Incident Database.", - "display_type": "int", - "mongo_type": "int", - "default": "", - "weight": { - "$numberInt": "5" - }, - "instant_facet": false, - "required": false, - "public": true, - "hide_search": true - }, - { - "field_number": "1.2", - "short_name": "Annotator", - "long_name": "Person responsible for the annotations", - "short_description": "This is the researcher that is responsible for applying the classifications of the CSET taxonomy.", - "long_description": "An ID designating the individual who classified this incident according to the CSET taxonomy.", - "display_type": "string", - "mongo_type": "string", - "default": "", - "placeholder": "Select name here", - "permitted_values": [], - "weight": { - "$numberInt": "5" - }, - "instant_facet": false, - "required": false, - "public": false, - "hide_search": true - }, - { - "field_number": "1.3", - "short_name": "Annotation Status", - "long_name": "Where in the annotation process is this incident?", - "short_description": "What is the quality assurance status of the CSET classifications for this incident?", - "long_description": "What is the quality assurance status of the CSET classifications for this incident?", - "display_type": "enum", - "mongo_type": "string", - "default": "", - "placeholder": "Select process status here", - "permitted_values": [ - "1. Annotation in progress", - "2. Initial annotation complete", - "3. In peer review", - "4. Peer review complete", - "5. In quality control", - "6. Complete and final" - ], - "weight": { - "$numberInt": "10" - }, - "instant_facet": false, - "required": false, - "public": false, - "hide_search": true, - "notes": "When you start a row, switch this field from blank to “Annotation in progress” so other annotators know not to work on the same row (and can be sure it wasn’t skipped or left unfinished accidentally).\n\nOnce a row is marked “Initial annotation complete,” we will assume that any remaining blanks were left deliberately - that is, you looked, but couldn’t find enough information to fill out the blank fields. For this reason, please don’t mark a row “Initial annotation complete” until you’ve truly finished filling it out.\n\nWhen peer review begins, the assigned reviewer should switch the status to “In peer review.” When the review is complete and all comments have been resolved, either the peer reviewer or the original annotator should switch the status to “Peer review complete.”\n\nOptions 5 and 6 should only ever be selected by the project lead." - }, - { - "field_number": "1.4", - "short_name": "Peer Reviewer", - "long_name": "Person responsible for reviewing annotations", - "short_description": "This is the researcher that is responsible for ensuring the quality of the classifications applied to this incident.", - "long_description": "The CSET taxonomy assigns individual researchers to each incident as the primary parties responsible for classifying the incident according to the taxonomy. This is the person responsible for assuring the integrity of annotator's classifications.", - "display_type": "string", - "mongo_type": "string", - "default": "", - "placeholder": "Select name here", - "permitted_values": [], - "weight": { - "$numberInt": "20" - }, - "instant_facet": false, - "required": false, - "public": false, - "hide_search": true, - "notes": "The project lead will assign a peer reviewer to each incident." - }, - { - "field_number": "1.5", - "short_name": "Quality Control", - "long_name": "Was this incident randomly selected for additional quality control?", - "short_description": "Has someone flagged a potential issue with this incident's classifications? Annotators should leave this field blank.", - "long_description": "The peer review process sometimes uncovers issues with the classifications that have been applied by the annotator. This field serves as a flag when there is a need for additional thought and input on the classifications applied", - "display_type": "bool", - "mongo_type": "bool", - "default": "false", - "placeholder": "", - "permitted_values": [], - "weight": { - "$numberInt": "15" - }, - "instant_facet": false, - "required": false, - "public": false, - "hide_search": true - }, - { - "field_number": "2.1", - "short_name": "Physical Objects", - "long_name": "Did the incident occur in a domain with physical objects ?", - "short_description": "Did the incident occur in a domain with physical objects ?", - "long_description": "“Yes” if the AI system(s) is embedded in hardware that can interact with, affect, and change the physical objects (cars, robots, medical facilities, etc.). Mark “No” if the system cannot. This includes systems that inform, detect, predict, or recommend.", - "display_type": "enum", - "mongo_type": "string", - "default": "maybe", - "permitted_values": [ - "yes", - "no", - "maybe" - ], - "placeholder": "", - "weight": { - "$numberInt": "40" - }, - "instant_facet": false, - "required": false, - "notes": "Context matters. AI systems embedded in hardware that can physically interact are more likely to cause death, injury, or damage." - }, - { - "field_number": "2.2", - "short_name": "Entertainment Industry", - "long_name": "Did the AI incident occur in the entertainment industry?", - "short_description": "Did the AI incident occur in the entertainment industry?", - "long_description": "“Yes” if the sector in which the AI was used is associated with entertainment. “No” if it was used in a different, clearly identifiable sector. “Maybe” if the sector of use could not be determined.", - "display_type": "enum", - "mongo_type": "string", - "default": "maybe", - "permitted_values": [ - "yes", - "no", - "maybe" - ], - "placeholder": "", - "weight": { - "$numberInt": "40" - }, - "instant_facet": false, - "required": false, - "notes": "Context matters. AI systems used for entertainment are less likely to result in harm. For example a deepfake used in a movie is less likely to cause harm than a deepfake used for political misinformation." - }, - { - "field_number": "2.3", - "short_name": "Report, Test, or Study of data", - "long_name": "Was the incident about a report, test, or study of data instead of the AI itself?", - "short_description": "Was the incident about a report, test, or study of data instead of the AI itself?", - "long_description": "“Yes” if the incident is about a report, test, or study of the data and does not discuss an instance of injury, damage, or loss. “Maybe” if it is unclear. Otherwise mark “No.”", - "display_type": "enum", - "mongo_type": "string", - "default": "maybe", - "permitted_values": [ - "yes", - "no", - "maybe" - ], - "placeholder": "", - "weight": { - "$numberInt": "40" - }, - "instant_facet": false, - "required": false, - "notes": "Sometimes there are reports about issues with the data that could be used to develop AI systems. Since there are mitigation approaches, data issues do not automatically mean that the associated AI will have issues that lead to harm. A projection or hypothesis of the harm resulting from data issues is not sufficient. There must be harm that can be clearly linked to an AI." - }, - { - "field_number": "2.4", - "short_name": "Deployed", - "long_name": "Was the reported system (even if AI involvement is unknown) deployed or sold to users?", - "short_description": "Was the reported system (even if AI involvement is unknown) deployed or sold to users?", - "long_description": "“Yes” if the involved system was deployed or sold to users. “No” if it was not. “Maybe” if there is not enough information or if the use is unclear.", - "display_type": "enum", - "mongo_type": "string", - "default": "maybe", - "permitted_values": [ - "yes", - "no", - "maybe" - ], - "placeholder": "", - "weight": { - "$numberInt": "40" - }, - "instant_facet": false, - "required": false, - "notes": "Systems that are not deployed or sold to users tend to still be in the development stage and hence are less likely to cause harm. However, harm can still be possible. " - }, - { - "field_number": "2.5", - "short_name": "Producer Test in Controlled Conditions", - "long_name": "Was this a test or demonstration of an AI system done by developers, producers or researchers (versus users) in controlled conditions?", - "short_description": "Was this a test or demonstration of an AI system done by developers, producers or researchers (versus users) in controlled conditions?", - "long_description": "“Yes” if it was a test/demonstration performed by developers, producers or journalists in controlled conditions. “No” if it was not a test/demonstration. “No” if the test/demonstration was done by a user. “No” if the test/demonstration was in operational or uncontrolled conditions. “Maybe” otherwise.", - "display_type": "enum", - "mongo_type": "string", - "default": "maybe", - "permitted_values": [ - "yes", - "no", - "maybe" - ], - "placeholder": "", - "weight": { - "$numberInt": "40" - }, - "instant_facet": false, - "required": false, - "notes": "AI system tests or demonstrations by developers, producers, or researchers in controlled environments are less likely to expose people, organizations, property, institutions, or the natural environment to harm. Controlled environments may include situations such as an isolated compute system, a regulatory sandbox, or an autonomous vehicle testing range. " - }, - { - "field_number": "2.6", - "short_name": "Producer Test in Operational Conditions", - "long_name": "Was this a test or demonstration of an AI system done by developers, producers or researchers (versus users) in operational conditions?", - "short_description": "Was this a test or demonstration of an AI system done by developers, producers or researchers (versus users) in operational conditions?", - "long_description": "“Yes” if it was a test/demonstration performed by developers, producers or journalists in controlled conditions. “No” if it was not a test/demonstration. “No” if the test/demonstration was done by a user. “No” if the test/demonstration was in controlled or non-operational conditions. “Maybe” otherwise.", - "display_type": "enum", - "mongo_type": "string", - "default": "maybe", - "permitted_values": [ - "yes", - "no", - "maybe" - ], - "placeholder": "", - "weight": { - "$numberInt": "40" - }, - "instant_facet": false, - "required": false, - "notes": "While almost every AI system undergoes testing or demonstration in a controlled environment, some also undergo testing or demonstration in an operational environment. Testing in operational environments still occurs before the system is deployed or sold to end-users. However, relative to controlled environments, operational environments try to closely represent real-world conditions and end-users that affect use of the AI system. Therefore, testing in an operational environment typically poses a heightened risk of harm to people, organizations, property, institutions, or the environment." - }, - { - "field_number": "2.7", - "short_name": "User Test in Controlled Conditions", - "long_name": "Was this a test or demonstration done by users in controlled conditions?", - "short_description": "Was this a test or demonstration done by users in controlled conditions?", - "long_description": "“Yes” if it was a test/demonstration performed by users in controlled conditions. “No” if it was not a test/demonstration. “No” if the test/demonstration was done by developers, producers or researchers. “No” if the test/demonstration was in controlled or non-controlled conditions.“Maybe” otherwise.", - "display_type": "enum", - "mongo_type": "string", - "default": "maybe", - "permitted_values": [ - "yes", - "no", - "maybe" - ], - "placeholder": "", - "weight": { - "$numberInt": "40" - }, - "instant_facet": false, - "required": false, - "notes": "The involvement of a user (versus a developer, producer, or researcher) increases the likelihood that harm can occur even if the AI system is being tested. Relative to controlled environments, controlled environments try to closely represent real-world conditions and end-users that affect use of the AI system. Therefore, testing in an controlled environment typically poses a heightened risk of harm to people, organizations, property, institutions, or the environment." - }, - { - "field_number": "2.8", - "short_name": "User Test in Operational Conditions", - "long_name": "Was this a test or demonstration done by users in operational conditions?", - "short_description": "Was this a test or demonstration done by users in operational conditions?", - "long_description": "“Yes” if it was a test/demonstration performed by users in operational conditions. “No” if it was not a test/demonstration. “No” if the test/demonstration was done by developers, producers or researchers. “No” if the test/demonstration was in controlled or non-operational conditions.“Maybe” otherwise.", - "display_type": "enum", - "mongo_type": "string", - "default": "maybe", - "permitted_values": [ - "yes", - "no", - "maybe" - ], - "placeholder": "", - "weight": { - "$numberInt": "40" - }, - "instant_facet": false, - "required": false, - "notes": "Sometimes, prior to deployment, the user will perform a test or demonstration of the AI system. The involvement of a user (versus a developer, producer, or researcher) increases the likelihood that harm can occur even if the AI system is being tested in controlled environments." - }, - { - "field_number": "2.9", - "short_name": "Harm Domain", - "long_name": "Incident occurred in a domain where we could expect harm to occur?", - "short_description": "Incident occurred in a domain where we could likely expect harm to occur?", - "long_description": "Using the answers to the 8 domain questions, assess if the incident occurred in a domain where harm could be expected to occur. If you are unclear, input “maybe.”", - "display_type": "enum", - "mongo_type": "string", - "default": "maybe", - "permitted_values": [ - "yes", - "no", - "maybe" - ], - "placeholder": "", - "weight": { - "$numberInt": "40" - }, - "instant_facet": false, - "required": false, - "hide_search": true, - "notes": "Reflecting upon the previously answered questions, decide if the reported incident or instance occurred in a domain in which harm could possibly occur. This is not a decision on whether or not harm did occur. Just a reflection on the operating conditions or context of the system." - }, - { - "field_number": "3.1", - "short_name": "Tangible Harm", - "long_name": "Did tangible harm (loss, damage or injury ) occur? ", - "short_description": "Did tangible harm (loss, damage or injury ) occur? ", - "long_description": "An assessment of whether tangible harm, imminent tangible harm, or non-imminent tangible harm occurred. This assessment does not consider the context of the tangible harm, if an AI was involved, or if there is an identifiable, specific, and harmed entity. It is also not assessing if an intangible harm occurred. It is only asking if tangible harm occurred and what its imminency was.", - "display_type": "enum", - "mongo_type": "string", - "default": "unclear", - "placeholder": "", - "permitted_values": [ - "tangible harm definitively occurred", - "imminent risk of tangible harm (near miss) did occur", - "non-imminent risk of tangible harm (an issue) occurred", - "no tangible harm, near-miss, or issue", - "unclear" - ], - "weight": { - "$numberInt": "40" - }, - "instant_facet": false, - "required": false, - "public": true - }, - { - "field_number": "3.2", - "short_name": "AI System", - "long_name": "Does the incident involve an AI system?", - "short_description": "Does the incident involve an AI system?", - "long_description": "An assessment of whether or not an AI system was involved. It is sometimes difficult to judge between an AI and an automated system or expert rules system. In these cases select “maybe”", - "display_type": "enum", - "mongo_type": "string", - "default": "maybe", - "permitted_values": [ - "yes", - "no", - "maybe" - ], - "placeholder": "", - "weight": { - "$numberInt": "40" - }, - "instant_facet": false, - "required": false, - "public": true, - "notes": "Note, over time more information about the incident may become available, allowing a ‘maybe’ to be changed to a ‘yes’ or ‘no.’" - }, - { - "field_number": "3.3", - "short_name": "Clear link to technology", - "long_name": "Can the technology be directly and clearly linked to the adverse outcome of the incident", - "short_description": "Can the technology be directly and clearly linked to the adverse outcome of the incident", - "long_description": "An assessment of the technology's involvement in the chain of harm. \"Yes\" indicates that the technology was involved in harm, its behavior can be directly linked to the harm, and the harm may not have occurred if the technology acted differently. \"No\", indicates that the technology's behavior cannot be linked to the harm outcome. \"Maybe\" indicates that the link is unclear.", - "hide_search": true, - "display_type": "enum", - "mongo_type": "string", - "default": "maybe", - "permitted_values": [ - "yes", - "no", - "maybe" - ], - "placeholder": "", - "weight": { - "$numberInt": "40" - }, - "instant_facet": false, - "required": false, - "public": true - }, - { - "field_number": "3.4", - "short_name": "There is a potentially identifiable specific entity that experienced the harm", - "long_name": "There is a potentially identifiable specific entity that experienced the harm", - "short_description": "A potentially identifiable specific entity that experienced the harm can be characterized or identified.", - "long_description": "“Yes” if it is theoretically possible to both specify and identify the entity. Having that information is not required. The information just needs to exist and be potentially discoverable. “No” if there are not any potentially identifiable specific entities or if the harmed entities are a class or subgroup that can only be characterized. ", - "display_type": "bool", - "mongo_type": "bool", - "permitted_values": [], - "placeholder": "", - "default": "", - "weight": { - "$numberInt": "40" - }, - "instant_facet": false, - "required": false, - "public": true, - "hide_search": true, - "notes": "A potentially identifiable specific entity is an entity that can be described in detail such that the name (Mr. Joe Smith, Acme Inc, etc.) or a unique identifier (e.g. 100 Main Street, Anywhere USA) of the entity could be found. We may not know the name or identifier of the entity from the reports, but it does exist and could be found. For example, the general public is not a potentially identifiable specific entity. However, incarcerated people in the Springfield penitentiary would be specific entities because it would be possible to get a list of all the prisoners in the facility." - }, - { - "field_number": "3.5", - "short_name": "AI Harm Level", - "long_name": "Annotator's AI tangible harm level assessment", - "short_description": "An assessment of the AI tangible harm level, which takes into account the CSET definitions of AI tangible harm levels, along with the inputs for annotation fields about the AI, harm, chain of harm, and entity. ", - "long_description": "An assessment of the AI tangible harm level, which takes into account the CSET definitions of AI tangible harm levels, along with the inputs for annotation fields about the AI, harm, chain of harm, and entity.", - "display_type": "enum", - "mongo_type": "string", - "default": "unclear", - "placeholder": "", - "permitted_values": [ - "AI tangible harm event", - "AI tangible harm near-miss", - "AI tangible harm issue", - "none", - "unclear" - ], - "weight": { - "$numberInt": "40" - }, - "instant_facet": false, - "required": false, - "public": true, - "notes": "Special interest intangible harm is determined in a different field. The determination of a special interest intangible harm is not dependant upon the AI tangible harm level" - }, - { - "field_number": "3.6", - "short_name": "AI Tangible Harm Level Notes", - "long_name": "AI Tangible Harm Level Notes", - "short_description": "Notes about the AI tangible harm level assessment", - "long_description": "If for 3.5 you select unclear or leave it blank, please provide a brief description of why.\n\n You can also add notes if you want to provide justification for a level", - "display_type": "long_string", - "mongo_type": "string", - "default": "", - "placeholder": "Notes about the AI tangible harm level assessment", - "permitted_values": [], - "weight": { - "$numberInt": "5" - }, - "instant_facet": false, - "required": false, - "public": true, - "hide_search": true - }, - { - "field_number": "4.1", - "short_name": "Impact on Critical Services", - "long_name": "Did this impact people's access to critical or public services (health care, social services, voting, transportation, etc)?", - "short_description": "Indicates if people’s access to critical public services was impacted.", - "long_description": "Did this impact people's access to critical or public services (health care, social services, voting, transportation, etc)?", - "display_type": "enum", - "mongo_type": "string", - "default": "maybe", - "permitted_values": [ - "yes", - "no", - "maybe" - ], - "placeholder": "", - "weight": { - "$numberInt": "40" - }, - "instant_facet": false, - "required": false, - "public": true, - "notes": "Public services include healthcare, social services, voting, public transportation, education, and consumer protection.\n\nNote, if ‘yes’ is selected then there was likely a violation of civil liberties and there was a special interest intangible harm." - }, - { - "field_number": "4.2", - "short_name": "Rights Violation", - "long_name": "Was this a violation of human rights, civil liberties, civil rights, or democratic norms?", - "short_description": "Indicate if a violation of human rights, civil rights, civil liberties, or democratic norms occurred.", - "long_description": "Indicate if a violation of human rights, civil rights, civil liberties, or democratic norms occurred.", - "display_type": "enum", - "mongo_type": "string", - "default": "maybe", - "permitted_values": [ - "yes", - "no", - "maybe" - ], - "placeholder": "", - "weight": { - "$numberInt": "40" - }, - "instant_facet": false, - "required": false, - "public": true, - "notes": "It can often be difficult for the typical annotator to differentiate between violations of civil liberties, civil rights, human rights, and democratic norms. For this reason CSET grouped them together.\n\nHuman rights are rights inherent to all human beings, regardless of race, sex, nationality, ethnicity, language, religion, or any other status. They include the right to life and liberty, freedom from slavery and torture, freedom of opinion and expression, and the right to work and education. Civil rights are legal provisions that originate from notions of equality and can be enforced by law. Civil liberties are personal freedoms that are referenced in the Bill of Rights. Democratic norms are traditions, customs, and best practices that support democracy. An example of a democratic norm is accepting election results and facilitating a peaceful transfer of political power. " - }, - { - "field_number": "4.3", - "short_name": "Involving Minor", - "long_name": "Was a minor involved in the incident (disproportionally treated or specifically targeted/affected)", - "short_description": "Was a minor involved in the incident (disproportionally treated or specifically targeted/affected)", - "long_description": "Indicate if a minor was disproportionately targeted or affected", - "display_type": "enum", - "mongo_type": "string", - "default": "maybe", - "permitted_values": [ - "yes", - "no", - "maybe" - ], - "placeholder": "", - "weight": { - "$numberInt": "40" - }, - "instant_facet": false, - "required": false, - "public": true, - "notes": "Generally, governments have an interest in establishing heightened protections for minors. These protections are often associated with media content or privacy. For example, if an AI system illegally tracked a minor’s activity online, then answer “yes” to this question. There are instances where an AI system causes indiscriminate harm to a group of people, and it is plausible that some of those people are minors. However, in this case the entire group of people, adults and children alike, shared the distribution of harm equally and therefore the answer to this question would be “no.”" - }, - { - "field_number": "4.4", - "short_name": "Detrimental Content", - "long_name": "Was detrimental content (misinformation, hate speech) involved?", - "short_description": "Was detrimental content (misinformation, hate speech) involved?", - "long_description": "Detrimental content can include deepfakes, identity misrepresentation, insults, threats of violence, eating disorder or self harm promotion, extremist content, misinformation, sexual abuse material, and scam emails. Detrimental content in itself is often not harmful, however, it can lead to or instigate injury, damage, or loss.", - "display_type": "enum", - "mongo_type": "string", - "default": "maybe", - "permitted_values": [ - "yes", - "no", - "maybe" - ], - "placeholder": "", - "weight": { - "$numberInt": "40" - }, - "instant_facet": false, - "required": false, - "public": true - }, - { - "field_number": "4.5", - "short_name": "Protected Characteristic", - "long_name": "Was a group of people or an individual treated differently based upon a protected characteristic?", - "short_description": "Was a group of people treated differently based upon a protected characteristic (e.g. race, ethnicity, creed, immigrant status, color, religion, sex, national origin, age, disability, genetic information)?", - "long_description": "Protected characteristics include religion, commercial facilities, geography, age, sex, sexual orientation or gender identity, familial status (e.g., having or not having children) or pregnancy, disability, veteran status, genetic information, financial means, race or creed, Ideology, nation of origin, citizenship, and immigrant status.\n\nAt the federal level in the US, age is a protected characteristic for people over the age of 40. Minors are not considered a protected class. For this reason the CSET annotation taxonomy has a separate field to note if a minor was involved.\n\nOnly mark yes if there is clear evidence discrimination occurred. If there are conflicting accounts, mark unsure. Do not mark that discrimination occurred based on expectation alone.", - "display_type": "enum", - "mongo_type": "string", - "default": "maybe", - "permitted_values": [ - "yes", - "no", - "maybe" - ], - "placeholder": "", - "weight": { - "$numberInt": "40" - }, - "instant_facet": false, - "required": false, - "public": true - }, - { - "field_number": "4.6", - "short_name": "Harm Distribution Basis", - "long_name": "If harms were potentially unevenly distributed among people, on what basis?", - "short_description": "Indicates how the harms were potentially distributed.", - "long_description": "Multiple can occur.\n\nGenetic information refers to information about a person’s genetic tests or the genetic tests of their relatives. Genetic information can predict the manifestation of a disease or disorder.", - "display_type": "multi", - "mongo_type": "string", - "default": "unclear", - "placeholder": "", - "permitted_values": [ - "none", - "age", - "disability", - "familial status (e.g., having or not having children) or pregnancy", - "financial means", - "genetic information", - "geography", - "ideology", - "nation of origin, citizenship, immigrant status", - "race", - "religion", - "sex", - "sexual orientation or gender identity", - "veteran status", - "unclear", - "other" - ], - "weight": { - "$numberInt": "60" - }, - "instant_facet": false, - "required": false, - "public": true - }, - { - "field_number": "4.7", - "short_name": "Notes (special interest intangible harm)", - "long_name": "Input any notes that may help explain your answers.", - "short_description": "Input any notes that may help explain your answers.", - "long_description": "Input any notes that may help explain your answers.", - "display_type": "long_string", - "mongo_type": "string", - "default": "", - "placeholder": "Notes", - "permitted_values": [], - "weight": { - "$numberInt": "5" - }, - "instant_facet": false, - "required": false, - "public": true, - "hide_search": true - }, - { - "field_number": "5.1", - "short_name": "Special Interest Intangible Harm", - "long_name": "Was there a special interest intangible harm or risk of harm?", - "short_description": "An assessment of whether a special interest intangible harm occurred. This assessment does not consider the context of the intangible harm, if an AI was involved, or if there is characterizable class or subgroup of harmed entities. It is also not assessing if an intangible harm occurred. It is only asking if a special interest intangible harm occurred.", - "long_description": "An assessment of whether a special interest intangible harm occurred. This assessment does not consider the context of the intangible harm, if an AI was involved, or if there is characterizable class or subgroup of harmed entities. It is also not assessing if an intangible harm occurred. It is only asking if a special interest intangible harm occurred.", - "display_type": "enum", - "mongo_type": "string", - "default": "maybe", - "permitted_values": [ - "yes", - "no", - "maybe" - ], - "placeholder": "", - "weight": { - "$numberInt": "5" - }, - "instant_facet": false, - "required": false, - "public": true - }, - { - "field_number": "5.2", - "short_name": "AI System", - "long_name": "Does the incident involve an AI system?", - "short_description": "Does the incident involve an AI system?", - "long_description": "An assessment of whether or not an AI system was involved. It is sometimes difficult to judge between an AI and an automated system or expert rules system. In these cases select “maybe”", - "display_type": "enum", - "mongo_type": "string", - "default": "maybe", - "permitted_values": [ - "yes", - "no", - "maybe" - ], - "placeholder": "", - "weight": { - "$numberInt": "40" - }, - "instant_facet": false, - "required": false, - "public": true, - "hide_search": true, - "notes": "Note, over time more information about the incident may become available, allowing a ‘maybe’ to be changed to a ‘yes’ or ‘no.’" - }, - { - "field_number": "5.3", - "short_name": "Clear link to Technology", - "long_name": "Can the technology be directly and clearly linked to the adverse outcome of the incident?", - "short_description": "Can the technology be directly and clearly linked to the adverse outcome of the incident?", - "long_description": "An assessment of the technology's involvement in the chain of harm. \"Yes\" indicates that the technology was involved in harm, its behavior can be directly linked to the harm, and the harm may not have occurred if the technology acted differently. \"No\", indicates that the technology's behavior cannot be linked to the harm outcome. \"Maybe\" indicates that the link is unclear.", - "display_type": "enum", - "mongo_type": "string", - "default": "maybe", - "permitted_values": [ - "yes", - "no", - "maybe" - ], - "placeholder": "", - "weight": { - "$numberInt": "40" - }, - "instant_facet": false, - "required": false, - "public": true - }, - { - "field_number": "5.4", - "short_name": "Harmed Class of Entities", - "long_name": "There is a characterizable class or subgroup of entities that experienced the harm", - "short_description": "“Yes” if the harmed entity or entities can be characterized. “No” if there are not any characterizable entities.", - "long_description": "A characterizable class or subgroup are descriptions of different populations of people. Often they are characteristics by which people qualify for special protection by a law, policy, or similar authority.\n\n Sometimes, groups may be characterized by their exposure to the incident via geographical proximity (e.g., ‘visitors to the park’) or participation in an activity (e.g.,‘Twitter users’).", - "display_type": "bool", - "mongo_type": "bool", - "permitted_values": [], - "placeholder": "", - "weight": { - "$numberInt": "40" - }, - "instant_facet": false, - "required": false, - "public": true, - "hide_search": true, - "notes": "" - }, - { - "field_number": "5.5", - "short_name": "Annotator’s AI special interest intangible harm assessment", - "long_name": "The annotator’s assessment of if an AI special interest intangible harm occurred.", - "short_description": "The annotator’s assessment of if an AI special interest intangible harm occurred.", - "long_description": "AI tangible harm is determined in a different field. The determination of a special interest intangible harm is not dependant upon the AI tangible harm level.", - "display_type": "enum", - "mongo_type": "string", - "default": "maybe", - "permitted_values": [ - "yes", - "no", - "maybe" - ], - "placeholder": "", - "weight": { - "$numberInt": "40" - }, - "instant_facet": false, - "required": false, - "public": true, - "notes": "" - }, - { - "field_number": "5.6", - "short_name": "Notes (AI special interest intangible harm)", - "long_name": "If for 5.5 you select unclear or leave it blank, please provide a brief description of why.\n\nYou can also add notes if you want to provide justification for a level.", - "short_description": "If for 5.5 you select unclear or leave it blank, please provide a brief description of why.\n\nYou can also add notes if you want to provide justification for a level.", - "long_description": "If for 5.5 you select unclear or leave it blank, please provide a brief description of why.\n\nYou can also add notes if you want to provide justification for a level.", - "display_type": "long_string", - "mongo_type": "string", - "default": "", - "placeholder": "Notes", - "permitted_values": [], - "weight": { - "$numberInt": "5" - }, - "instant_facet": false, - "required": false, - "public": true, - "hide_search": true - }, - { - "field_number": "6.1", - "short_name": "Date of Incident Year", - "long_name": "The year in which the incident first occurred.", - "short_description": "The year in which the incident occurred. If there are multiple harms or occurrences of the incident, list the earliest. If a precise date is unavailable, but the available sources provide a basis for estimating the year, estimate. Otherwise, leave blank.\n\nEnter in the format of YYYY", - "long_description": "The year in which the incident occurred. If there are multiple harms or occurrences of the incident, list the earliest. If a precise date is unavailable, but the available sources provide a basis for estimating the year, estimate. Otherwise, leave blank.", - "display_type": "string", - "mongo_type": "string", - "default": "", - "placeholder": "YYYY", - "permitted_values": [], - "weight": { - "$numberInt": "5" - }, - "instant_facet": false, - "required": false, - "public": true - }, - { - "field_number": "6.2", - "short_name": "Date of Incident Month", - "long_name": "The month in which the incident first occurred.", - "short_description": "The month in which the incident occurred. If there are multiple harms or occurrences of the incident, list the earliest. If a precise date is unavailable, but the available sources provide a basis for estimating the month, estimate. Otherwise, leave blank.\n\nEnter in the format of MM", - "long_description": "The month in which the incident occurred. If there are multiple harms or occurrences of the incident, list the earliest. If a precise date is unavailable, but the available sources provide a basis for estimating the month, estimate. Otherwise, leave blank.", - "display_type": "string", - "mongo_type": "string", - "default": "", - "placeholder": "MM", - "permitted_values": [], - "weight": { - "$numberInt": "5" - }, - "instant_facet": false, - "required": false, - "public": true, - "hide_search": true - }, - { - "field_number": "6.3", - "short_name": "Date of Incident Day", - "long_name": "The day on which the first incident occurred.", - "short_description": "The day on which the incident occurred. If a precise date is unavailable, leave blank.\n\nEnter in the format of DD", - "long_description": "The day on which the incident occurred. If a precise date is unavailable, leave blank.\n\nEnter in the format of DD", - "display_type": "string", - "mongo_type": "string", - "default": "", - "placeholder": "DD", - "permitted_values": [], - "weight": { - "$numberInt": "5" - }, - "instant_facet": false, - "required": false, - "public": true, - "hide_search": true - }, - { - "field_number": "6.4", - "short_name": "Estimated Date", - "long_name": "Is the date estimated?", - "short_description": "“Yes” if the data was estimated. “No” otherwise.", - "long_description": "“Yes” if the data was estimated. “No” otherwise.", - "display_type": "bool", - "mongo_type": "bool", - "permitted_values": [], - "placeholder": "", - "weight": { - "$numberInt": "5" - }, - "instant_facet": false, - "required": false, - "public": true, - "hide_search": true - }, - { - "field_number": "6.5", - "short_name": "Multiple AI Interaction", - "long_name": "Was the AI interacting with another AI?", - "short_description": "“Yes” if two or more independently operating AI systems were involved. “No” otherwise.", - "long_description": "This happens very rarely but is possible. Examples include two chatbots having a conversation with each other, or two autonomous vehicles in a crash.", - "display_type": "enum", - "mongo_type": "string", - "default": "maybe", - "permitted_values": [ - "yes", - "no", - "maybe" - ], - "placeholder": "", - "weight": { - "$numberInt": "5" - }, - "instant_facet": false, - "required": false, - "public": true - }, - { - "field_number": "6.6", - "short_name": "Embedded", - "long_name": "Is the AI embedded in a physical system or have a physical presence?", - "short_description": "“Yes” if the AI is embedded in a physical system. “No” if it is not. “Maybe” if it is unclear.", - "long_description": "This question is slightly different from the one in field 2.1.1. That question asks about there being interaction with physical objects–an ability to manipulate or change. A system can be embedded in a physical object and able to interact with the physical environment, e.g. a vacuum robot. A system can be embedded in a physical object and not interact with a physical environment, e.g. a camera system that only records images when the AI detects that dogs are present. AI systems that are accessed through API, web-browser, etc by using a mobile device or computer are not considered to be embedded in hardware systems. They are accessed through hardware.", - "display_type": "enum", - "mongo_type": "string", - "default": "maybe", - "permitted_values": [ - "yes", - "no", - "maybe" - ], - "placeholder": "", - "weight": { - "$numberInt": "5" - }, - "instant_facet": false, - "required": false, - "public": true - }, - { - "field_number": "6.7", - "short_name": "Location City", - "long_name": "If the incident occurred at a specific known location, note the city.", - "short_description": "If the incident occurred at a specific known location, note the city.", - "long_description": "If the incident occurred at a specific known location, note the city. If there are multiple relevant locations, enter multiple city/state/country values.", - "display_type": "string", - "mongo_type": "string", - "default": "", - "placeholder": "City", - "permitted_values": [], - "weight": { - "$numberInt": "5" - }, - "instant_facet": false, - "required": false, - "public": true, - "hide_search": true - }, - { - "field_number": "6.8", - "short_name": "Location State/Province (two letters)", - "long_name": "If the incident occurred at a specific known location, note the state/province.", - "short_description": "If the incident occurred at a specific known location, note the state/province.", - "long_description": "If the incident occurred at a specific known location, note the state/province. If there are multiple relevant locations, enter multiple city/state/country values.", - "display_type": "string", - "mongo_type": "string", - "default": "", - "placeholder": "State or Province", - "permitted_values": [], - "weight": { - "$numberInt": "5" - }, - "instant_facet": false, - "required": false, - "public": true, - "hide_search": true - }, - { - "field_number": "6.9", - "short_name": "Location Country (two letters)", - "long_name": "If the incident occurred at a specific known location, note the country. ", - "short_description": "If the incident occurred at a specific known location, note the country. Follow ISO 3166 for the 2-letter country codes.", - "long_description": "Follow ISO 3166 for the 2-letter country codes.\n\nIf there are multiple relevant locations, enter multiple city/state/country values.", - "display_type": "string", - "mongo_type": "string", - "default": "", - "placeholder": "Country", - "permitted_values": [], - "weight": { - "$numberInt": "5" - }, - "instant_facet": false, - "required": false, - "public": true - }, - { - "field_number": "6.10", - "short_name": "Location Region", - "long_name": "Location Region", - "short_description": "Select the region of the world where the incident occurred. If it occurred in multiple, leave blank.", - "long_description": "Use this reference to map countries to regions: https://www.dhs.gov/geographic-regions", - "display_type": "enum", - "mongo_type": "string", - "default": "unclear", - "placeholder": "", - "permitted_values": [ - "Global", - "Africa", - "Asia", - "Caribbean", - "Central America", - "Europe", - "North America", - "Oceania", - "South America", - "unclear" - ], - "weight": { - "$numberInt": "5" - }, - "instant_facet": false, - "required": false, - "public": true - }, - { - "field_number": "6.11", - "short_name": "Infrastructure Sectors", - "long_name": "Which critical infrastructure sectors were affected, if any?", - "short_description": "Which critical infrastructure sectors were affected, if any?", - "long_description": "Which critical infrastructure sectors were affected, if any?", - "display_type": "multi", - "mongo_type": "string", - "default": "unclear", - "placeholder": "", - "permitted_values": [ - "chemical", - "commercial facilities", - "communications", - "critical manufacturing", - "dams", - "defense-industrial base", - "emergency services", - "energy", - "financial services", - "food and agriculture", - "government facilities", - "healthcare and public health", - "information technology", - "nuclear ", - "transportation", - "water and wastewater", - "Other", - "unclear" - ], - "weight": { - "$numberInt": "5" - }, - "instant_facet": false, - "required": false, - "public": true - }, - { - "field_number": "6.12", - "short_name": "Operating Conditions", - "long_name": "A record of any abnormal or atypical operational conditions that occurred.", - "short_description": "A record of any abnormal or atypical operational conditions that occurred.", - "long_description": "A record of any abnormal or atypical operational conditions that occurred. This field is most often blank.", - "display_type": "list", - "mongo_type": "array", - "default": "", - "placeholder": "e.g. raining; night; low visibility", - "permitted_values": [], - "weight": { - "$numberInt": "5" - }, - "instant_facet": false, - "required": false, - "public": true - }, - { - "field_number": "6.13", - "short_name": "Notes (Environmental and Temporal Characteristics)", - "long_name": "Notes (Environmental and Temporal Characteristics)", - "short_description": "Input any notes that may help explain your answers.", - "long_description": "Input any notes that may help explain your answers.", - "display_type": "long_string", - "mongo_type": "string", - "default": "", - "placeholder": "Notes", - "permitted_values": [], - "weight": { - "$numberInt": "5" - }, - "instant_facet": false, - "required": false, - "public": true, - "hide_search": true - }, - { - "field_number": "7", - "short_name": "Entities", - "long_name": "Characterizing Entities and the Harm", - "short_description": "Characterizing Entities and the Harm", - "long_description": "Characterizing Entities and the Harm", - "display_type": "object-list", - "mongo_type": "array", - "hide_search": true, - "subfields": [ - { - "field_number": "7.1", - "short_name": "Entity", - "long_name": "A short 1 to 2 word description of the entity.", - "short_description": "A short 1 to 2 word description of the entity. When possible use a proper name for the entity, making it a Named Entity.", - "long_description": "A short 1 to 2 word description of the entity. When possible use a proper name for the entity, making it a Named Entity.\n\nAnnotate information for each entity involved in the report. Try to capture every entity directly linked to the harm. Think about the entity that experienced the harm, all of the entities between them and the AI, and then all of the entities involved in producing and deploying the AI.\n\nEmployees representing a company in a media or public relations capacity should not be included as an entity.", - "display_type": "string", - "mongo_type": "string", - "complete_from": { - "entities": true - }, - "default": "", - "placeholder": "", - "permitted_values": [], - "weight": { - "$numberInt": "5" - }, - "instant_facet": false, - "required": false, - "public": true - }, - { - "field_number": "7.2", - "short_name": "Named Entity", - "long_name": "Named Entity Indicator", - "short_description": "Indicates if the entity is a Named Entity.", - "long_description": "Indicates if the entity is a Named Entity. “Yes” if the entity is a named entity. “No” otherwise.", - "display_type": "bool", - "mongo_type": "bool", - "permitted_values": [], - "placeholder": "", - "weight": { - "$numberInt": "5" - }, - "instant_facet": false, - "required": false, - "public": true - }, - { - "field_number": "7.3", - "short_name": "Entity type", - "long_name": "Indicates the type of entity", - "short_description": "Indicates the type of entity", - "long_description": "Indicates the type of entity. If multiple selections could characterize the entity, select the primary function of the entity.", - "display_type": "enum", - "mongo_type": "string", - "default": "unclear", - "placeholder": "Entity Type", - "permitted_values": [ - "individual", - "group of individuals", - "for-profit organization", - "non-profit organization", - "government entity", - "privately owned space", - "public space", - "infrastructure", - "social or political system", - "product", - "other", - "unclear" - ], - "weight": { - "$numberInt": "5" - }, - "instant_facet": false, - "required": false, - "public": true, - "hide_search": true - }, - { - "field_number": "7.4", - "short_name": "Entity Relationship to the AI", - "long_name": "Entity Relationship to the AI", - "short_description": "Indicates the entity’s relationship to the AI.", - "long_description": "Indicates the entity’s relationship to the AI. Note, the smallest possible chain of harm has just two elements; an AI and an entity experiencing harm, near-miss, or issue.", - "display_type": "list", - "mongo_type": "array", - "default": "", - "placeholder": "", - "permitted_values": [ - "developer", - "deployer", - "government oversight", - "user", - "AI", - "geographic area of use", - "researcher", - "product containing AI", - "watchdog" - ], - "weight": { - "$numberInt": "5" - }, - "instant_facet": false, - "required": false, - "public": true - }, - { - "field_number": "7.5", - "short_name": "Harm Category Experienced", - "long_name": "Was an AI special interest intangible harm, tangible harm event, near-miss, or issue experienced by this entity", - "short_description": "Was an AI special interest intangible harm, tangible harm event, near-miss, or issue experienced by this entity", - "long_description": "Was an AI special interest intangible harm, tangible harm event, near-miss, or issue experienced by this entity. For each recorded entity, indicate the harm category that they experienced. Because recorded entities have a variety of roles in the AI incident, not every recorded entity will experience harm.", - "display_type": "enum", - "mongo_type": "string", - "default": "unclear", - "placeholder": "", - "permitted_values": [ - "AI special interest intangible harm", - "AI tangible harm event", - "AI tangible harm near-miss", - "AI tangible harm issue", - "Other harm not meeting CSET definitions", - "not applicable", - "unclear" - ], - "weight": { - "$numberInt": "5" - }, - "instant_facet": false, - "required": false, - "public": true - }, - { - "field_number": "7.6", - "short_name": "Harm Type Experienced", - "long_name": "Type of harm experienced by entity ", - "short_description": "Indicates the type of harm experienced by the harmed entity", - "long_description": "Indicates the type of harm experienced by the harmed entity. Only entities experiencing harm should have an assigned type. If the entity did not experience the harm, ‘not applicable’ should be selected.", - "display_type": "enum", - "mongo_type": "string", - "default": "not applicable", - "placeholder": "", - "permitted_values": [ - "physical health/safety", - "financial loss", - "physical property", - "intangible property", - "infrastructure", - "natural environment", - "social or political systems", - "violation of human rights, civil liberties, civil rights, or democratic norms", - "detrimental content", - "disproportionate treatment based upon a protected characteristic", - "other tangible harm", - "other intangible harm", - "not applicable" - ], - "weight": { - "$numberInt": "5" - }, - "instant_facet": false, - "required": false, - "public": true - }, - { - "field_number": "7.7", - "short_name": "Notes (Characterizing Entities and the Harm)", - "long_name": "Notes (Characterizing Entities and the Harm)", - "short_description": "Input any notes that may help explain your answers.", - "long_description": "Input any notes that may help explain your answers.", - "display_type": "long_string", - "mongo_type": "string", - "default": "", - "placeholder": "Notes", - "permitted_values": [], - "weight": { - "$numberInt": "5" - }, - "instant_facet": false, - "required": false, - "public": true, - "hide_search": true - } - ], - "default": "[]", - "placeholder": "", - "permitted_values": [], - "weight": { - "$numberInt": "5" - }, - "instant_facet": false, - "required": false, - "public": true - }, - { - "field_number": "8.1", - "short_name": "Lives Lost", - "long_name": "How many human lives were lost?", - "short_description": "Indicates the number of deaths reported", - "long_description": "This field cannot be greater than zero if the harm is anything besides ‘Physical health/safety.’ ", - "display_type": "int", - "mongo_type": "int", - "default": "", - "placeholder": "", - "permitted_values": [], - "weight": { - "$numberInt": "5" - }, - "instant_facet": false, - "required": false, - "public": true - }, - { - "field_number": "8.2", - "short_name": "Injuries", - "long_name": "How many humans were injured?", - "short_description": "Indicate the number of injuries reported.", - "long_description": "This field cannot be greater than zero if the harm is anything besides 'Physical health/safety'.\n\nAll reported injuries should count, regardless of their severity level. If a person lost their limb and another person scraped their elbow, both cases would be considered injuries. Do not include the number of deaths in this count.", - "display_type": "int", - "mongo_type": "int", - "default": "", - "placeholder": "", - "permitted_values": [], - "weight": { - "$numberInt": "5" - }, - "instant_facet": false, - "required": false, - "public": true - }, - { - "field_number": "8.3", - "short_name": "Estimated Harm Quantities", - "long_name": "Are any quantities estimated?", - "short_description": "Indicates if the amount was estimated.", - "long_description": "Indicates if the amount was estimated.", - "display_type": "bool", - "mongo_type": "bool", - "permitted_values": [], - "placeholder": "", - "weight": { - "$numberInt": "5" - }, - "instant_facet": false, - "required": false, - "hide_search": true, - "public": true - }, - { - "field_number": "8.4", - "short_name": "Notes ( Tangible Harm Quantities Information)", - "long_name": "Notes ( Tangible Harm Quantities Information)", - "short_description": "Input any notes that may help explain your answers.", - "long_description": "Input any notes that may help explain your answers.", - "display_type": "long_string", - "mongo_type": "string", - "default": "", - "placeholder": "Notes", - "permitted_values": [], - "weight": { - "$numberInt": "5" - }, - "instant_facet": false, - "required": false, - "public": true, - "hide_search": true - }, - { - "field_number": "9.1", - "short_name": "AI System Description", - "long_name": "Description of the AI system involved", - "short_description": "A description of the AI system (when possible)", - "long_description": "Describe the AI system in as much detail as the reports will allow.\n\nA high level description of the AI system is sufficient, but if more technical details about the AI system are available, include them in the description as well.", - "display_type": "long_string", - "mongo_type": "string", - "default": "", - "placeholder": "Description of the AI system involved", - "permitted_values": [], - "weight": { - "$numberInt": "5" - }, - "instant_facet": false, - "required": false, - "public": true, - "hide_search": true - }, - { - "field_number": "9.2", - "short_name": "Data Inputs", - "long_name": "Description of data inputs to the AI system", - "short_description": "A list of the types of data inputs for the AI system.", - "long_description": "This is a freeform field that can have any value. There could be multiple entries for this field.\n\nCommon ones include\n\n- still images\n- video\n- text\n- speech\n- Personally Identifiable Information\n- structured data\n- other\n- unclear\n\nStill images are static images. Video images consist of moving images. Text and speech data are considered an important category of unstructured data. They consist of written and spoken words that are not in a tabular format. Personally identifiable information is data that can uniquely identify an individual and may contain sensitive information. Structured data is often in a tabular, machine readable format and can typically be used by an AI system without much preprocessing.\n\nAvoid using ‘unstructured data’ data in this field. Instead specify the type of unstructured data; text, images, audio files, etc. It is ok to use ‘structured data’ in this field.\n\nRecord what the media report explicitly states. If the report does not explicitly state an input modality but it is likely that a particular kind of input contributed to the harm or near harm, record that input. If you are still unsure, do not record anything.", - "display_type": "list", - "mongo_type": "array", - "default": "", - "placeholder": "", - "permitted_values": [], - "weight": { - "$numberInt": "5" - }, - "instant_facet": false, - "required": false, - "public": true - }, - { - "field_number": "9.3", - "short_name": "Sector of Deployment", - "long_name": "Indicates the sector in which the AI system is deployed", - "short_description": "Indicate the sector in which the AI system is deployed", - "long_description": "Indicate the sector in which the AI system is deployed\n\nThere could be multiple entries for this field.", - "display_type": "multi", - "mongo_type": "array", - "default": "", - "placeholder": "", - "permitted_values": [ - "agriculture, forestry and fishing", - "mining and quarrying", - "manufacturing", - "electricity, gas, steam and air conditioning supply", - "water supply", - "construction", - "wholesale and retail trade", - "transportation and storage", - "accommodation and food service activities", - "information and communication", - "financial and insurance activities", - "real estate activities", - "professional, scientific and technical activities", - "administrative and support service activities", - "public administration", - "defense", - "law enforcement", - "Education", - "human health and social work activities", - "Arts, entertainment and recreation", - "other service activities", - "activities of households as employers", - "activities of extraterritorial organizations and bodies", - "other", - "unclear" - ], - "weight": { - "$numberInt": "60" - }, - "instant_facet": false, - "required": false, - "public": true - }, - { - "field_number": "9.4", - "short_name": "Public Sector Deployment", - "long_name": "Indicates whether the AI system is deployed in the public sector", - "short_description": "Indicate whether the AI system is deployed in the public sector", - "long_description": "Indicate whether the AI system is deployed in the public sector. The public sector is the part of the economy that is controlled and operated by the government.", - "display_type": "enum", - "mongo_type": "string", - "default": "maybe", - "permitted_values": [ - "yes", - "no", - "maybe" - ], - "placeholder": "", - "weight": { - "$numberInt": "40" - }, - "instant_facet": false, - "required": false, - "public": true - }, - { - "field_number": "9.5", - "short_name": "Autonomy Level", - "long_name": "Autonomy Level", - "short_description": "Autonomy1: The system operates independently without simultaneous human oversight, interaction, or intervention.\n\nAutonomy2: The system operates independently but with human oversight, where a human can observe and override the system’s decisions in real time.\n\nAutonomy3: The system does not independently make decisions but instead provides information to a human who actively chooses to proceed with the AI’s information.", - "long_description": "Autonomy1: The system operates independently without simultaneous human oversight, interaction, or intervention.\n\nAutonomy2: The system operates independently but with human oversight, where a human can observe and override the system’s decisions in real time.\n\nAutonomy3: The system does not independently make decisions but instead provides information to a human who actively chooses to proceed with the AI’s information.", - "display_type": "enum", - "mongo_type": "string", - "default": "", - "placeholder": "", - "permitted_values": [ - "Autonomy1", - "Autonomy2", - "Autonomy3", - "unclear" - ], - "weight": { - "$numberInt": "40" - }, - "instant_facet": false, - "required": false, - "public": true - }, - { - "field_number": "9.8", - "short_name": "Notes (Information about AI System)", - "long_name": "Notes (Information about AI System)", - "short_description": "Input any notes that may help explain your answers.", - "long_description": "Input any notes that may help explain your answers.", - "display_type": "long_string", - "mongo_type": "string", - "default": "", - "placeholder": "Notes", - "permitted_values": [], - "weight": { - "$numberInt": "5" - }, - "instant_facet": false, - "required": false, - "public": true, - "hide_search": true - }, - { - "field_number": "10.1", - "short_name": "Intentional Harm", - "long_name": "Was the AI intentionally developed or deployed to perform the harm?", - "short_description": "Was the AI intentionally developed or deployed to perform the harm?\n\nIf yes, did the AI’s behavior result in unintended or intended harm? ", - "long_description": "Indicates if the system was designed to do harm. If it was designed to perform harm, the field will indicate if the AI system did or did not create unintended harm–i.e. was the reported harm the harm that AI was expected to perform or a different unexpected harm? ", - "display_type": "enum", - "mongo_type": "string", - "default": "unclear", - "placeholder": "", - "permitted_values": [ - "Yes. Intentionally designed to perform harm and did create intended harm", - "Yes. Intentionally designed to perform harm but created an unintended harm (a different harm may have occurred)", - "No. Not intentionally designed to perform harm", - "unclear" - ], - "weight": { - "$numberInt": "40" - }, - "instant_facet": false, - "required": false, - "public": true, - "notes": "Tracking and analyzing harm from AI systems designed to do harm is valuable and worthwhile. However, analysts may want to separately analyze harm from AI systems that were or were not designed to produce the observed harm." - }, - { - "field_number": "10.2", - "short_name": "Physical System Type", - "long_name": "Into what type of physical system was the AI integrated, if any?", - "short_description": "Describe the type of physical system that the AI was integrated into.", - "long_description": "Describe the type of physical system that the AI was integrated into. ", - "display_type": "string", - "mongo_type": "string", - "default": "", - "placeholder": "Physical System Type (e.g. trash sorting robot)", - "permitted_values": [], - "weight": { - "$numberInt": "5" - }, - "instant_facet": false, - "required": false, - "public": true - }, - { - "field_number": "10.3", - "short_name": "AI Task", - "long_name": "AI task or core application area", - "short_description": "Describe the AI’s application.", - "long_description": "Describe the AI’s application.\n\nIt is likely that the annotator will not have enough information to complete this field. If this occurs, enter unclear.\n\nThis is a freeform field. Some possible entries are\n\n- unclear\n- human language technologies\n- computer vision\n- robotics\n- automation and/or optimization\n- other\n\nThe application area of an AI is the high level task that the AI is intended to perform. It does not describe the technical methods by which the AI performs the task. Considering what an AI’s technical methods enable it to do is another way of arriving at what an AI’s application is. \n\nIt is possible for multiple application areas to be involved. When possible pick the principle or domain area, but it is ok to select multiple areas.", - "display_type": "list", - "mongo_type": "array", - "default": "", - "placeholder": "", - "permitted_values": [], - "weight": { - "$numberInt": "5" - }, - "instant_facet": false, - "required": false, - "public": true - }, - { - "field_number": "10.4", - "short_name": "AI tools and methods", - "long_name": "AI tools and methods", - "short_description": "Describe the tools and methods that enable the AI’s application.", - "long_description": "Describe the tools and methods that enable the AI’s application.\n\nIt is likely that the annotator will not have enough information to complete this field. If this occurs, enter unclear\n\nThis is a freeform field. Some possible entries are\n\n- unclear\n- reinforcement learning\n- neural networks\n- decision trees\n- bias mitigation\n- optimization\n- classifier\n- NLP/text analytics\n- continuous learning\n- unsupervised learning\n- supervised learning\n- clustering\n- prediction\n- rules\n- random forest\n\nAI tools and methods are the technical building blocks that enable the AI’s application.", - "display_type": "list", - "mongo_type": "array", - "default": "unclear", - "placeholder": "", - "permitted_values": [], - "weight": { - "$numberInt": "40" - }, - "instant_facet": false, - "required": false, - "public": true - }, - { - "field_number": "10.5", - "short_name": "Notes (AI Functionality and Techniques)", - "long_name": "Notes (AI Functionality and Techniques)", - "short_description": "Input any notes that may help explain your answers.", - "long_description": "Input any notes that may help explain your answers.", - "display_type": "long_string", - "mongo_type": "string", - "default": "", - "placeholder": "Notes", - "permitted_values": [], - "weight": { - "$numberInt": "5" - }, - "instant_facet": false, - "required": false, - "public": true, - "hide_search": true - } - ] -} \ No newline at end of file diff --git a/site/gatsby-site/cypress/fixtures/taxa/csetV1.json b/site/gatsby-site/cypress/fixtures/taxa/csetV1.json deleted file mode 100644 index 2356d07d65..0000000000 --- a/site/gatsby-site/cypress/fixtures/taxa/csetV1.json +++ /dev/null @@ -1,1574 +0,0 @@ -{ - "_id": { - "$oid": "64c27aef6bd9217f29557f14" - }, - "namespace": "CSETv1", - "weight": { - "$numberInt": "70" - }, - "complete_entities": true, - "description": "# What is the CSET Taxonomy?\n\nThe CSET AI Harm Taxonomy for AIID is the second edition of the \nCSET incident taxonomy. It characterizes the harms, entities and \ntechnologies involved in AI incidents and the circumstances of \ntheir occurrence. Every incident is independently classified by \ntwo CSET annotators. Annotations are peer reviewed and finally \nrandomly selected for quality control ahead of publication. \nDespite this rigorous process, mistakes do happen, and readers \nare invited to report any errors they might discover while \nbrowsing. The first version of the CSET taxonomy is available [here](/taxonomy/csetv0/).", - "dummy_fields": [ - { - "field_number": "1", - "short_name": "Metadata" - }, - { - "field_number": "2", - "short_name": "Incident Domain" - }, - { - "field_number": "3", - "short_name": "AI Tangible Harm Assessment" - }, - { - "field_number": "4", - "short_name": "Special Interest Intangible Harm" - }, - { - "field_number": "5", - "short_name": "AI Special Interest Intangible Harm Assessment" - }, - { - "field_number": "6", - "short_name": "Environmental and Temporal Characteristics" - }, - { - "field_number": "7", - "short_name": "Characterizing Entities and the Harm" - }, - { - "field_number": "8", - "short_name": "Tangible Harm Quantities " - }, - { - "field_number": "9", - "short_name": "Information about AI System" - }, - { - "field_number": "10", - "short_name": "AI Functionality and Techniques" - } - ], - "field_list": [ - { - "field_number": "1.1", - "short_name": "Incident Number", - "long_name": "The number of the incident in the AI Incident Database.", - "short_description": "The number of the incident in the AI Incident Database.", - "long_description": "The number of the incident in the AI Incident Database.", - "display_type": "int", - "mongo_type": "int", - "default": "", - "weight": { - "$numberInt": "5" - }, - "instant_facet": false, - "required": false, - "public": true, - "hide_search": true - }, - { - "field_number": "1.2", - "short_name": "Annotator", - "long_name": "Person responsible for the annotations", - "short_description": "This is the researcher that is responsible for applying the classifications of the CSET taxonomy.", - "long_description": "An ID designating the individual who classified this incident according to the CSET taxonomy.", - "display_type": "string", - "mongo_type": "string", - "default": "", - "placeholder": "Select name here", - "permitted_values": [], - "weight": { - "$numberInt": "5" - }, - "instant_facet": false, - "required": false, - "public": false, - "hide_search": true - }, - { - "field_number": "1.3", - "short_name": "Annotation Status", - "long_name": "Where in the annotation process is this incident?", - "short_description": "What is the quality assurance status of the CSET classifications for this incident?", - "long_description": "What is the quality assurance status of the CSET classifications for this incident?", - "display_type": "enum", - "mongo_type": "string", - "default": "", - "placeholder": "Select process status here", - "permitted_values": [ - "1. Annotation in progress", - "2. Initial annotation complete", - "3. In peer review", - "4. Peer review complete", - "5. In quality control", - "6. Complete and final" - ], - "weight": { - "$numberInt": "10" - }, - "instant_facet": false, - "required": false, - "public": false, - "hide_search": true, - "notes": "When you start a row, switch this field from blank to “Annotation in progress” so other annotators know not to work on the same row (and can be sure it wasn’t skipped or left unfinished accidentally).\n\nOnce a row is marked “Initial annotation complete,” we will assume that any remaining blanks were left deliberately - that is, you looked, but couldn’t find enough information to fill out the blank fields. For this reason, please don’t mark a row “Initial annotation complete” until you’ve truly finished filling it out.\n\nWhen peer review begins, the assigned reviewer should switch the status to “In peer review.” When the review is complete and all comments have been resolved, either the peer reviewer or the original annotator should switch the status to “Peer review complete.”\n\nOptions 5 and 6 should only ever be selected by the project lead." - }, - { - "field_number": "1.4", - "short_name": "Peer Reviewer", - "long_name": "Person responsible for reviewing annotations", - "short_description": "This is the researcher that is responsible for ensuring the quality of the classifications applied to this incident.", - "long_description": "The CSET taxonomy assigns individual researchers to each incident as the primary parties responsible for classifying the incident according to the taxonomy. This is the person responsible for assuring the integrity of annotator's classifications.", - "display_type": "string", - "mongo_type": "string", - "default": "", - "placeholder": "Select name here", - "permitted_values": [], - "weight": { - "$numberInt": "20" - }, - "instant_facet": false, - "required": false, - "public": false, - "hide_search": true, - "notes": "The project lead will assign a peer reviewer to each incident." - }, - { - "field_number": "1.5", - "short_name": "Quality Control", - "long_name": "Was this incident randomly selected for additional quality control?", - "short_description": "Has someone flagged a potential issue with this incident's classifications? Annotators should leave this field blank.", - "long_description": "The peer review process sometimes uncovers issues with the classifications that have been applied by the annotator. This field serves as a flag when there is a need for additional thought and input on the classifications applied", - "display_type": "bool", - "mongo_type": "bool", - "default": "false", - "placeholder": "", - "permitted_values": [], - "weight": { - "$numberInt": "15" - }, - "instant_facet": false, - "required": false, - "public": false, - "hide_search": true - }, - { - "field_number": "2.1", - "short_name": "Physical Objects", - "long_name": "Did the incident occur in a domain with physical objects ?", - "short_description": "Did the incident occur in a domain with physical objects ?", - "long_description": "“Yes” if the AI system(s) is embedded in hardware that can interact with, affect, and change the physical objects (cars, robots, medical facilities, etc.). Mark “No” if the system cannot. This includes systems that inform, detect, predict, or recommend.", - "display_type": "enum", - "mongo_type": "string", - "default": "maybe", - "permitted_values": [ - "yes", - "no", - "maybe" - ], - "placeholder": "", - "weight": { - "$numberInt": "40" - }, - "instant_facet": false, - "required": false, - "notes": "Context matters. AI systems embedded in hardware that can physically interact are more likely to cause death, injury, or damage." - }, - { - "field_number": "2.2", - "short_name": "Entertainment Industry", - "long_name": "Did the AI incident occur in the entertainment industry?", - "short_description": "Did the AI incident occur in the entertainment industry?", - "long_description": "“Yes” if the sector in which the AI was used is associated with entertainment. “No” if it was used in a different, clearly identifiable sector. “Maybe” if the sector of use could not be determined.", - "display_type": "enum", - "mongo_type": "string", - "default": "maybe", - "permitted_values": [ - "yes", - "no", - "maybe" - ], - "placeholder": "", - "weight": { - "$numberInt": "40" - }, - "instant_facet": false, - "required": false, - "notes": "Context matters. AI systems used for entertainment are less likely to result in harm. For example a deepfake used in a movie is less likely to cause harm than a deepfake used for political misinformation." - }, - { - "field_number": "2.3", - "short_name": "Report, Test, or Study of data", - "long_name": "Was the incident about a report, test, or study of data instead of the AI itself?", - "short_description": "Was the incident about a report, test, or study of data instead of the AI itself?", - "long_description": "“Yes” if the incident is about a report, test, or study of the data and does not discuss an instance of injury, damage, or loss. “Maybe” if it is unclear. Otherwise mark “No.”", - "display_type": "enum", - "mongo_type": "string", - "default": "maybe", - "permitted_values": [ - "yes", - "no", - "maybe" - ], - "placeholder": "", - "weight": { - "$numberInt": "40" - }, - "instant_facet": false, - "required": false, - "notes": "Sometimes there are reports about issues with the data that could be used to develop AI systems. Since there are mitigation approaches, data issues do not automatically mean that the associated AI will have issues that lead to harm. A projection or hypothesis of the harm resulting from data issues is not sufficient. There must be harm that can be clearly linked to an AI." - }, - { - "field_number": "2.4", - "short_name": "Deployed", - "long_name": "Was the reported system (even if AI involvement is unknown) deployed or sold to users?", - "short_description": "Was the reported system (even if AI involvement is unknown) deployed or sold to users?", - "long_description": "“Yes” if the involved system was deployed or sold to users. “No” if it was not. “Maybe” if there is not enough information or if the use is unclear.", - "display_type": "enum", - "mongo_type": "string", - "default": "maybe", - "permitted_values": [ - "yes", - "no", - "maybe" - ], - "placeholder": "", - "weight": { - "$numberInt": "40" - }, - "instant_facet": false, - "required": false, - "notes": "Systems that are not deployed or sold to users tend to still be in the development stage and hence are less likely to cause harm. However, harm can still be possible. " - }, - { - "field_number": "2.5", - "short_name": "Producer Test in Controlled Conditions", - "long_name": "Was this a test or demonstration of an AI system done by developers, producers or researchers (versus users) in controlled conditions?", - "short_description": "Was this a test or demonstration of an AI system done by developers, producers or researchers (versus users) in controlled conditions?", - "long_description": "“Yes” if it was a test/demonstration performed by developers, producers or journalists in controlled conditions. “No” if it was not a test/demonstration. “No” if the test/demonstration was done by a user. “No” if the test/demonstration was in operational or uncontrolled conditions. “Maybe” otherwise.", - "display_type": "enum", - "mongo_type": "string", - "default": "maybe", - "permitted_values": [ - "yes", - "no", - "maybe" - ], - "placeholder": "", - "weight": { - "$numberInt": "40" - }, - "instant_facet": false, - "required": false, - "notes": "AI system tests or demonstrations by developers, producers, or researchers in controlled environments are less likely to expose people, organizations, property, institutions, or the natural environment to harm. Controlled environments may include situations such as an isolated compute system, a regulatory sandbox, or an autonomous vehicle testing range. " - }, - { - "field_number": "2.6", - "short_name": "Producer Test in Operational Conditions", - "long_name": "Was this a test or demonstration of an AI system done by developers, producers or researchers (versus users) in operational conditions?", - "short_description": "Was this a test or demonstration of an AI system done by developers, producers or researchers (versus users) in operational conditions?", - "long_description": "“Yes” if it was a test/demonstration performed by developers, producers or journalists in controlled conditions. “No” if it was not a test/demonstration. “No” if the test/demonstration was done by a user. “No” if the test/demonstration was in controlled or non-operational conditions. “Maybe” otherwise.", - "display_type": "enum", - "mongo_type": "string", - "default": "maybe", - "permitted_values": [ - "yes", - "no", - "maybe" - ], - "placeholder": "", - "weight": { - "$numberInt": "40" - }, - "instant_facet": false, - "required": false, - "notes": "While almost every AI system undergoes testing or demonstration in a controlled environment, some also undergo testing or demonstration in an operational environment. Testing in operational environments still occurs before the system is deployed or sold to end-users. However, relative to controlled environments, operational environments try to closely represent real-world conditions and end-users that affect use of the AI system. Therefore, testing in an operational environment typically poses a heightened risk of harm to people, organizations, property, institutions, or the environment." - }, - { - "field_number": "2.7", - "short_name": "User Test in Controlled Conditions", - "long_name": "Was this a test or demonstration done by users in controlled conditions?", - "short_description": "Was this a test or demonstration done by users in controlled conditions?", - "long_description": "“Yes” if it was a test/demonstration performed by users in controlled conditions. “No” if it was not a test/demonstration. “No” if the test/demonstration was done by developers, producers or researchers. “No” if the test/demonstration was in controlled or non-controlled conditions.“Maybe” otherwise.", - "display_type": "enum", - "mongo_type": "string", - "default": "maybe", - "permitted_values": [ - "yes", - "no", - "maybe" - ], - "placeholder": "", - "weight": { - "$numberInt": "40" - }, - "instant_facet": false, - "required": false, - "notes": "The involvement of a user (versus a developer, producer, or researcher) increases the likelihood that harm can occur even if the AI system is being tested. Relative to controlled environments, controlled environments try to closely represent real-world conditions and end-users that affect use of the AI system. Therefore, testing in an controlled environment typically poses a heightened risk of harm to people, organizations, property, institutions, or the environment." - }, - { - "field_number": "2.8", - "short_name": "User Test in Operational Conditions", - "long_name": "Was this a test or demonstration done by users in operational conditions?", - "short_description": "Was this a test or demonstration done by users in operational conditions?", - "long_description": "“Yes” if it was a test/demonstration performed by users in operational conditions. “No” if it was not a test/demonstration. “No” if the test/demonstration was done by developers, producers or researchers. “No” if the test/demonstration was in controlled or non-operational conditions.“Maybe” otherwise.", - "display_type": "enum", - "mongo_type": "string", - "default": "maybe", - "permitted_values": [ - "yes", - "no", - "maybe" - ], - "placeholder": "", - "weight": { - "$numberInt": "40" - }, - "instant_facet": false, - "required": false, - "notes": "Sometimes, prior to deployment, the user will perform a test or demonstration of the AI system. The involvement of a user (versus a developer, producer, or researcher) increases the likelihood that harm can occur even if the AI system is being tested in controlled environments." - }, - { - "field_number": "2.9", - "short_name": "Harm Domain", - "long_name": "Incident occurred in a domain where we could expect harm to occur?", - "short_description": "Incident occurred in a domain where we could likely expect harm to occur?", - "long_description": "Using the answers to the 8 domain questions, assess if the incident occurred in a domain where harm could be expected to occur. If you are unclear, input “maybe.”", - "display_type": "enum", - "mongo_type": "string", - "default": "maybe", - "permitted_values": [ - "yes", - "no", - "maybe" - ], - "placeholder": "", - "weight": { - "$numberInt": "40" - }, - "instant_facet": false, - "required": false, - "hide_search": true, - "notes": "Reflecting upon the previously answered questions, decide if the reported incident or instance occurred in a domain in which harm could possibly occur. This is not a decision on whether or not harm did occur. Just a reflection on the operating conditions or context of the system." - }, - { - "field_number": "3.1", - "short_name": "Tangible Harm", - "long_name": "Did tangible harm (loss, damage or injury ) occur? ", - "short_description": "Did tangible harm (loss, damage or injury ) occur? ", - "long_description": "An assessment of whether tangible harm, imminent tangible harm, or non-imminent tangible harm occurred. This assessment does not consider the context of the tangible harm, if an AI was involved, or if there is an identifiable, specific, and harmed entity. It is also not assessing if an intangible harm occurred. It is only asking if tangible harm occurred and what its imminency was.", - "display_type": "enum", - "mongo_type": "string", - "default": "unclear", - "placeholder": "", - "permitted_values": [ - "tangible harm definitively occurred", - "imminent risk of tangible harm (near miss) did occur", - "non-imminent risk of tangible harm (an issue) occurred", - "no tangible harm, near-miss, or issue", - "unclear" - ], - "weight": { - "$numberInt": "40" - }, - "instant_facet": false, - "required": false, - "public": true - }, - { - "field_number": "3.2", - "short_name": "AI System", - "long_name": "Does the incident involve an AI system?", - "short_description": "Does the incident involve an AI system?", - "long_description": "An assessment of whether or not an AI system was involved. It is sometimes difficult to judge between an AI and an automated system or expert rules system. In these cases select “maybe”", - "display_type": "enum", - "mongo_type": "string", - "default": "maybe", - "permitted_values": [ - "yes", - "no", - "maybe" - ], - "placeholder": "", - "weight": { - "$numberInt": "40" - }, - "instant_facet": false, - "required": false, - "public": true, - "notes": "Note, over time more information about the incident may become available, allowing a ‘maybe’ to be changed to a ‘yes’ or ‘no.’" - }, - { - "field_number": "3.3", - "short_name": "Clear link to technology", - "long_name": "Can the technology be directly and clearly linked to the adverse outcome of the incident", - "short_description": "Can the technology be directly and clearly linked to the adverse outcome of the incident", - "long_description": "An assessment of the technology's involvement in the chain of harm. \"Yes\" indicates that the technology was involved in harm, its behavior can be directly linked to the harm, and the harm may not have occurred if the technology acted differently. \"No\", indicates that the technology's behavior cannot be linked to the harm outcome. \"Maybe\" indicates that the link is unclear.", - "hide_search": true, - "display_type": "enum", - "mongo_type": "string", - "default": "maybe", - "permitted_values": [ - "yes", - "no", - "maybe" - ], - "placeholder": "", - "weight": { - "$numberInt": "40" - }, - "instant_facet": false, - "required": false, - "public": true - }, - { - "field_number": "3.4", - "short_name": "There is a potentially identifiable specific entity that experienced the harm", - "long_name": "There is a potentially identifiable specific entity that experienced the harm", - "short_description": "A potentially identifiable specific entity that experienced the harm can be characterized or identified.", - "long_description": "“Yes” if it is theoretically possible to both specify and identify the entity. Having that information is not required. The information just needs to exist and be potentially discoverable. “No” if there are not any potentially identifiable specific entities or if the harmed entities are a class or subgroup that can only be characterized. ", - "display_type": "bool", - "mongo_type": "bool", - "permitted_values": [], - "placeholder": "", - "default": "", - "weight": { - "$numberInt": "40" - }, - "instant_facet": false, - "required": false, - "public": true, - "hide_search": true, - "notes": "A potentially identifiable specific entity is an entity that can be described in detail such that the name (Mr. Joe Smith, Acme Inc, etc.) or a unique identifier (e.g. 100 Main Street, Anywhere USA) of the entity could be found. We may not know the name or identifier of the entity from the reports, but it does exist and could be found. For example, the general public is not a potentially identifiable specific entity. However, incarcerated people in the Springfield penitentiary would be specific entities because it would be possible to get a list of all the prisoners in the facility." - }, - { - "field_number": "3.5", - "short_name": "AI Harm Level", - "long_name": "Annotator's AI tangible harm level assessment", - "short_description": "An assessment of the AI tangible harm level, which takes into account the CSET definitions of AI tangible harm levels, along with the inputs for annotation fields about the AI, harm, chain of harm, and entity. ", - "long_description": "An assessment of the AI tangible harm level, which takes into account the CSET definitions of AI tangible harm levels, along with the inputs for annotation fields about the AI, harm, chain of harm, and entity.", - "display_type": "enum", - "mongo_type": "string", - "default": "unclear", - "placeholder": "", - "permitted_values": [ - "AI tangible harm event", - "AI tangible harm near-miss", - "AI tangible harm issue", - "none", - "unclear" - ], - "weight": { - "$numberInt": "40" - }, - "instant_facet": false, - "required": false, - "public": true, - "notes": "Special interest intangible harm is determined in a different field. The determination of a special interest intangible harm is not dependant upon the AI tangible harm level" - }, - { - "field_number": "3.6", - "short_name": "AI Tangible Harm Level Notes", - "long_name": "AI Tangible Harm Level Notes", - "short_description": "Notes about the AI tangible harm level assessment", - "long_description": "If for 3.5 you select unclear or leave it blank, please provide a brief description of why.\n\n You can also add notes if you want to provide justification for a level", - "display_type": "long_string", - "mongo_type": "string", - "default": "", - "placeholder": "Notes about the AI tangible harm level assessment", - "permitted_values": [], - "weight": { - "$numberInt": "5" - }, - "instant_facet": false, - "required": false, - "public": true, - "hide_search": true - }, - { - "field_number": "4.1", - "short_name": "Impact on Critical Services", - "long_name": "Did this impact people's access to critical or public services (health care, social services, voting, transportation, etc)?", - "short_description": "Indicates if people’s access to critical public services was impacted.", - "long_description": "Did this impact people's access to critical or public services (health care, social services, voting, transportation, etc)?", - "display_type": "enum", - "mongo_type": "string", - "default": "maybe", - "permitted_values": [ - "yes", - "no", - "maybe" - ], - "placeholder": "", - "weight": { - "$numberInt": "40" - }, - "instant_facet": false, - "required": false, - "public": true, - "notes": "Public services include healthcare, social services, voting, public transportation, education, and consumer protection.\n\nNote, if ‘yes’ is selected then there was likely a violation of civil liberties and there was a special interest intangible harm." - }, - { - "field_number": "4.2", - "short_name": "Rights Violation", - "long_name": "Was this a violation of human rights, civil liberties, civil rights, or democratic norms?", - "short_description": "Indicate if a violation of human rights, civil rights, civil liberties, or democratic norms occurred.", - "long_description": "Indicate if a violation of human rights, civil rights, civil liberties, or democratic norms occurred.", - "display_type": "enum", - "mongo_type": "string", - "default": "maybe", - "permitted_values": [ - "yes", - "no", - "maybe" - ], - "placeholder": "", - "weight": { - "$numberInt": "40" - }, - "instant_facet": false, - "required": false, - "public": true, - "notes": "It can often be difficult for the typical annotator to differentiate between violations of civil liberties, civil rights, human rights, and democratic norms. For this reason CSET grouped them together.\n\nHuman rights are rights inherent to all human beings, regardless of race, sex, nationality, ethnicity, language, religion, or any other status. They include the right to life and liberty, freedom from slavery and torture, freedom of opinion and expression, and the right to work and education. Civil rights are legal provisions that originate from notions of equality and can be enforced by law. Civil liberties are personal freedoms that are referenced in the Bill of Rights. Democratic norms are traditions, customs, and best practices that support democracy. An example of a democratic norm is accepting election results and facilitating a peaceful transfer of political power. " - }, - { - "field_number": "4.3", - "short_name": "Involving Minor", - "long_name": "Was a minor involved in the incident (disproportionally treated or specifically targeted/affected)", - "short_description": "Was a minor involved in the incident (disproportionally treated or specifically targeted/affected)", - "long_description": "Indicate if a minor was disproportionately targeted or affected", - "display_type": "enum", - "mongo_type": "string", - "default": "maybe", - "permitted_values": [ - "yes", - "no", - "maybe" - ], - "placeholder": "", - "weight": { - "$numberInt": "40" - }, - "instant_facet": false, - "required": false, - "public": true, - "notes": "Generally, governments have an interest in establishing heightened protections for minors. These protections are often associated with media content or privacy. For example, if an AI system illegally tracked a minor’s activity online, then answer “yes” to this question. There are instances where an AI system causes indiscriminate harm to a group of people, and it is plausible that some of those people are minors. However, in this case the entire group of people, adults and children alike, shared the distribution of harm equally and therefore the answer to this question would be “no.”" - }, - { - "field_number": "4.4", - "short_name": "Detrimental Content", - "long_name": "Was detrimental content (misinformation, hate speech) involved?", - "short_description": "Was detrimental content (misinformation, hate speech) involved?", - "long_description": "Detrimental content can include deepfakes, identity misrepresentation, insults, threats of violence, eating disorder or self harm promotion, extremist content, misinformation, sexual abuse material, and scam emails. Detrimental content in itself is often not harmful, however, it can lead to or instigate injury, damage, or loss.", - "display_type": "enum", - "mongo_type": "string", - "default": "maybe", - "permitted_values": [ - "yes", - "no", - "maybe" - ], - "placeholder": "", - "weight": { - "$numberInt": "40" - }, - "instant_facet": false, - "required": false, - "public": true - }, - { - "field_number": "4.5", - "short_name": "Protected Characteristic", - "long_name": "Was a group of people or an individual treated differently based upon a protected characteristic?", - "short_description": "Was a group of people treated differently based upon a protected characteristic (e.g. race, ethnicity, creed, immigrant status, color, religion, sex, national origin, age, disability, genetic information)?", - "long_description": "Protected characteristics include religion, commercial facilities, geography, age, sex, sexual orientation or gender identity, familial status (e.g., having or not having children) or pregnancy, disability, veteran status, genetic information, financial means, race or creed, Ideology, nation of origin, citizenship, and immigrant status.\n\nAt the federal level in the US, age is a protected characteristic for people over the age of 40. Minors are not considered a protected class. For this reason the CSET annotation taxonomy has a separate field to note if a minor was involved.\n\nOnly mark yes if there is clear evidence discrimination occurred. If there are conflicting accounts, mark unsure. Do not mark that discrimination occurred based on expectation alone.", - "display_type": "enum", - "mongo_type": "string", - "default": "maybe", - "permitted_values": [ - "yes", - "no", - "maybe" - ], - "placeholder": "", - "weight": { - "$numberInt": "40" - }, - "instant_facet": false, - "required": false, - "public": true - }, - { - "field_number": "4.6", - "short_name": "Harm Distribution Basis", - "long_name": "If harms were potentially unevenly distributed among people, on what basis?", - "short_description": "Indicates how the harms were potentially distributed.", - "long_description": "Multiple can occur.\n\nGenetic information refers to information about a person’s genetic tests or the genetic tests of their relatives. Genetic information can predict the manifestation of a disease or disorder.", - "display_type": "multi", - "mongo_type": "string", - "default": "unclear", - "placeholder": "", - "permitted_values": [ - "none", - "age", - "disability", - "familial status (e.g., having or not having children) or pregnancy", - "financial means", - "genetic information", - "geography", - "ideology", - "nation of origin, citizenship, immigrant status", - "race", - "religion", - "sex", - "sexual orientation or gender identity", - "veteran status", - "unclear", - "other" - ], - "weight": { - "$numberInt": "60" - }, - "instant_facet": false, - "required": false, - "public": true - }, - { - "field_number": "4.7", - "short_name": "Notes (special interest intangible harm)", - "long_name": "Input any notes that may help explain your answers.", - "short_description": "Input any notes that may help explain your answers.", - "long_description": "Input any notes that may help explain your answers.", - "display_type": "long_string", - "mongo_type": "string", - "default": "", - "placeholder": "Notes", - "permitted_values": [], - "weight": { - "$numberInt": "5" - }, - "instant_facet": false, - "required": false, - "public": true, - "hide_search": true - }, - { - "field_number": "5.1", - "short_name": "Special Interest Intangible Harm", - "long_name": "Was there a special interest intangible harm or risk of harm?", - "short_description": "An assessment of whether a special interest intangible harm occurred. This assessment does not consider the context of the intangible harm, if an AI was involved, or if there is characterizable class or subgroup of harmed entities. It is also not assessing if an intangible harm occurred. It is only asking if a special interest intangible harm occurred.", - "long_description": "An assessment of whether a special interest intangible harm occurred. This assessment does not consider the context of the intangible harm, if an AI was involved, or if there is characterizable class or subgroup of harmed entities. It is also not assessing if an intangible harm occurred. It is only asking if a special interest intangible harm occurred.", - "display_type": "enum", - "mongo_type": "string", - "default": "maybe", - "permitted_values": [ - "yes", - "no", - "maybe" - ], - "placeholder": "", - "weight": { - "$numberInt": "5" - }, - "instant_facet": false, - "required": false, - "public": true - }, - { - "field_number": "5.2", - "short_name": "AI System", - "long_name": "Does the incident involve an AI system?", - "short_description": "Does the incident involve an AI system?", - "long_description": "An assessment of whether or not an AI system was involved. It is sometimes difficult to judge between an AI and an automated system or expert rules system. In these cases select “maybe”", - "display_type": "enum", - "mongo_type": "string", - "default": "maybe", - "permitted_values": [ - "yes", - "no", - "maybe" - ], - "placeholder": "", - "weight": { - "$numberInt": "40" - }, - "instant_facet": false, - "required": false, - "public": true, - "hide_search": true, - "notes": "Note, over time more information about the incident may become available, allowing a ‘maybe’ to be changed to a ‘yes’ or ‘no.’" - }, - { - "field_number": "5.3", - "short_name": "Clear link to Technology", - "long_name": "Can the technology be directly and clearly linked to the adverse outcome of the incident?", - "short_description": "Can the technology be directly and clearly linked to the adverse outcome of the incident?", - "long_description": "An assessment of the technology's involvement in the chain of harm. \"Yes\" indicates that the technology was involved in harm, its behavior can be directly linked to the harm, and the harm may not have occurred if the technology acted differently. \"No\", indicates that the technology's behavior cannot be linked to the harm outcome. \"Maybe\" indicates that the link is unclear.", - "display_type": "enum", - "mongo_type": "string", - "default": "maybe", - "permitted_values": [ - "yes", - "no", - "maybe" - ], - "placeholder": "", - "weight": { - "$numberInt": "40" - }, - "instant_facet": false, - "required": false, - "public": true - }, - { - "field_number": "5.4", - "short_name": "Harmed Class of Entities", - "long_name": "There is a characterizable class or subgroup of entities that experienced the harm", - "short_description": "“Yes” if the harmed entity or entities can be characterized. “No” if there are not any characterizable entities.", - "long_description": "A characterizable class or subgroup are descriptions of different populations of people. Often they are characteristics by which people qualify for special protection by a law, policy, or similar authority.\n\n Sometimes, groups may be characterized by their exposure to the incident via geographical proximity (e.g., ‘visitors to the park’) or participation in an activity (e.g.,‘Twitter users’).", - "display_type": "bool", - "mongo_type": "bool", - "permitted_values": [], - "placeholder": "", - "weight": { - "$numberInt": "40" - }, - "instant_facet": false, - "required": false, - "public": true, - "hide_search": true, - "notes": "" - }, - { - "field_number": "5.5", - "short_name": "Annotator’s AI special interest intangible harm assessment", - "long_name": "The annotator’s assessment of if an AI special interest intangible harm occurred.", - "short_description": "The annotator’s assessment of if an AI special interest intangible harm occurred.", - "long_description": "AI tangible harm is determined in a different field. The determination of a special interest intangible harm is not dependant upon the AI tangible harm level.", - "display_type": "enum", - "mongo_type": "string", - "default": "maybe", - "permitted_values": [ - "yes", - "no", - "maybe" - ], - "placeholder": "", - "weight": { - "$numberInt": "40" - }, - "instant_facet": false, - "required": false, - "public": true, - "notes": "" - }, - { - "field_number": "5.6", - "short_name": "Notes (AI special interest intangible harm)", - "long_name": "If for 5.5 you select unclear or leave it blank, please provide a brief description of why.\n\nYou can also add notes if you want to provide justification for a level.", - "short_description": "If for 5.5 you select unclear or leave it blank, please provide a brief description of why.\n\nYou can also add notes if you want to provide justification for a level.", - "long_description": "If for 5.5 you select unclear or leave it blank, please provide a brief description of why.\n\nYou can also add notes if you want to provide justification for a level.", - "display_type": "long_string", - "mongo_type": "string", - "default": "", - "placeholder": "Notes", - "permitted_values": [], - "weight": { - "$numberInt": "5" - }, - "instant_facet": false, - "required": false, - "public": true, - "hide_search": true - }, - { - "field_number": "6.1", - "short_name": "Date of Incident Year", - "long_name": "The year in which the incident first occurred.", - "short_description": "The year in which the incident occurred. If there are multiple harms or occurrences of the incident, list the earliest. If a precise date is unavailable, but the available sources provide a basis for estimating the year, estimate. Otherwise, leave blank.\n\nEnter in the format of YYYY", - "long_description": "The year in which the incident occurred. If there are multiple harms or occurrences of the incident, list the earliest. If a precise date is unavailable, but the available sources provide a basis for estimating the year, estimate. Otherwise, leave blank.", - "display_type": "string", - "mongo_type": "string", - "default": "", - "placeholder": "YYYY", - "permitted_values": [], - "weight": { - "$numberInt": "5" - }, - "instant_facet": false, - "required": false, - "public": true - }, - { - "field_number": "6.2", - "short_name": "Date of Incident Month", - "long_name": "The month in which the incident first occurred.", - "short_description": "The month in which the incident occurred. If there are multiple harms or occurrences of the incident, list the earliest. If a precise date is unavailable, but the available sources provide a basis for estimating the month, estimate. Otherwise, leave blank.\n\nEnter in the format of MM", - "long_description": "The month in which the incident occurred. If there are multiple harms or occurrences of the incident, list the earliest. If a precise date is unavailable, but the available sources provide a basis for estimating the month, estimate. Otherwise, leave blank.", - "display_type": "string", - "mongo_type": "string", - "default": "", - "placeholder": "MM", - "permitted_values": [], - "weight": { - "$numberInt": "5" - }, - "instant_facet": false, - "required": false, - "public": true, - "hide_search": true - }, - { - "field_number": "6.3", - "short_name": "Date of Incident Day", - "long_name": "The day on which the first incident occurred.", - "short_description": "The day on which the incident occurred. If a precise date is unavailable, leave blank.\n\nEnter in the format of DD", - "long_description": "The day on which the incident occurred. If a precise date is unavailable, leave blank.\n\nEnter in the format of DD", - "display_type": "string", - "mongo_type": "string", - "default": "", - "placeholder": "DD", - "permitted_values": [], - "weight": { - "$numberInt": "5" - }, - "instant_facet": false, - "required": false, - "public": true, - "hide_search": true - }, - { - "field_number": "6.4", - "short_name": "Estimated Date", - "long_name": "Is the date estimated?", - "short_description": "“Yes” if the data was estimated. “No” otherwise.", - "long_description": "“Yes” if the data was estimated. “No” otherwise.", - "display_type": "bool", - "mongo_type": "bool", - "permitted_values": [], - "placeholder": "", - "weight": { - "$numberInt": "5" - }, - "instant_facet": false, - "required": false, - "public": true, - "hide_search": true - }, - { - "field_number": "6.5", - "short_name": "Multiple AI Interaction", - "long_name": "Was the AI interacting with another AI?", - "short_description": "“Yes” if two or more independently operating AI systems were involved. “No” otherwise.", - "long_description": "This happens very rarely but is possible. Examples include two chatbots having a conversation with each other, or two autonomous vehicles in a crash.", - "display_type": "enum", - "mongo_type": "string", - "default": "maybe", - "permitted_values": [ - "yes", - "no", - "maybe" - ], - "placeholder": "", - "weight": { - "$numberInt": "5" - }, - "instant_facet": false, - "required": false, - "public": true - }, - { - "field_number": "6.6", - "short_name": "Embedded", - "long_name": "Is the AI embedded in a physical system or have a physical presence?", - "short_description": "“Yes” if the AI is embedded in a physical system. “No” if it is not. “Maybe” if it is unclear.", - "long_description": "This question is slightly different from the one in field 2.1.1. That question asks about there being interaction with physical objects–an ability to manipulate or change. A system can be embedded in a physical object and able to interact with the physical environment, e.g. a vacuum robot. A system can be embedded in a physical object and not interact with a physical environment, e.g. a camera system that only records images when the AI detects that dogs are present. AI systems that are accessed through API, web-browser, etc by using a mobile device or computer are not considered to be embedded in hardware systems. They are accessed through hardware.", - "display_type": "enum", - "mongo_type": "string", - "default": "maybe", - "permitted_values": [ - "yes", - "no", - "maybe" - ], - "placeholder": "", - "weight": { - "$numberInt": "5" - }, - "instant_facet": false, - "required": false, - "public": true - }, - { - "field_number": "6.7", - "short_name": "Location City", - "long_name": "If the incident occurred at a specific known location, note the city.", - "short_description": "If the incident occurred at a specific known location, note the city.", - "long_description": "If the incident occurred at a specific known location, note the city. If there are multiple relevant locations, enter multiple city/state/country values.", - "display_type": "string", - "mongo_type": "string", - "default": "", - "placeholder": "City", - "permitted_values": [], - "weight": { - "$numberInt": "5" - }, - "instant_facet": false, - "required": false, - "public": true, - "hide_search": true - }, - { - "field_number": "6.8", - "short_name": "Location State/Province (two letters)", - "long_name": "If the incident occurred at a specific known location, note the state/province.", - "short_description": "If the incident occurred at a specific known location, note the state/province.", - "long_description": "If the incident occurred at a specific known location, note the state/province. If there are multiple relevant locations, enter multiple city/state/country values.", - "display_type": "string", - "mongo_type": "string", - "default": "", - "placeholder": "State or Province", - "permitted_values": [], - "weight": { - "$numberInt": "5" - }, - "instant_facet": false, - "required": false, - "public": true, - "hide_search": true - }, - { - "field_number": "6.9", - "short_name": "Location Country (two letters)", - "long_name": "If the incident occurred at a specific known location, note the country. ", - "short_description": "If the incident occurred at a specific known location, note the country. Follow ISO 3166 for the 2-letter country codes.", - "long_description": "Follow ISO 3166 for the 2-letter country codes.\n\nIf there are multiple relevant locations, enter multiple city/state/country values.", - "display_type": "string", - "mongo_type": "string", - "default": "", - "placeholder": "Country", - "permitted_values": [], - "weight": { - "$numberInt": "5" - }, - "instant_facet": false, - "required": false, - "public": true - }, - { - "field_number": "6.10", - "short_name": "Location Region", - "long_name": "Location Region", - "short_description": "Select the region of the world where the incident occurred. If it occurred in multiple, leave blank.", - "long_description": "Use this reference to map countries to regions: https://www.dhs.gov/geographic-regions", - "display_type": "enum", - "mongo_type": "string", - "default": "unclear", - "placeholder": "", - "permitted_values": [ - "Global", - "Africa", - "Asia", - "Caribbean", - "Central America", - "Europe", - "North America", - "Oceania", - "South America", - "unclear" - ], - "weight": { - "$numberInt": "5" - }, - "instant_facet": false, - "required": false, - "public": true - }, - { - "field_number": "6.11", - "short_name": "Infrastructure Sectors", - "long_name": "Which critical infrastructure sectors were affected, if any?", - "short_description": "Which critical infrastructure sectors were affected, if any?", - "long_description": "Which critical infrastructure sectors were affected, if any?", - "display_type": "multi", - "mongo_type": "string", - "default": "unclear", - "placeholder": "", - "permitted_values": [ - "chemical", - "commercial facilities", - "communications", - "critical manufacturing", - "dams", - "defense-industrial base", - "emergency services", - "energy", - "financial services", - "food and agriculture", - "government facilities", - "healthcare and public health", - "information technology", - "nuclear ", - "transportation", - "water and wastewater", - "Other", - "unclear" - ], - "weight": { - "$numberInt": "5" - }, - "instant_facet": false, - "required": false, - "public": true - }, - { - "field_number": "6.12", - "short_name": "Operating Conditions", - "long_name": "A record of any abnormal or atypical operational conditions that occurred.", - "short_description": "A record of any abnormal or atypical operational conditions that occurred.", - "long_description": "A record of any abnormal or atypical operational conditions that occurred. This field is most often blank.", - "display_type": "list", - "mongo_type": "array", - "default": "", - "placeholder": "e.g. raining; night; low visibility", - "permitted_values": [], - "weight": { - "$numberInt": "5" - }, - "instant_facet": false, - "required": false, - "public": true - }, - { - "field_number": "6.13", - "short_name": "Notes (Environmental and Temporal Characteristics)", - "long_name": "Notes (Environmental and Temporal Characteristics)", - "short_description": "Input any notes that may help explain your answers.", - "long_description": "Input any notes that may help explain your answers.", - "display_type": "long_string", - "mongo_type": "string", - "default": "", - "placeholder": "Notes", - "permitted_values": [], - "weight": { - "$numberInt": "5" - }, - "instant_facet": false, - "required": false, - "public": true, - "hide_search": true - }, - { - "field_number": "7", - "short_name": "Entities", - "long_name": "Characterizing Entities and the Harm", - "short_description": "Characterizing Entities and the Harm", - "long_description": "Characterizing Entities and the Harm", - "display_type": "object-list", - "mongo_type": "array", - "hide_search": true, - "subfields": [ - { - "field_number": "7.1", - "short_name": "Entity", - "long_name": "A short 1 to 2 word description of the entity.", - "short_description": "A short 1 to 2 word description of the entity. When possible use a proper name for the entity, making it a Named Entity.", - "long_description": "A short 1 to 2 word description of the entity. When possible use a proper name for the entity, making it a Named Entity.\n\nAnnotate information for each entity involved in the report. Try to capture every entity directly linked to the harm. Think about the entity that experienced the harm, all of the entities between them and the AI, and then all of the entities involved in producing and deploying the AI.\n\nEmployees representing a company in a media or public relations capacity should not be included as an entity.", - "display_type": "string", - "mongo_type": "string", - "complete_from": { - "entities": true - }, - "default": "", - "placeholder": "", - "permitted_values": [], - "weight": { - "$numberInt": "5" - }, - "instant_facet": false, - "required": false, - "public": true - }, - { - "field_number": "7.2", - "short_name": "Named Entity", - "long_name": "Named Entity Indicator", - "short_description": "Indicates if the entity is a Named Entity.", - "long_description": "Indicates if the entity is a Named Entity. “Yes” if the entity is a named entity. “No” otherwise.", - "display_type": "bool", - "mongo_type": "bool", - "permitted_values": [], - "placeholder": "", - "weight": { - "$numberInt": "5" - }, - "instant_facet": false, - "required": false, - "public": true - }, - { - "field_number": "7.3", - "short_name": "Entity type", - "long_name": "Indicates the type of entity", - "short_description": "Indicates the type of entity", - "long_description": "Indicates the type of entity. If multiple selections could characterize the entity, select the primary function of the entity.", - "display_type": "enum", - "mongo_type": "string", - "default": "unclear", - "placeholder": "Entity Type", - "permitted_values": [ - "individual", - "group of individuals", - "for-profit organization", - "non-profit organization", - "government entity", - "privately owned space", - "public space", - "infrastructure", - "social or political system", - "product", - "other", - "unclear" - ], - "weight": { - "$numberInt": "5" - }, - "instant_facet": false, - "required": false, - "public": true, - "hide_search": true - }, - { - "field_number": "7.4", - "short_name": "Entity Relationship to the AI", - "long_name": "Entity Relationship to the AI", - "short_description": "Indicates the entity’s relationship to the AI.", - "long_description": "Indicates the entity’s relationship to the AI. Note, the smallest possible chain of harm has just two elements; an AI and an entity experiencing harm, near-miss, or issue.", - "display_type": "list", - "mongo_type": "array", - "default": "", - "placeholder": "", - "permitted_values": [ - "developer", - "deployer", - "government oversight", - "user", - "AI", - "geographic area of use", - "researcher", - "product containing AI", - "watchdog" - ], - "weight": { - "$numberInt": "5" - }, - "instant_facet": false, - "required": false, - "public": true - }, - { - "field_number": "7.5", - "short_name": "Harm Category Experienced", - "long_name": "Was an AI special interest intangible harm, tangible harm event, near-miss, or issue experienced by this entity", - "short_description": "Was an AI special interest intangible harm, tangible harm event, near-miss, or issue experienced by this entity", - "long_description": "Was an AI special interest intangible harm, tangible harm event, near-miss, or issue experienced by this entity. For each recorded entity, indicate the harm category that they experienced. Because recorded entities have a variety of roles in the AI incident, not every recorded entity will experience harm.", - "display_type": "enum", - "mongo_type": "string", - "default": "unclear", - "placeholder": "", - "permitted_values": [ - "AI special interest intangible harm", - "AI tangible harm event", - "AI tangible harm near-miss", - "AI tangible harm issue", - "Other harm not meeting CSET definitions", - "not applicable", - "unclear" - ], - "weight": { - "$numberInt": "5" - }, - "instant_facet": false, - "required": false, - "public": true - }, - { - "field_number": "7.6", - "short_name": "Harm Type Experienced", - "long_name": "Type of harm experienced by entity ", - "short_description": "Indicates the type of harm experienced by the harmed entity", - "long_description": "Indicates the type of harm experienced by the harmed entity. Only entities experiencing harm should have an assigned type. If the entity did not experience the harm, ‘not applicable’ should be selected.", - "display_type": "enum", - "mongo_type": "string", - "default": "not applicable", - "placeholder": "", - "permitted_values": [ - "physical health/safety", - "financial loss", - "physical property", - "intangible property", - "infrastructure", - "natural environment", - "social or political systems", - "violation of human rights, civil liberties, civil rights, or democratic norms", - "detrimental content", - "disproportionate treatment based upon a protected characteristic", - "other tangible harm", - "other intangible harm", - "not applicable" - ], - "weight": { - "$numberInt": "5" - }, - "instant_facet": false, - "required": false, - "public": true - }, - { - "field_number": "7.7", - "short_name": "Notes (Characterizing Entities and the Harm)", - "long_name": "Notes (Characterizing Entities and the Harm)", - "short_description": "Input any notes that may help explain your answers.", - "long_description": "Input any notes that may help explain your answers.", - "display_type": "long_string", - "mongo_type": "string", - "default": "", - "placeholder": "Notes", - "permitted_values": [], - "weight": { - "$numberInt": "5" - }, - "instant_facet": false, - "required": false, - "public": true, - "hide_search": true - } - ], - "default": "[]", - "placeholder": "", - "permitted_values": [], - "weight": { - "$numberInt": "5" - }, - "instant_facet": false, - "required": false, - "public": true - }, - { - "field_number": "8.1", - "short_name": "Lives Lost", - "long_name": "How many human lives were lost?", - "short_description": "Indicates the number of deaths reported", - "long_description": "This field cannot be greater than zero if the harm is anything besides ‘Physical health/safety.’ ", - "display_type": "int", - "mongo_type": "int", - "default": "", - "placeholder": "", - "permitted_values": [], - "weight": { - "$numberInt": "5" - }, - "instant_facet": false, - "required": false, - "public": true - }, - { - "field_number": "8.2", - "short_name": "Injuries", - "long_name": "How many humans were injured?", - "short_description": "Indicate the number of injuries reported.", - "long_description": "This field cannot be greater than zero if the harm is anything besides 'Physical health/safety'.\n\nAll reported injuries should count, regardless of their severity level. If a person lost their limb and another person scraped their elbow, both cases would be considered injuries. Do not include the number of deaths in this count.", - "display_type": "int", - "mongo_type": "int", - "default": "", - "placeholder": "", - "permitted_values": [], - "weight": { - "$numberInt": "5" - }, - "instant_facet": false, - "required": false, - "public": true - }, - { - "field_number": "8.3", - "short_name": "Estimated Harm Quantities", - "long_name": "Are any quantities estimated?", - "short_description": "Indicates if the amount was estimated.", - "long_description": "Indicates if the amount was estimated.", - "display_type": "bool", - "mongo_type": "bool", - "permitted_values": [], - "placeholder": "", - "weight": { - "$numberInt": "5" - }, - "instant_facet": false, - "required": false, - "hide_search": true, - "public": true - }, - { - "field_number": "8.4", - "short_name": "Notes ( Tangible Harm Quantities Information)", - "long_name": "Notes ( Tangible Harm Quantities Information)", - "short_description": "Input any notes that may help explain your answers.", - "long_description": "Input any notes that may help explain your answers.", - "display_type": "long_string", - "mongo_type": "string", - "default": "", - "placeholder": "Notes", - "permitted_values": [], - "weight": { - "$numberInt": "5" - }, - "instant_facet": false, - "required": false, - "public": true, - "hide_search": true - }, - { - "field_number": "9.1", - "short_name": "AI System Description", - "long_name": "Description of the AI system involved", - "short_description": "A description of the AI system (when possible)", - "long_description": "Describe the AI system in as much detail as the reports will allow.\n\nA high level description of the AI system is sufficient, but if more technical details about the AI system are available, include them in the description as well.", - "display_type": "long_string", - "mongo_type": "string", - "default": "", - "placeholder": "Description of the AI system involved", - "permitted_values": [], - "weight": { - "$numberInt": "5" - }, - "instant_facet": false, - "required": false, - "public": true, - "hide_search": true - }, - { - "field_number": "9.2", - "short_name": "Data Inputs", - "long_name": "Description of data inputs to the AI system", - "short_description": "A list of the types of data inputs for the AI system.", - "long_description": "This is a freeform field that can have any value. There could be multiple entries for this field.\n\nCommon ones include\n\n- still images\n- video\n- text\n- speech\n- Personally Identifiable Information\n- structured data\n- other\n- unclear\n\nStill images are static images. Video images consist of moving images. Text and speech data are considered an important category of unstructured data. They consist of written and spoken words that are not in a tabular format. Personally identifiable information is data that can uniquely identify an individual and may contain sensitive information. Structured data is often in a tabular, machine readable format and can typically be used by an AI system without much preprocessing.\n\nAvoid using ‘unstructured data’ data in this field. Instead specify the type of unstructured data; text, images, audio files, etc. It is ok to use ‘structured data’ in this field.\n\nRecord what the media report explicitly states. If the report does not explicitly state an input modality but it is likely that a particular kind of input contributed to the harm or near harm, record that input. If you are still unsure, do not record anything.", - "display_type": "list", - "mongo_type": "array", - "default": "", - "placeholder": "", - "permitted_values": [], - "weight": { - "$numberInt": "5" - }, - "instant_facet": false, - "required": false, - "public": true - }, - { - "field_number": "9.3", - "short_name": "Sector of Deployment", - "long_name": "Indicates the sector in which the AI system is deployed", - "short_description": "Indicate the sector in which the AI system is deployed", - "long_description": "Indicate the sector in which the AI system is deployed\n\nThere could be multiple entries for this field.", - "display_type": "multi", - "mongo_type": "array", - "default": "", - "placeholder": "", - "permitted_values": [ - "agriculture, forestry and fishing", - "mining and quarrying", - "manufacturing", - "electricity, gas, steam and air conditioning supply", - "water supply", - "construction", - "wholesale and retail trade", - "transportation and storage", - "accommodation and food service activities", - "information and communication", - "financial and insurance activities", - "real estate activities", - "professional, scientific and technical activities", - "administrative and support service activities", - "public administration", - "defense", - "law enforcement", - "Education", - "human health and social work activities", - "Arts, entertainment and recreation", - "other service activities", - "activities of households as employers", - "activities of extraterritorial organizations and bodies", - "other", - "unclear" - ], - "weight": { - "$numberInt": "60" - }, - "instant_facet": false, - "required": false, - "public": true - }, - { - "field_number": "9.4", - "short_name": "Public Sector Deployment", - "long_name": "Indicates whether the AI system is deployed in the public sector", - "short_description": "Indicate whether the AI system is deployed in the public sector", - "long_description": "Indicate whether the AI system is deployed in the public sector. The public sector is the part of the economy that is controlled and operated by the government.", - "display_type": "enum", - "mongo_type": "string", - "default": "maybe", - "permitted_values": [ - "yes", - "no", - "maybe" - ], - "placeholder": "", - "weight": { - "$numberInt": "40" - }, - "instant_facet": false, - "required": false, - "public": true - }, - { - "field_number": "9.5", - "short_name": "Autonomy Level", - "long_name": "Autonomy Level", - "short_description": "Autonomy1: The system operates independently without simultaneous human oversight, interaction, or intervention.\n\nAutonomy2: The system operates independently but with human oversight, where a human can observe and override the system’s decisions in real time.\n\nAutonomy3: The system does not independently make decisions but instead provides information to a human who actively chooses to proceed with the AI’s information.", - "long_description": "Autonomy1: The system operates independently without simultaneous human oversight, interaction, or intervention.\n\nAutonomy2: The system operates independently but with human oversight, where a human can observe and override the system’s decisions in real time.\n\nAutonomy3: The system does not independently make decisions but instead provides information to a human who actively chooses to proceed with the AI’s information.", - "display_type": "enum", - "mongo_type": "string", - "default": "", - "placeholder": "", - "permitted_values": [ - "Autonomy1", - "Autonomy2", - "Autonomy3", - "unclear" - ], - "weight": { - "$numberInt": "40" - }, - "instant_facet": false, - "required": false, - "public": true - }, - { - "field_number": "9.8", - "short_name": "Notes (Information about AI System)", - "long_name": "Notes (Information about AI System)", - "short_description": "Input any notes that may help explain your answers.", - "long_description": "Input any notes that may help explain your answers.", - "display_type": "long_string", - "mongo_type": "string", - "default": "", - "placeholder": "Notes", - "permitted_values": [], - "weight": { - "$numberInt": "5" - }, - "instant_facet": false, - "required": false, - "public": true, - "hide_search": true - }, - { - "field_number": "10.1", - "short_name": "Intentional Harm", - "long_name": "Was the AI intentionally developed or deployed to perform the harm?", - "short_description": "Was the AI intentionally developed or deployed to perform the harm?\n\nIf yes, did the AI’s behavior result in unintended or intended harm? ", - "long_description": "Indicates if the system was designed to do harm. If it was designed to perform harm, the field will indicate if the AI system did or did not create unintended harm–i.e. was the reported harm the harm that AI was expected to perform or a different unexpected harm? ", - "display_type": "enum", - "mongo_type": "string", - "default": "unclear", - "placeholder": "", - "permitted_values": [ - "Yes. Intentionally designed to perform harm and did create intended harm", - "Yes. Intentionally designed to perform harm but created an unintended harm (a different harm may have occurred)", - "No. Not intentionally designed to perform harm", - "unclear" - ], - "weight": { - "$numberInt": "40" - }, - "instant_facet": false, - "required": false, - "public": true, - "notes": "Tracking and analyzing harm from AI systems designed to do harm is valuable and worthwhile. However, analysts may want to separately analyze harm from AI systems that were or were not designed to produce the observed harm." - }, - { - "field_number": "10.2", - "short_name": "Physical System Type", - "long_name": "Into what type of physical system was the AI integrated, if any?", - "short_description": "Describe the type of physical system that the AI was integrated into.", - "long_description": "Describe the type of physical system that the AI was integrated into. ", - "display_type": "string", - "mongo_type": "string", - "default": "", - "placeholder": "Physical System Type (e.g. trash sorting robot)", - "permitted_values": [], - "weight": { - "$numberInt": "5" - }, - "instant_facet": false, - "required": false, - "public": true - }, - { - "field_number": "10.3", - "short_name": "AI Task", - "long_name": "AI task or core application area", - "short_description": "Describe the AI’s application.", - "long_description": "Describe the AI’s application.\n\nIt is likely that the annotator will not have enough information to complete this field. If this occurs, enter unclear.\n\nThis is a freeform field. Some possible entries are\n\n- unclear\n- human language technologies\n- computer vision\n- robotics\n- automation and/or optimization\n- other\n\nThe application area of an AI is the high level task that the AI is intended to perform. It does not describe the technical methods by which the AI performs the task. Considering what an AI’s technical methods enable it to do is another way of arriving at what an AI’s application is. \n\nIt is possible for multiple application areas to be involved. When possible pick the principle or domain area, but it is ok to select multiple areas.", - "display_type": "list", - "mongo_type": "array", - "default": "", - "placeholder": "", - "permitted_values": [], - "weight": { - "$numberInt": "5" - }, - "instant_facet": false, - "required": false, - "public": true - }, - { - "field_number": "10.4", - "short_name": "AI tools and methods", - "long_name": "AI tools and methods", - "short_description": "Describe the tools and methods that enable the AI’s application.", - "long_description": "Describe the tools and methods that enable the AI’s application.\n\nIt is likely that the annotator will not have enough information to complete this field. If this occurs, enter unclear\n\nThis is a freeform field. Some possible entries are\n\n- unclear\n- reinforcement learning\n- neural networks\n- decision trees\n- bias mitigation\n- optimization\n- classifier\n- NLP/text analytics\n- continuous learning\n- unsupervised learning\n- supervised learning\n- clustering\n- prediction\n- rules\n- random forest\n\nAI tools and methods are the technical building blocks that enable the AI’s application.", - "display_type": "list", - "mongo_type": "array", - "default": "unclear", - "placeholder": "", - "permitted_values": [], - "weight": { - "$numberInt": "40" - }, - "instant_facet": false, - "required": false, - "public": true - }, - { - "field_number": "10.5", - "short_name": "Notes (AI Functionality and Techniques)", - "long_name": "Notes (AI Functionality and Techniques)", - "short_description": "Input any notes that may help explain your answers.", - "long_description": "Input any notes that may help explain your answers.", - "display_type": "long_string", - "mongo_type": "string", - "default": "", - "placeholder": "Notes", - "permitted_values": [], - "weight": { - "$numberInt": "5" - }, - "instant_facet": false, - "required": false, - "public": true, - "hide_search": true - } - ] -} \ No newline at end of file diff --git a/site/gatsby-site/cypress/fixtures/users/users.json b/site/gatsby-site/cypress/fixtures/users/users.json deleted file mode 100644 index 3c59427c3e..0000000000 --- a/site/gatsby-site/cypress/fixtures/users/users.json +++ /dev/null @@ -1,102 +0,0 @@ -{ - "data": { - "users": [ - { - "__typename": "User", - "adminData": { - "__typename": "UserAdminDatum", - "creationDate": "2022-09-26T20:34:46Z", - "disabled": false, - "email": "pablo@botsfactory.io", - "lastAuthenticationDate": "2023-06-06T00:07:30Z" - }, - "first_name": "Test", - "last_name": "User", - "roles": [ - "admin" - ], - "userId": "63320ce63ec803072c9f529c" - }, - { - "__typename": "User", - "adminData": { - "__typename": "UserAdminDatum", - "creationDate": "2022-09-27T00:07:21Z", - "disabled": false, - "email": "pablo+1@botsfactory.io", - "lastAuthenticationDate": "2022-09-27T17:34:37Z" - }, - "first_name": null, - "last_name": null, - "roles": [ - "subscriber" - ], - "userId": "63323eb94c44d44dee0887cd" - }, - { - "__typename": "User", - "adminData": { - "__typename": "UserAdminDatum", - "creationDate": "2022-09-27T17:00:26Z", - "disabled": false, - "email": "mail+1@cesarvarela.com", - "lastAuthenticationDate": "2022-09-27T17:00:36Z" - }, - "first_name": null, - "last_name": null, - "roles": [ - "subscriber" - ], - "userId": "63332c2ae42c6c12924ffc57" - }, - { - "__typename": "User", - "adminData": { - "__typename": "UserAdminDatum", - "creationDate": "2022-10-03T23:48:44Z", - "disabled": false, - "email": "pablo+2@botsfactory.io", - "lastAuthenticationDate": "2022-10-03T23:48:58Z" - }, - "first_name": "Cesar", - "last_name": "Varela", - "roles": [ - "subscriber" - ], - "userId": "633b74dc95edb45d34a95895" - }, - { - "__typename": "User", - "adminData": { - "__typename": "UserAdminDatum", - "creationDate": "2022-10-03T23:48:44Z", - "disabled": false, - "email": "sean@mcgregor.com", - "lastAuthenticationDate": "2022-10-03T23:48:58Z" - }, - "first_name": "Sean", - "last_name": "McGregor", - "roles": [ - "subscriber" - ], - "userId": "619b47ea5eed5334edfa3bbc" - }, - { - "__typename": "User", - "adminData": { - "__typename": "UserAdminDatum", - "creationDate": null, - "disabled": null, - "email": null, - "lastAuthenticationDate": null - }, - "first_name": null, - "last_name": null, - "roles": [ - "subscriber" - ], - "userId": "63e5d7a329e0d8b04a5092a6" - } - ] - } -} \ No newline at end of file diff --git a/site/gatsby-site/cypress/fixtures/variants/variantIncidents.json b/site/gatsby-site/cypress/fixtures/variants/variantIncidents.json deleted file mode 100644 index c43d061968..0000000000 --- a/site/gatsby-site/cypress/fixtures/variants/variantIncidents.json +++ /dev/null @@ -1,144 +0,0 @@ -{ - "data": { - "incidents": [ - { - "AllegedDeployerOfAISystem": [ - { - "entity_id": "youtube", - "name": "YouTube" - } - ], - "AllegedDeveloperOfAISystem": [ - { - "entity_id": "youtube", - "name": "YouTube" - } - ], - "AllegedHarmedOrNearlyHarmedParties": [ - { - "entity_id": "google", - "name": "Google" - } - ], - "__typename": "Incident", - "date": "2015-05-19", - "description": "YouTube’s content filtering and recommendation algorithms exposed children to disturbing and inappropriate videos.", - "incident_id": 112, - "nlp_similar_incidents": [], - "editors": [], - "editor_similar_incidents": [], - "editor_dissimilar_incidents": [], - "flagged_dissimilar_incidents": [], - "embedding": [], - "reports": [ - { - "__typename": "Report", - "report_number": 1 - }, - { - "__typename": "Report", - "report_number": 2 - }, - { - "__typename": "Report", - "report_number": 3 - }, - { - "__typename": "Report", - "report_number": 4 - }, - { - "__typename": "Report", - "report_number": 5 - }, - { - "__typename": "Report", - "report_number": 6 - }, - { - "__typename": "Report", - "report_number": 7 - }, - { - "__typename": "Report", - "report_number": 8 - }, - { - "__typename": "Report", - "report_number": 9 - }, - { - "__typename": "Report", - "report_number": 10 - }, - { - "__typename": "Report", - "report_number": 11 - }, - { - "__typename": "Report", - "report_number": 12 - }, - { - "__typename": "Report", - "report_number": 14 - }, - { - "__typename": "Report", - "report_number": 15 - }, - { - "__typename": "Report", - "report_number": 2310 - } - ], - "title": "YouTube Kids app is STILL showing disturbing videos" - }, - { - "AllegedDeployerOfAISystem": [ - { - "entity_id": "youtube", - "name": "YouTube" - } - ], - "AllegedDeveloperOfAISystem": [ - { - "entity_id": "youtube", - "name": "YouTube" - } - ], - "AllegedHarmedOrNearlyHarmedParties": [ - { - "entity_id": "google", - "name": "Google" - } - ], - "__typename": "Incident", - "date": "2022-12-15", - "description": "Kronos’s scheduling algorithm and managers’ use of its outputs allegedly negatively caused Starbucks employees’ financial and scheduling stability due to erratic schedules which disadvantaged wage workers.", - "incident_id": 10, - "nlp_similar_incidents": [], - "editors": [], - "editor_similar_incidents": [], - "editor_dissimilar_incidents": [], - "flagged_dissimilar_incidents": [], - "embedding": [], - "reports": [ - { - "__typename": "Report", - "report_number": 16 - }, - { - "__typename": "Report", - "report_number": 2311 - }, - { - "__typename": "Report", - "report_number": 2312 - } - ], - "title": "Kronos’s Algorithm Allegedly Created Scheduling and Financial Issues for Starbucks Employees" - } - ] - } -} diff --git a/site/gatsby-site/cypress/fixtures/variants/variants.json b/site/gatsby-site/cypress/fixtures/variants/variants.json deleted file mode 100644 index b75de5c3dd..0000000000 --- a/site/gatsby-site/cypress/fixtures/variants/variants.json +++ /dev/null @@ -1,39 +0,0 @@ -{ - "data": { - "reports": [ - { - "date_published": "2022-12-12", - "report_number": 2310, - "tags": ["variant:unreviewed"], - "text": "Example text", - "submitters": ["Pablo Costa"], - "inputs_outputs": [ - "**Test input text** _with markdown_", - "**Test output text** _with markdown_" - ], - "title": "Variant #2310" - }, - { - "date_published": "2022-12-12", - "report_number": 2311, - "tags": ["variant:approved"], - "text": "Example text", - "submitters": ["Pablo Costa"], - "inputs_outputs": ["Test input text without markdown", "Test output text without markdown"], - "title": "Variant #2311" - }, - { - "date_published": "2022-12-12", - "report_number": 2312, - "tags": ["variant:rejected"], - "text": "", - "submitters": ["Pablo Costa"], - "inputs_outputs": [ - "Test input text without markdown (rejected)", - "Test output text without markdown (rejected)" - ], - "title": "Variant #2312" - } - ] - } -} diff --git a/site/gatsby-site/cypress/fixtures/variants/variantsIncident.json b/site/gatsby-site/cypress/fixtures/variants/variantsIncident.json deleted file mode 100644 index 310bf55cc8..0000000000 --- a/site/gatsby-site/cypress/fixtures/variants/variantsIncident.json +++ /dev/null @@ -1,63 +0,0 @@ -{ - "data": { - "incident": { - "incident_id": 10, - "reports": [ - { - "date_published": "2022-12-12", - "report_number": 2310, - "tags": ["variant:unreviewed"], - "text": "", - "submitters": ["Pablo Costa"], - "inputs_outputs": [ - "**Test input text** _with markdown_", - "**Test output text** _with markdown_" - ], - "title": "Variant #2310" - }, - { - "date_published": "2022-12-12", - "report_number": 2311, - "tags": ["variant:approved"], - "text": "Example text", - "submitters": ["Pablo Costa"], - "inputs_outputs": [ - "Test input text without markdown", - "Test output text without markdown" - ], - "title": "Variant #2311" - }, - { - "date_published": "2022-12-12", - "report_number": 2312, - "tags": ["variant:rejected"], - "text": "", - "submitters": ["Pablo Costa"], - "inputs_outputs": [ - "Test input text without markdown (rejected)", - "Test output text without markdown (rejected)" - ], - "title": "Variant #2312" - }, - { - "date_published": "2021-04-23", - "report_number": 2316, - "tags": [], - "text": "", - "submitters": ["Pablo Costa"], - "inputs_outputs": [], - "title": "Court clears 39 post office operators convicted due to ‘corrupt data’" - }, - { - "date_published": "2015-06-03", - "report_number": 25, - "tags": [], - "text": "", - "submitters": ["Pablo Costa"], - "inputs_outputs": [], - "title": "After A Wave Of Bad Press, This Controversial Software Company Is Making Changes" - } - ] - } - } -} diff --git a/site/gatsby-site/cypress/plugins/index.js b/site/gatsby-site/cypress/plugins/index.js deleted file mode 100644 index d2fe083220..0000000000 --- a/site/gatsby-site/cypress/plugins/index.js +++ /dev/null @@ -1,53 +0,0 @@ -/// -// *********************************************************** -// This example plugins/index.js can be used to load plugins -// -// You can change the location of this file or turn off loading -// the plugins file with the 'pluginsFile' configuration option. -// -// You can read more here: -// https://on.cypress.io/plugins-guide -// *********************************************************** - -// This function is called when a project is opened or re-opened (e.g. due to -// the project's config changing) - -require('dotenv').config(); - -const fs = require('fs'); - -const path = require('path'); - -/** - * @type {Cypress.PluginConfig} - */ -module.exports = (on, config) => { - on('task', { - listFiles(directoryPath) { - return new Promise((resolve, reject) => { - fs.readdir(directoryPath, (err, files) => { - if (err) { - reject(err); - } else { - const jsonFiles = files.filter((file) => { - return ( - path.extname(file).toLowerCase() === '.json' && - fs.statSync(path.join(directoryPath, file)).isFile() - ); - }); - - resolve(jsonFiles); - } - }); - }); - }, - }); - - config.env.e2eUsername = process.env.E2E_ADMIN_USERNAME; - config.env.e2ePassword = process.env.E2E_ADMIN_PASSWORD; - config.env.realmAppId = process.env.GATSBY_REALM_APP_ID; - config.env.isEmptyEnvironment = process.env.IS_EMPTY_ENVIRONMENT == 'true'; - config.env.availableLanguages = process.env.GATSBY_AVAILABLE_LANGUAGES; - - return config; -}; diff --git a/site/gatsby-site/cypress/support/commands.js b/site/gatsby-site/cypress/support/commands.js deleted file mode 100644 index 90026154c1..0000000000 --- a/site/gatsby-site/cypress/support/commands.js +++ /dev/null @@ -1,83 +0,0 @@ -import { getApolloClient } from './utils'; -import { registerCommand } from 'cypress-wait-for-stable-dom'; -import deepEqualInAnyOrder from 'deep-equal-in-any-order'; - -chai.use(deepEqualInAnyOrder); - -registerCommand(); - -Cypress.Commands.add('disableSmoothScroll', () => { - return cy.document().then((document) => { - const node = document.createElement('style'); - - node.innerHTML = 'html { scroll-behavior: auto !important;}'; - - document.body.appendChild(node); - }); -}); - -const loginSteps = (email, password) => { - cy.visit('/login'); - - cy.get('input[name=email]').type(email); - - cy.get('input[name=password]').type(password); - - cy.get('[data-cy="login-btn"]').click(); - - return cy.location('pathname', { timeout: 8000 }).should('not.equal', '/login/'); -}; - -Cypress.Commands.add('login', (email, password, options = { skipSession: false }) => { - if (options.skipSession) { - return loginSteps(email, password); - } else { - cy.session([email, password], () => { - loginSteps(email, password); - }); - } -}); - -Cypress.Commands.add( - 'conditionalIntercept', - (url, condition, alias, response = null, options = { method: '*' }) => { - cy.intercept(url, options, (req) => { - if (condition(req)) { - req.alias = alias; - - if (response) { - req.reply(response); - } - } - }); - } -); - -Cypress.Commands.add('query', ({ query, variables, timeout = 30000 }) => { - const client = getApolloClient(); - - return cy.wrap(client.query({ query, variables }), { log: true, timeout }); -}); - -Cypress.Commands.add('clickOutside', () => { - return cy.get('body').click(0, 0); -}); - -Cypress.Commands.add('setEditorText', (value, selector = '.CodeMirror') => { - return cy - .get(selector) - .first() - .click() - .then((editor) => { - editor[0].CodeMirror.setValue(value); - }); -}); - -Cypress.Commands.add('getEditorText', (selector = '.CodeMirror') => { - return cy - .get(selector) - .first() - .then(({ 0: editor }) => { - return editor.CodeMirror.options.value; - }); -}); diff --git a/site/gatsby-site/cypress/support/e2e.js b/site/gatsby-site/cypress/support/e2e.js deleted file mode 100644 index 0b15c846ce..0000000000 --- a/site/gatsby-site/cypress/support/e2e.js +++ /dev/null @@ -1,39 +0,0 @@ -// *********************************************************** -// This example support/index.js is processed and -// loaded automatically before your test files. -// -// This is a great place to put global configuration and -// behavior that modifies Cypress. -// -// You can change the location of this file or turn off -// automatically serving support files with the -// 'supportFile' configuration option. -// -// You can read more here: -// https://on.cypress.io/configuration -// *********************************************************** - -import '@cypress/code-coverage/support'; - -// Import commands.js using ES2015 syntax: -import './commands'; - -// Alternatively you can use CommonJS syntax: -// require('./commands') - -const ignoredErrors = [ - // disable Realm error when origin is not whitelisted - 'origin forbidden (status 403 Forbidden)', - - // I have no Idea why this is happening, but it is. - 'Transport not open', - - // flakiness, flakiness everywhere: https://github.com/cypress-io/cypress/issues/8418 - 'ResizeObserver loop limit exceeded', -]; - -Cypress.on('uncaught:exception', (err) => { - if (ignoredErrors.some((e) => err.message.includes(e))) { - return false; - } -}); diff --git a/site/gatsby-site/cypress/support/index.d.ts b/site/gatsby-site/cypress/support/index.d.ts deleted file mode 100644 index 236429c83a..0000000000 --- a/site/gatsby-site/cypress/support/index.d.ts +++ /dev/null @@ -1,11 +0,0 @@ -declare namespace Cypress { - interface Chainable { - disableSmoothScroll(): Chainable; - login(email: string, password: string, options: { skipSession: boolean?} = {}): Chainable; - conditionalIntercept(url: string, condition: (req) => Boolean, alias: string, response: unknown?, options?: Omit) - clickOutside(): Chainable; - setEditorText(value: string, index?: string): Chainable; - getEditorText(index?: string): Chainable; - query({ query, variables, timeout = 30000 }): Chainable; - } -} \ No newline at end of file diff --git a/site/gatsby-site/cypress/support/utils.js b/site/gatsby-site/cypress/support/utils.js deleted file mode 100644 index 5a46ab01e8..0000000000 --- a/site/gatsby-site/cypress/support/utils.js +++ /dev/null @@ -1,35 +0,0 @@ -// Meanwhile there is not reproducible environment skip tests with admin permissions - -import { ApolloClient, HttpLink, InMemoryCache } from '@apollo/client'; - -export const maybeIt = Cypress.env('e2eUsername') && Cypress.env('e2ePassword') ? it : it.skip; - -export const conditionalIt = (condition, ...rest) => - condition ? it.call(null, ...rest) : it.skip.call(null, ...rest); - -conditionalIt.only = (condition, ...rest) => - condition ? it.only.call(null, ...rest) : it.skip.call(null, ...rest); - -conditionalIt.skip = (condition, ...rest) => it.skip.call(null, ...rest); - -export const getApolloClient = () => { - const email = Cypress.env('e2eUsername'); - - const password = Cypress.env('e2ePassword'); - - const client = new ApolloClient({ - link: new HttpLink({ - uri: `/api/graphql`, - - fetch: async (uri, options) => { - options.headers.email = email; - options.headers.password = password; - - return fetch(uri, options); - }, - }), - cache: new InMemoryCache(), - }); - - return client; -}; diff --git a/site/gatsby-site/cypress/tsconfig.json b/site/gatsby-site/cypress/tsconfig.json deleted file mode 100644 index 1924ef87dd..0000000000 --- a/site/gatsby-site/cypress/tsconfig.json +++ /dev/null @@ -1,15 +0,0 @@ -{ - "extends": "../tsconfig.json", - "compilerOptions": { - "noEmit": true, - // be explicit about types included - // to avoid clashing with Jest types - "types": [ - "cypress" - ] - }, - "include": [ - "../node_modules/cypress", - "./**/*.ts" - ] -} \ No newline at end of file diff --git a/site/gatsby-site/gatsby-config.js b/site/gatsby-site/gatsby-config.js index d3ac502d00..7d1f5ec68c 100755 --- a/site/gatsby-site/gatsby-config.js +++ b/site/gatsby-site/gatsby-config.js @@ -11,6 +11,12 @@ cloudinary.config({ cloud_name: config.cloudinary.cloudName }); const adapter = require('gatsby-adapter-netlify').default; +let googleTrackingIds = []; + +if (process.env.SITE_URL === config.gatsby.siteUrl) { + googleTrackingIds.push(config.gatsby.gaTrackingId); +} + const plugins = [ 'layout', { @@ -65,7 +71,7 @@ const plugins = [ { resolve: `gatsby-plugin-google-gtag`, options: { - trackingIds: [config.gatsby.gaTrackingId], + trackingIds: googleTrackingIds, }, }, { diff --git a/site/gatsby-site/gatsby-node.js b/site/gatsby-site/gatsby-node.js index 99fa3f5c34..7161cdbd68 100644 --- a/site/gatsby-site/gatsby-node.js +++ b/site/gatsby-site/gatsby-node.js @@ -4,8 +4,6 @@ const fs = require('fs'); const { Client: GoogleMapsAPIClient } = require('@googlemaps/google-maps-services-js'); -const { Translate } = require('@google-cloud/translate').v2; - const { startCase, differenceWith } = require('lodash'); const config = require('./config'); @@ -38,8 +36,6 @@ const createMissingTranslationsPage = require('./page-creators/createMissingTran const algoliasearch = require('algoliasearch'); -const Translator = require('./src/utils/Translator'); - const { MongoClient } = require('mongodb'); const { getLanguages } = require('./i18n'); @@ -286,10 +282,11 @@ exports.onPreBootstrap = async ({ reporter }) => { migrationsActivity.end(); } + // Algolia index update process if (process.env.CONTEXT === 'production') { - const translationsActivity = reporter.activityTimer(`Translations`); + const algoliaUpdaterActivity = reporter.activityTimer(`Algolia`); - translationsActivity.start(); + algoliaUpdaterActivity.start(); const configuredLanguages = getLanguages(); @@ -309,30 +306,15 @@ exports.onPreBootstrap = async ({ reporter }) => { if ( config.mongodb.translationsConnectionString && - config.i18n.translateApikey && config.i18n.availableLanguages && config.header.search.algoliaAdminKey && config.header.search.algoliaAppId ) { - if (process.env.TRANSLATE_DRY_RUN !== 'false') { - reporter.warn( - 'Please set `TRANSLATE_DRY_RUN=false` to disable dry running of translation process.' - ); - } - - translationsActivity.setStatus('Translating incident reports...'); - - const translateClient = new Translate({ key: config.i18n.translateApikey }); - const mongoClient = new MongoClient(config.mongodb.translationsConnectionString); const languages = getLanguages(); - const translator = new Translator({ mongoClient, translateClient, languages, reporter }); - - await translator.run(); - - translationsActivity.setStatus('Updating incidents indexes...'); + algoliaUpdaterActivity.setStatus('Updating Algolia incidents indexes...'); try { const algoliaClient = algoliasearch( @@ -352,12 +334,12 @@ exports.onPreBootstrap = async ({ reporter }) => { reporter.panicOnBuild('Error updating Algolia index:', e); } } else { - throw `Missing environment variable, can't run translation process.`; + throw `Missing environment variable, can't run Algolia update process.`; } - translationsActivity.end(); + algoliaUpdaterActivity.end(); } else { - reporter.warn('Netlify CONTEXT is not production, skipping translations.'); + reporter.warn('Netlify CONTEXT is not production, skipping Algolia index update process.'); } }; diff --git a/site/gatsby-site/i18n/locales/en/popovers.json b/site/gatsby-site/i18n/locales/en/popovers.json index 290daa0db3..86e60d335f 100644 --- a/site/gatsby-site/i18n/locales/en/popovers.json +++ b/site/gatsby-site/i18n/locales/en/popovers.json @@ -79,6 +79,10 @@ "title": "Quiet", "text": "Quiet reports are those that will not be published in the 'Latest Reports' section of the homepage. Quiet reports are used for reports that are useful for internal data but do not need to be promoted. If you are unsure, leave this field blank." }, + "implicated_systems": { + "title": "Implicated Systems", + "text": "The AI systems involved in the incident. If multiple, press “enter” to begin a new item." + }, "similarIncidentSearch": { "title": "Search for similar incidents", "text": "Search for a similar or dissimilar incident by typing an incident ID. If the resulting incident is similar click 'Yes', if it's dissimilar click 'No'. If you're not sure click 'Not sure'. Once selected, the incident will be added to the 'Manually-selected similar and dissimilar incidents' section of the form. Once added, if you want to remove it, click the 'Not Sure' button." diff --git a/site/gatsby-site/i18n/locales/es/entities.json b/site/gatsby-site/i18n/locales/es/entities.json index 313232db10..ad0e9a5f5e 100644 --- a/site/gatsby-site/i18n/locales/es/entities.json +++ b/site/gatsby-site/i18n/locales/es/entities.json @@ -20,7 +20,9 @@ "{{count}} Report_plural": "{{count}} Reportes", "Incidents involved as both Developer and Deployer": "Incidentes involucrados como desarrollador e implementador", "Incidents Harmed By": "Afectado por Incidentes", - "Alleged: <2> developed and deployed an AI system, which harmed <5>.": "Presunto: un sistema de IA desarrollado e implementado por <2>, perjudicó a <5>.", + "Alleged: <2> developed and deployed an AI system, which harmed <6>.": "Presunto: un sistema de IA desarrollado e implementado por <2>, perjudicó a <6>.", + "Implicated AI system:": "Sistema de IA implicado:", + "Implicated AI systems:": "Sistemas de IA implicados:", "Alleged: <1> developed an AI system deployed by <4>, which harmed <6>.": "Presunto: un sistema de IA desarrollado por <1> e implementado por <4>, perjudicó a <6>.", "Entities involved in AI Incidents": "^Entities involved in AI Incidents", "{{count}} Incident responses": "{{count}} respuestas de incidentes", diff --git a/site/gatsby-site/i18n/locales/es/popovers.json b/site/gatsby-site/i18n/locales/es/popovers.json index 106a717726..7a6f518847 100644 --- a/site/gatsby-site/i18n/locales/es/popovers.json +++ b/site/gatsby-site/i18n/locales/es/popovers.json @@ -79,6 +79,10 @@ "title": "¿Es un informe silencioso?", "text": "Los informes silenciosos son aquellos que no se publicarán en la sección 'Últimos informes' de la página de inicio. Los informes silenciosos se utilizan para informes que son útiles para datos internos pero que no necesitan promocionarse. Si no está seguro, deje este campo en blanco." }, + "implicated_systems": { + "title": "Sistemas implicados", + "text": "Los sistemas de IA involucrados en el incidente. Si son múltiples, presione “enter” después del elemento para ingresar uno nuevo." + }, "similarIncidentSearch": { "title": "Buscar incidentes similares", "text": "Busque un incidente similar o diferente escribiendo un ID de incidente. Si el incidente resultante es similar, haga clic en 'Sí', si es diferente, haga clic en 'No'. Si no está seguro, haga clic en 'No estoy seguro'. Una vez seleccionado, el incidente se agregará a la sección del formulario 'Incidentes similares y diferentes seleccionados manualmente'. Una vez agregado, si desea eliminarlo, haga clic en el botón “No estoy seguro”." diff --git a/site/gatsby-site/i18n/locales/es/submit.json b/site/gatsby-site/i18n/locales/es/submit.json index 8a0911172b..37289145f7 100644 --- a/site/gatsby-site/i18n/locales/es/submit.json +++ b/site/gatsby-site/i18n/locales/es/submit.json @@ -62,5 +62,7 @@ "Submit a new incident report to the Artificial Intelligence Incident Database": "Enviar un nuevo informe de incidente a la Base de Datos de Incidentes de Inteligencia Artificial", "Draft saved": "Borrador guardado", "Saving as draft...": "Guardando como borrador...", + "Implicated Systems": "Sistemas Implicados", + "What systems were involved in the incident?": "¿Qué sistemas estuvieron involucrados en el incidente?", "Please confirm you are ready to submit this report. Report details cannot be changed after submission.": "Por favor confirma que estás listo para enviar este informe. Los detalles del informe no se pueden cambiar después de la presentación." } diff --git a/site/gatsby-site/i18n/locales/es/translation.json b/site/gatsby-site/i18n/locales/es/translation.json index 9e760d0ad2..0ce17ac5d5 100644 --- a/site/gatsby-site/i18n/locales/es/translation.json +++ b/site/gatsby-site/i18n/locales/es/translation.json @@ -564,6 +564,7 @@ "Redirecting...": "Redirigiendo", "Random Incident": "Incidente aleatorio", "Risk Checklists": "Control de Riesgos", + "Implicated Systems": "Sistemas Implicados", "Manually-selected similar and dissimilar incidents": "Incidentes similares y disímiles seleccionados manualmente", "Search similar/dissimilar Incident Id": "Buscar incidente similar/disímil por ID", "Assigned similar/dissimilar incidents": "Incidentes similares/disímiles asignados", diff --git a/site/gatsby-site/i18n/locales/es/validation.json b/site/gatsby-site/i18n/locales/es/validation.json index 778cf63f7c..8b25ed4c09 100644 --- a/site/gatsby-site/i18n/locales/es/validation.json +++ b/site/gatsby-site/i18n/locales/es/validation.json @@ -34,5 +34,7 @@ "*Description is required": "*La descripción es obligatoria", "*Incident Date is required": "*Fecha del incidente es obligatoria", "*Incident Date required": "*Fecha del incidente es obligatoria", - "*Incident ID(s) must be a number": "*El ID del incidente debe ser un número" + "*Incident ID(s) must be a number": "*El ID del incidente debe ser un número", + "Implicated Systems must have at least 3 characters": "Los sistemas implicados deben tener al menos 3 caracteres", + "Implicated Systems can't be longer than 200 characters": "Los sistemas implicados no pueden tener más de 200 caracteres" } diff --git a/site/gatsby-site/i18n/locales/fr/entities.json b/site/gatsby-site/i18n/locales/fr/entities.json index 0ab08c7066..6eac29c7fc 100644 --- a/site/gatsby-site/i18n/locales/fr/entities.json +++ b/site/gatsby-site/i18n/locales/fr/entities.json @@ -27,5 +27,7 @@ "Editing Entity": "Modification de l'entité", "Back to Entity: {{name}}": "Retour à l'entité: {{name}}", "Entity updated successfully.": "Entité mise à jour avec succès.", - "Error updating Entity.": "Erreur lors de la mise à jour de l'entité." + "Error updating Entity.": "Erreur lors de la mise à jour de l'entité.", + "Implicated AI system:": "Système de l'IA impliqué:", + "Implicated AI systems:": "Systèmes de l'IA impliqués:" } diff --git a/site/gatsby-site/i18n/locales/fr/popovers.json b/site/gatsby-site/i18n/locales/fr/popovers.json index 99c4ea0da4..f76f03e07c 100644 --- a/site/gatsby-site/i18n/locales/fr/popovers.json +++ b/site/gatsby-site/i18n/locales/fr/popovers.json @@ -67,6 +67,10 @@ "title": "Est-ce un rapport silencieux ?", "text": "Les rapports silencieux sont ceux qui ne seront pas publiés dans la section « Derniers rapports » de la page d'accueil. Les rapports silencieux sont utilisés pour les rapports utiles pour les données internes mais qui n'ont pas besoin d'être promus. Si vous n'êtes pas sûr, laissez ce champ vide." }, + "implicated_systems": { + "title": "Systèmes d'IA impliqués dans l'incident", + "text": "Si plusieurs, appuyez sur « entrée » pour commencer un nouvel élément" + }, "similarIncidentSearch": { "title": "Rechercher des incidents similaires", "text": "Recherchez un incident similaire ou différent en saisissant un ID d'incident. Si l'incident qui en résulte est similaire, cliquez sur « Oui », s'il est différent, cliquez sur « Non ». Si vous n'êtes pas sûr, cliquez sur « Pas sûr ». Une fois sélectionné, l'incident sera ajouté à la section « Incidents similaires et différents sélectionnés manuellement » du formulaire. Une fois ajouté, si vous souhaitez le supprimer, cliquez sur le bouton « Pas sûr »." diff --git a/site/gatsby-site/i18n/locales/fr/submit.json b/site/gatsby-site/i18n/locales/fr/submit.json index ea9ab12166..dc101887be 100644 --- a/site/gatsby-site/i18n/locales/fr/submit.json +++ b/site/gatsby-site/i18n/locales/fr/submit.json @@ -59,5 +59,7 @@ "Submit a new incident report to the Artificial Intelligence Incident Database.": "Soumettre un nouveau rapport d'incident à la base de données des incidents d'intelligence artificielle.", "Draft saved": "Brouillon enregistré", "Saving as draft...": "Enregistrement en tant que brouillon...", + "Implicated Systems": "Systèmes impliqués", + "What systems were involved in the incident?": "Quels systèmes étaient impliqués dans l'incident ?", "Please confirm you are ready to submit this report. Report details cannot be changed after submission.": "Veuillez confirmer que vous êtes prêt à soumettre ce rapport. Les détails du rapport ne peuvent pas être modifiés après la soumission." } diff --git a/site/gatsby-site/i18n/locales/fr/translation.json b/site/gatsby-site/i18n/locales/fr/translation.json index 4d17ab37f5..65cf363a35 100644 --- a/site/gatsby-site/i18n/locales/fr/translation.json +++ b/site/gatsby-site/i18n/locales/fr/translation.json @@ -551,6 +551,8 @@ "Random Incident": "Incident au hasard", "Read the Source": "Lire la source", "Risk Checklists": "Contrôle des risques", + "Implicated Systems": "Systèmes impliqués", + "and": "et", "Manually-selected similar and dissimilar incidents": "Incidents similaires et dissemblables sélectionnés manuellement", "Search similar/dissimilar Incident Id": "Rechercher un incident similaire/dissimilaire", "Assigned similar/dissimilar incidents": "Incidents similaires/dissimilaires attribués", diff --git a/site/gatsby-site/i18n/locales/fr/validation.json b/site/gatsby-site/i18n/locales/fr/validation.json index 1701f0da92..75fb77e759 100644 --- a/site/gatsby-site/i18n/locales/fr/validation.json +++ b/site/gatsby-site/i18n/locales/fr/validation.json @@ -28,5 +28,7 @@ "*Description is required": "*La description est requise", "*Incident Date is required": "*Date de l'incident requise", "*Incident Date required": "*Date de l'incident requise", - "*Incident ID(s) must be a number": "*L'ID de l'incident doit être un nombre" + "*Incident ID(s) must be a number": "*L'ID de l'incident doit être un nombre", + "Implicated Systems must have at least 3 characters": "Les systèmes impliqués doivent comporter au moins 3 caractères", + "Implicated Systems can't be longer than 200 characters": "Les systèmes impliqués ne peuvent pas comporter plus de 200 caractères" } diff --git a/site/gatsby-site/i18n/locales/ja/entities.json b/site/gatsby-site/i18n/locales/ja/entities.json index 4f3e80aba2..462109f69a 100644 --- a/site/gatsby-site/i18n/locales/ja/entities.json +++ b/site/gatsby-site/i18n/locales/ja/entities.json @@ -28,5 +28,7 @@ "Editing Entity": "組織の編集", "Back to Entity: {{name}}": "組織に戻る: {{name}}", "Entity updated successfully.": "組織が正常に更新されました。", - "Error updating Entity.": "組織の更新中にエラーが発生しました。" + "Error updating Entity.": "組織の更新中にエラーが発生しました。", + "Implicated AI system:": "AI関連システム:", + "Implicated AI systems:": "AI関連システム:" } diff --git a/site/gatsby-site/i18n/locales/ja/popovers.json b/site/gatsby-site/i18n/locales/ja/popovers.json index 31a75349c9..bcfc303c7f 100644 --- a/site/gatsby-site/i18n/locales/ja/popovers.json +++ b/site/gatsby-site/i18n/locales/ja/popovers.json @@ -75,6 +75,10 @@ "title": "あなたです！", "text": "現在ログイン中なのでこの投稿は自動的にあなたのアカウントに関連づけられます。匿名で投稿するには、ブラウザウィンドウを匿名モードで開いて投稿してください。" }, + "implicated_systems": { + "title": "インシデントに関与したAIシステム", + "text": "複数ある場合は、新しい項目を入力するためにEnterキーを押してください" + }, "similarIncidentSearch": { "title": "類似の事件を検索", "text": "インシデント ID を入力して、類似または非類似のインシデントを検索します。結果のインシデントが類似している場合は「はい」をクリックし、非類似の場合は「いいえ」をクリックします。不明な場合は「不明」をクリックします。選択すると、インシデントはフォームの「手動で選択された類似および非類似のインシデント」セクションに追加されます。追加した後で削除したい場合は、「わからない」ボタンをクリックします。" diff --git a/site/gatsby-site/i18n/locales/ja/submit.json b/site/gatsby-site/i18n/locales/ja/submit.json index 1602798113..be5758583e 100644 --- a/site/gatsby-site/i18n/locales/ja/submit.json +++ b/site/gatsby-site/i18n/locales/ja/submit.json @@ -61,5 +61,7 @@ "Saving changes...": "変更を保存中...", "Draft saved": "下書きが保存されました", "Saving as draft...": "下書きとして保存中...", + "Implicated Systems": "関連システム", + "What systems were involved in the incident?": "このインシデントに関わるシステムは何ですか？", "Please confirm you are ready to submit this report. Report details cannot be changed after submission.": "このレポートを送信する準備ができていることを確認してください。送信後にレポートの詳細を変更することはできません" } diff --git a/site/gatsby-site/i18n/locales/ja/validation.json b/site/gatsby-site/i18n/locales/ja/validation.json index 9dcfd40b74..2f030128d5 100644 --- a/site/gatsby-site/i18n/locales/ja/validation.json +++ b/site/gatsby-site/i18n/locales/ja/validation.json @@ -13,5 +13,7 @@ "Please review report. Some data is missing.": "レポートを確認してください。足りない情報があります", "Please review submission. Some data is missing.": "投稿を確認してください。足りない情報があります", "Some data is missing.": "足りない情報があります", - "Please review. Some data is missing.": "確認してください。足りない情報があります" + "Please review. Some data is missing.": "確認してください。足りない情報があります", + "Implicated Systems must have at least 3 characters": "関連システムは3文字以上でなければいけません", + "Implicated Systems can't be longer than 200 characters": "関連システムは200文字を超えることはできません" } diff --git a/site/gatsby-site/migrations/2024.07.25T14.51.08.set-implicated-systems.js b/site/gatsby-site/migrations/2024.07.25T14.51.08.set-implicated-systems.js new file mode 100644 index 0000000000..c4437b2b9f --- /dev/null +++ b/site/gatsby-site/migrations/2024.07.25T14.51.08.set-implicated-systems.js @@ -0,0 +1,77 @@ +const config = require('../config'); +/** + * + * @param {{context: {client: import('mongodb').MongoClient}}} context + */ + +exports.up = async ({ context: { client } }) => { + await client.connect(); + + // New implicated_systems field on submissions and incidents collections from production db and history db + + const submissionsCollection = client + .db(config.realm.production_db.db_name) + .collection('submissions'); + + const submissionsHistoryCollection = client + .db(config.realm.production_db.db_history_name) + .collection('submissions'); + + const incidentsCollection = client.db(config.realm.production_db.db_name).collection('incidents'); + + const incidentsHistoryCollection = client + .db(config.realm.production_db.db_history_name) + .collection('incidents'); + + const submissionsUpdates = await submissionsCollection.updateMany( + {}, + { + $set: { + implicated_systems: [], + }, + } + ); + + console.log( + `Updated ${submissionsUpdates.modifiedCount} submissions with new implicated_systems field` + ); + + const submissionsHistoryUpdates = await submissionsHistoryCollection.updateMany( + {}, + { + $set: { + implicated_systems: [], + }, + } + ); + + console.log( + `Updated ${submissionsHistoryUpdates.modifiedCount} submissions history with new created_at field` + ); + + const incidentsUpdates = await incidentsCollection.updateMany( + {}, + { + $set: { + implicated_systems: [], + }, + } + ); + + console.log( + `Updated ${incidentsUpdates.modifiedCount} incidents with new implicated_systems field` + ); + + const incidentsHistoryUpdates = await incidentsHistoryCollection.updateMany( + {}, + { + $set: { + implicated_systems: [], + }, + } + ); + + console.log( + `Updated ${incidentsHistoryUpdates.modifiedCount} incidents history with new created_at field` + ); +}; diff --git a/site/gatsby-site/migrations/2024.11.13T19.53.42.delete-all-translation-placeholders.js b/site/gatsby-site/migrations/2024.11.13T19.53.42.delete-all-translation-placeholders.js new file mode 100644 index 0000000000..3c31d0ba36 --- /dev/null +++ b/site/gatsby-site/migrations/2024.11.13T19.53.42.delete-all-translation-placeholders.js @@ -0,0 +1,24 @@ +const languages = require('../i18n/config.json'); + +/** + * + * @param {{context: {client: import('mongodb').MongoClient}}} context + */ + +exports.up = async ({ context: { client } }) => { + const db = client.db('translations'); + + for (const language of languages) { + const name = `reports_${language.code}`; + + console.log(`Deleting dummy translations from ${name}`); + + const translations = db.collection(name); + + const result = await translations.deleteMany({ + text: /^translated-/, + }); + + console.log(`Deleted ${result.deletedCount} dummy translations for ${language.code}`); + } +}; diff --git a/site/gatsby-site/migrations/2024.11.26T19.41.18.notifications-processed.js b/site/gatsby-site/migrations/2024.11.26T19.41.18.notifications-processed.js new file mode 100644 index 0000000000..59a6e9b0b9 --- /dev/null +++ b/site/gatsby-site/migrations/2024.11.26T19.41.18.notifications-processed.js @@ -0,0 +1,16 @@ +const config = require('../config'); + +/** @type {import('umzug').MigrationFn} */ +exports.up = async ({ context: { client } }) => { + const db = client.db(config.realm.production_db.db_custom_data); + + const notifications = db.collection('notifications'); + + // Mark all pending notifications as processed + const result = await notifications.updateMany( + { processed: false }, + { $set: { processed: true } } + ); + + console.log(`All pending notifications marked as processed. Total: ${result.modifiedCount}`); +}; diff --git a/site/gatsby-site/package-lock.json b/site/gatsby-site/package-lock.json index d04fcad1d8..a989d6450f 100644 --- a/site/gatsby-site/package-lock.json +++ b/site/gatsby-site/package-lock.json @@ -77,6 +77,7 @@ "is-absolute-url": "^3.0.3", "json-bigint-patch": "^0.0.8", "json-diff-ts": "^1.2.6", + "jsonwebtoken": "^9.0.2", "mailersend": "^2.3.0", "md5": "^2.3.0", "minimatch": "^9.0.4", @@ -105,6 +106,7 @@ "react-zoom-pan-pinch": "^2.6.1", "realm-web": "^1.3.0", "rehype-raw": "^7.0.0", + "rehype-slug": "^6.0.0", "rehype-truncate": "^1.2.2", "remark": "^13.0.0", "remark-gfm": "^3.0.1", @@ -137,6 +139,7 @@ "@playwright/test": "^1.47.2", "@tailwindcss/typography": "^0.5.8", "@types/jest": "^29.5.12", + "@types/jsonwebtoken": "^9.0.7", "@types/node": "^20.12.13", "@types/pluralize": "^0.0.33", "@types/sinon": "^17.0.3", @@ -5904,6 +5907,17 @@ "version": "14.18.63", "license": "MIT" }, + "node_modules/@cloudinary/html/node_modules/typescript": { + "version": "4.9.5", + "license": "Apache-2.0", + "bin": { + "tsc": "bin/tsc", + "tsserver": "bin/tsserver" + }, + "engines": { + "node": ">=4.2.0" + } + }, "node_modules/@cloudinary/react": { "version": "1.11.2", "license": "MIT", @@ -5921,7 +5935,6 @@ "version": "1.5.0", "dev": true, "license": "MIT", - "optional": true, "engines": { "node": ">=0.1.90" } @@ -5947,14 +5960,14 @@ } }, "node_modules/@cypress/code-coverage": { - "version": "3.12.15", + "version": "3.13.7", "dev": true, "license": "MIT", "dependencies": { "@cypress/webpack-preprocessor": "^6.0.0", "chalk": "4.1.2", - "dayjs": "1.11.10", - "debug": "4.3.4", + "dayjs": "1.11.13", + "debug": "4.3.7", "execa": "4.1.0", "globby": "11.1.0", "istanbul-lib-coverage": "^3.0.0", @@ -6019,6 +6032,22 @@ "dev": true, "license": "MIT" }, + "node_modules/@cypress/code-coverage/node_modules/debug": { + "version": "4.3.7", + "dev": true, + "license": "MIT", + "dependencies": { + "ms": "^2.1.3" + }, + "engines": { + "node": ">=6.0" + }, + "peerDependenciesMeta": { + "supports-color": { + "optional": true + } + } + }, "node_modules/@cypress/code-coverage/node_modules/has-flag": { "version": "4.0.0", "dev": true, @@ -6038,6 +6067,11 @@ "js-yaml": "bin/js-yaml.js" } }, + "node_modules/@cypress/code-coverage/node_modules/ms": { + "version": "2.1.3", + "dev": true, + "license": "MIT" + }, "node_modules/@cypress/code-coverage/node_modules/supports-color": { "version": "7.2.0", "dev": true, @@ -6050,7 +6084,7 @@ } }, "node_modules/@cypress/request": { - "version": "3.0.0", + "version": "3.0.6", "dev": true, "license": "Apache-2.0", "dependencies": { @@ -6060,16 +6094,16 @@ "combined-stream": "~1.0.6", "extend": "~3.0.2", "forever-agent": "~0.6.1", - "form-data": "~2.3.2", - "http-signature": "~1.3.6", + "form-data": "~4.0.0", + "http-signature": "~1.4.0", "is-typedarray": "~1.0.0", "isstream": "~0.1.2", "json-stringify-safe": "~5.0.1", "mime-types": "~2.1.19", "performance-now": "^2.1.0", - "qs": "~6.10.3", + "qs": "6.13.0", "safe-buffer": "^5.1.2", - "tough-cookie": "^4.1.3", + "tough-cookie": "^5.0.0", "tunnel-agent": "^0.6.0", "uuid": "^8.3.2" }, @@ -6077,35 +6111,21 @@ "node": ">= 6" } }, - "node_modules/@cypress/request/node_modules/form-data": { - "version": "2.3.3", + "node_modules/@cypress/request/node_modules/http-signature": { + "version": "1.4.0", "dev": true, "license": "MIT", "dependencies": { - "asynckit": "^0.4.0", - "combined-stream": "^1.0.6", - "mime-types": "^2.1.12" - }, - "engines": { - "node": ">= 0.12" - } - }, - "node_modules/@cypress/request/node_modules/qs": { - "version": "6.10.4", - "dev": true, - "license": "BSD-3-Clause", - "dependencies": { - "side-channel": "^1.0.4" + "assert-plus": "^1.0.0", + "jsprim": "^2.0.2", + "sshpk": "^1.18.0" }, "engines": { - "node": ">=0.6" - }, - "funding": { - "url": "https://github.com/sponsors/ljharb" + "node": ">=0.10" } }, "node_modules/@cypress/webpack-preprocessor": { - "version": "6.0.1", + "version": "6.0.2", "dev": true, "license": "MIT", "dependencies": { @@ -11260,13 +11280,12 @@ } }, "node_modules/@parcel/watcher-android-arm64": { - "version": "2.4.0", - "resolved": "https://registry.npmjs.org/@parcel/watcher-android-arm64/-/watcher-android-arm64-2.4.0.tgz", - "integrity": "sha512-+fPtO/GsbYX1LJnCYCaDVT3EOBjvSFdQN9Mrzh9zWAOOfvidPWyScTrHIZHHfJBvlHzNA0Gy0U3NXFA/M7PHUA==", + "version": "2.4.1", + "resolved": "https://registry.npmjs.org/@parcel/watcher-android-arm64/-/watcher-android-arm64-2.4.1.tgz", + "integrity": "sha512-LOi/WTbbh3aTn2RYddrO8pnapixAziFl6SMxHM69r3tvdSm94JtCenaKgk1GRg5FJ5wpMCpHeW+7yqPlvZv7kg==", "cpu": [ "arm64" ], - "dev": true, "optional": true, "os": [ "android" @@ -11298,13 +11317,12 @@ } }, "node_modules/@parcel/watcher-darwin-x64": { - "version": "2.4.0", - "resolved": "https://registry.npmjs.org/@parcel/watcher-darwin-x64/-/watcher-darwin-x64-2.4.0.tgz", - "integrity": "sha512-vZMv9jl+szz5YLsSqEGCMSllBl1gU1snfbRL5ysJU03MEa6gkVy9OMcvXV1j4g0++jHEcvzhs3Z3LpeEbVmY6Q==", + "version": "2.4.1", + 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}, "node_modules/cachedir": { - "version": "2.3.0", + "version": "2.4.0", "dev": true, "license": "MIT", "engines": { @@ -16441,11 +16330,17 @@ } }, "node_modules/call-bind": { - "version": "1.0.2", + "version": "1.0.7", "license": "MIT", "dependencies": { - "function-bind": "^1.1.1", - "get-intrinsic": "^1.0.2" + "es-define-property": "^1.0.0", + "es-errors": "^1.3.0", + "function-bind": "^1.1.2", + "get-intrinsic": "^1.2.4", + "set-function-length": "^1.2.1" + }, + "engines": { + "node": ">= 0.4" }, "funding": { "url": "https://github.com/sponsors/ljharb" @@ -16867,7 +16762,7 @@ } }, "node_modules/cli-table3": { - "version": "0.6.3", + "version": "0.6.5", "dev": true, "license": "MIT", "dependencies": { @@ -17547,7 +17442,7 @@ "license": "MIT" }, "node_modules/cookies": { - "version": "0.8.0", + "version": "0.9.1", "dev": true, "license": "MIT", "dependencies": { @@ -18274,12 +18169,12 @@ } }, "node_modules/cypress": { - "version": "13.8.1", + "version": "13.16.0", "dev": true, "hasInstallScript": true, "license": "MIT", "dependencies": { - "@cypress/request": "^3.0.0", + "@cypress/request": "^3.0.6", "@cypress/xvfb": "^1.2.4", "@types/sinonjs__fake-timers": "8.1.1", "@types/sizzle": "^2.3.2", @@ -18290,6 +18185,7 @@ "cachedir": "^2.3.0", "chalk": "^4.1.0", "check-more-types": "^2.24.0", + "ci-info": "^4.0.0", "cli-cursor": "^3.1.0", "cli-table3": "~0.6.1", "commander": "^6.2.1", @@ -18304,7 +18200,6 @@ "figures": "^3.2.0", "fs-extra": "^9.1.0", "getos": "^3.2.1", - "is-ci": "^3.0.1", "is-installed-globally": "~0.4.0", "lazy-ass": "^1.6.0", "listr2": "^3.8.3", @@ -18318,7 +18213,8 @@ "request-progress": "^3.0.0", "semver": "^7.5.3", "supports-color": "^8.1.1", - "tmp": "~0.2.1", + "tmp": "~0.2.3", + "tree-kill": "1.2.2", "untildify": "^4.0.0", "yauzl": "^2.10.0" }, @@ -18374,6 +18270,20 @@ "node": ">=8" } }, + "node_modules/cypress/node_modules/ci-info": { + "version": "4.1.0", + "dev": true, + "funding": [ + { + "type": "github", + "url": "https://github.com/sponsors/sibiraj-s" + } + ], + "license": "MIT", + "engines": { + "node": ">=8" + } + }, "node_modules/cypress/node_modules/color-convert": { "version": "2.0.1", "dev": true, @@ -18412,24 +18322,10 @@ "node": ">=8" } }, - "node_modules/cypress/node_modules/lru-cache": { - "version": "6.0.0", - "dev": true, - "license": "ISC", - "dependencies": { - "yallist": "^4.0.0" - }, - "engines": { - "node": ">=10" - } - }, "node_modules/cypress/node_modules/semver": { - "version": "7.5.4", + "version": "7.6.3", "dev": true, "license": "ISC", - "dependencies": { - "lru-cache": "^6.0.0" - }, "bin": { "semver": "bin/semver.js" }, @@ -18451,11 +18347,6 @@ "url": "https://github.com/chalk/supports-color?sponsor=1" } }, - "node_modules/cypress/node_modules/yallist": { - "version": "4.0.0", - "dev": true, - "license": "ISC" - }, "node_modules/d": { "version": "1.0.1", "license": "ISC", @@ -18878,7 +18769,7 @@ } }, "node_modules/dayjs": { - "version": "1.11.10", + "version": "1.11.13", "dev": true, "license": "MIT" }, @@ -18934,6 +18825,28 @@ "node": ">=0.10" } }, + "node_modules/decompress-response": { + "version": "5.0.0", + "dev": true, + "license": "MIT", + "dependencies": { + "mimic-response": "^2.0.0" + }, + "engines": { + "node": ">=10" + } + }, + "node_modules/decompress-response/node_modules/mimic-response": { + "version": "2.1.0", + "dev": true, + "license": "MIT", + "engines": { + "node": ">=8" + }, + "funding": { + "url": "https://github.com/sponsors/sindresorhus" + } + }, "node_modules/dedent": { "version": "0.7.0", "dev": true, @@ -19039,6 +18952,21 @@ "node": ">=10" } }, + "node_modules/define-data-property": { + "version": "1.1.4", + "license": "MIT", + "dependencies": { + "es-define-property": "^1.0.0", + "es-errors": "^1.3.0", + "gopd": "^1.0.1" + }, + "engines": { + "node": ">= 0.4" + }, + "funding": { + "url": "https://github.com/sponsors/ljharb" + } + }, "node_modules/define-lazy-prop": { "version": "2.0.0", "license": "MIT", @@ -19757,6 +19685,23 @@ "url": "https://github.com/sponsors/ljharb" } }, + "node_modules/es-define-property": { + "version": "1.0.0", + "license": "MIT", + "dependencies": { + "get-intrinsic": "^1.2.4" + }, + "engines": { + "node": ">= 0.4" + } + }, + "node_modules/es-errors": { + "version": "1.3.0", + "license": "MIT", + "engines": { + "node": ">= 0.4" + } + }, "node_modules/es-get-iterator": { "version": "1.1.3", "license": "MIT", @@ -20028,16 +19973,30 @@ } }, "node_modules/eslint-plugin-cypress": { - "version": "2.12.1", + "version": "2.15.2", "dev": true, "license": "MIT", "dependencies": { - "globals": "^11.12.0" + "globals": "^13.20.0" }, "peerDependencies": { "eslint": ">= 3.2.1" } }, + "node_modules/eslint-plugin-cypress/node_modules/globals": { + "version": "13.24.0", + "dev": true, + "license": "MIT", + "dependencies": { + "type-fest": "^0.20.2" + }, + "engines": { + "node": ">=8" + }, + "funding": { + "url": "https://github.com/sponsors/sindresorhus" + } + }, "node_modules/eslint-plugin-flowtype": { "version": "5.10.0", "license": "BSD-3-Clause", @@ -20744,14 +20703,6 @@ "node": ">=4" } }, - "node_modules/executable/node_modules/pify": { - "version": "2.3.0", - "dev": true, - "license": "MIT", - "engines": { - "node": ">=0.10.0" - } - }, "node_modules/exit": { "version": "0.1.2", "dev": true, @@ -22357,14 +22308,6 @@ "url": "https://github.com/sindresorhus/got?sponsor=1" } }, - "node_modules/gatsby-core-utils/node_modules/http-cache-semantics": { - "version": "4.1.1", - "license": "BSD-2-Clause" - }, - "node_modules/gatsby-core-utils/node_modules/json-buffer": { - "version": "3.0.1", - "license": "MIT" - }, "node_modules/gatsby-core-utils/node_modules/keyv": { "version": "4.5.2", "license": "MIT", @@ -22372,13 +22315,6 @@ "json-buffer": "3.0.1" } }, - "node_modules/gatsby-core-utils/node_modules/lowercase-keys": { - "version": "2.0.0", - "license": "MIT", - "engines": { - "node": ">=8" - } - }, "node_modules/gatsby-core-utils/node_modules/mimic-response": { "version": "3.1.0", "license": "MIT", @@ -22389,33 +22325,6 @@ "url": "https://github.com/sponsors/sindresorhus" } }, - "node_modules/gatsby-core-utils/node_modules/normalize-url": { - "version": "6.1.0", - "license": "MIT", - "engines": { - "node": ">=10" - }, - "funding": { - "url": "https://github.com/sponsors/sindresorhus" - } - }, - "node_modules/gatsby-core-utils/node_modules/p-cancelable": { - "version": "2.1.1", - "license": "MIT", - "engines": { - "node": ">=8" - } - }, - "node_modules/gatsby-core-utils/node_modules/responselike": { - "version": "2.0.1", - "license": "MIT", - "dependencies": { - "lowercase-keys": "^2.0.0" - }, - "funding": { - "url": "https://github.com/sponsors/sindresorhus" - } - }, "node_modules/gatsby-graphiql-explorer": { "version": "3.13.1", "license": "MIT", @@ -24136,10 +24045,6 @@ "node": ">=8" } }, - "node_modules/gatsby/node_modules/http-cache-semantics": { - "version": "4.1.1", - "license": "BSD-2-Clause" - }, "node_modules/gatsby/node_modules/human-signals": { "version": "2.1.0", "license": "Apache-2.0", @@ -24174,10 +24079,6 @@ "url": "https://github.com/sponsors/sindresorhus" } }, - "node_modules/gatsby/node_modules/json-buffer": { - "version": "3.0.1", - "license": "MIT" - }, "node_modules/gatsby/node_modules/keyv": { "version": "4.5.2", "license": "MIT", @@ -24185,13 +24086,6 @@ "json-buffer": "3.0.1" } }, - "node_modules/gatsby/node_modules/lowercase-keys": { - "version": "2.0.0", - "license": "MIT", - "engines": { - "node": ">=8" - } - }, "node_modules/gatsby/node_modules/lru-cache": { "version": "6.0.0", "license": "ISC", @@ -24216,23 +24110,6 @@ "version": "2.0.0", "license": "MIT" }, - "node_modules/gatsby/node_modules/normalize-url": { - "version": "6.1.0", - "license": "MIT", - "engines": { - "node": ">=10" - }, - "funding": { - "url": "https://github.com/sponsors/sindresorhus" - } - }, - "node_modules/gatsby/node_modules/p-cancelable": { - "version": "2.1.1", - "license": "MIT", - "engines": { - "node": ">=8" - } - }, "node_modules/gatsby/node_modules/qs": { "version": "6.11.0", "license": "BSD-3-Clause", @@ -24275,16 +24152,6 @@ "node": ">= 0.8" } }, - "node_modules/gatsby/node_modules/responselike": { - "version": "2.0.1", - "license": "MIT", - "dependencies": { - "lowercase-keys": "^2.0.0" - }, - "funding": { - "url": "https://github.com/sponsors/sindresorhus" - } - }, "node_modules/gatsby/node_modules/semver": { "version": "7.5.4", "license": "ISC", @@ -24375,12 +24242,17 @@ } }, "node_modules/get-intrinsic": { - "version": "1.2.0", + "version": "1.2.4", "license": "MIT", "dependencies": { - "function-bind": "^1.1.1", - "has": "^1.0.3", - "has-symbols": "^1.0.3" + "es-errors": "^1.3.0", + "function-bind": "^1.1.2", + "has-proto": "^1.0.1", + "has-symbols": "^1.0.3", + "hasown": "^2.0.0" + }, + "engines": { + "node": ">= 0.4" }, "funding": { "url": "https://github.com/sponsors/ljharb" @@ -24695,6 +24567,82 @@ "url": "https://github.com/sponsors/ljharb" } }, + "node_modules/got": { + "version": "10.7.0", + "dev": true, + "license": "MIT", + "dependencies": { + "@sindresorhus/is": "^2.0.0", + "@szmarczak/http-timer": "^4.0.0", + "@types/cacheable-request": "^6.0.1", + "cacheable-lookup": "^2.0.0", + "cacheable-request": "^7.0.1", + "decompress-response": "^5.0.0", + "duplexer3": "^0.1.4", + "get-stream": "^5.0.0", + "lowercase-keys": "^2.0.0", + "mimic-response": "^2.1.0", + "p-cancelable": "^2.0.0", + "p-event": "^4.0.0", + "responselike": "^2.0.0", + "to-readable-stream": "^2.0.0", + "type-fest": "^0.10.0" + }, + "engines": { + "node": ">=10" + }, + "funding": { + "url": "https://github.com/sindresorhus/got?sponsor=1" + } + }, + "node_modules/got/node_modules/cacheable-lookup": { + "version": "2.0.1", + "dev": true, + "license": "MIT", + "dependencies": { + "@types/keyv": "^3.1.1", + "keyv": "^4.0.0" + }, + "engines": { + "node": ">=10" + } + }, + "node_modules/got/node_modules/get-stream": { + "version": "5.2.0", + "dev": true, + "license": "MIT", + "dependencies": { + "pump": "^3.0.0" + }, + "engines": { + "node": ">=8" + }, + "funding": { + "url": "https://github.com/sponsors/sindresorhus" + } + }, + "node_modules/got/node_modules/mimic-response": { + "version": "2.1.0", + "dev": true, + "license": "MIT", + "engines": { + "node": ">=8" + }, + "funding": { + "url": "https://github.com/sponsors/sindresorhus" + } + }, + "node_modules/got/node_modules/type-fest": { + "version": "0.10.0", + "dev": true, + "license": "(MIT OR CC0-1.0)", + "engines": { + "node": ">=8" + }, + "funding": { + "url": "https://github.com/sponsors/sindresorhus" + } + }, "node_modules/graceful-fs": { "version": "4.2.10", "license": "ISC" @@ -25120,10 +25068,10 @@ } }, "node_modules/has-property-descriptors": { - "version": "1.0.0", + "version": "1.0.2", "license": "MIT", "dependencies": { - "get-intrinsic": "^1.1.1" + "es-define-property": "^1.0.0" }, "funding": { "url": "https://github.com/sponsors/ljharb" @@ -25292,6 +25240,24 @@ "url": "https://opencollective.com/unified" } }, + "node_modules/hast-util-heading-rank": { + "version": "3.0.0", + "license": "MIT", + "dependencies": { + "@types/hast": "^3.0.0" + }, + "funding": { + "type": "opencollective", + "url": "https://opencollective.com/unified" + } + }, + "node_modules/hast-util-heading-rank/node_modules/@types/hast": { + "version": "3.0.4", + "license": "MIT", + "dependencies": { + "@types/unist": "*" + } + }, "node_modules/hast-util-parse-selector": { "version": "4.0.0", "license": "MIT", @@ -25946,6 +25912,10 @@ "node": ">= 0.6" } }, + "node_modules/http-cache-semantics": { + "version": "4.1.1", + "license": "BSD-2-Clause" + }, "node_modules/http-errors": { "version": "2.0.0", "license": "MIT", @@ -26659,31 +26629,6 @@ "url": "https://github.com/sponsors/ljharb" } }, - "node_modules/is-ci": { - "version": "3.0.1", - "dev": true, - "license": "MIT", - "dependencies": { - "ci-info": "^3.2.0" - }, - "bin": { - "is-ci": "bin.js" - } - }, - "node_modules/is-ci/node_modules/ci-info": { - "version": "3.8.0", - "dev": true, - "funding": [ - { - "type": "github", - "url": "https://github.com/sponsors/sibiraj-s" - } - ], - "license": "MIT", - "engines": { - "node": ">=8" - } - }, "node_modules/is-core-module": { "version": "2.15.0", "license": "MIT", @@ -27225,8 +27170,7 @@ }, "node_modules/isomorphic-unfetch": { "version": "3.1.0", - "resolved": "https://registry.npmjs.org/isomorphic-unfetch/-/isomorphic-unfetch-3.1.0.tgz", - "integrity": "sha512-geDJjpoZ8N0kWexiwkX8F9NkTsXhetLPVbZFQ+JTW239QNOwvB0gniuR1Wc6f0AMTn7/mFGyXvHTifrCp/GH8Q==", + "license": "MIT", "dependencies": { "node-fetch": "^2.6.1", "unfetch": "^4.2.0" @@ -29103,7 +29047,7 @@ } }, "node_modules/jose": { - "version": "5.4.0", + "version": "5.9.6", "dev": true, "license": "MIT", "funding": { @@ -29158,6 +29102,10 @@ "version": "0.0.8", "license": "MIT" }, + "node_modules/json-buffer": { + "version": "3.0.1", + "license": "MIT" + }, "node_modules/json-diff-ts": { "version": "1.2.6", "license": "MIT", @@ -29248,6 +29196,57 @@ "node": "*" } }, + "node_modules/jsonwebtoken": { + "version": "9.0.2", + "resolved": "https://registry.npmjs.org/jsonwebtoken/-/jsonwebtoken-9.0.2.tgz", + "integrity": "sha512-PRp66vJ865SSqOlgqS8hujT5U4AOgMfhrwYIuIhfKaoSCZcirrmASQr8CX7cUg+RMih+hgznrjp99o+W4pJLHQ==", + "dependencies": { + "jws": "^3.2.2", + "lodash.includes": "^4.3.0", + "lodash.isboolean": "^3.0.3", + "lodash.isinteger": "^4.0.4", + "lodash.isnumber": "^3.0.3", + "lodash.isplainobject": "^4.0.6", + "lodash.isstring": "^4.0.1", + "lodash.once": "^4.0.0", + "ms": "^2.1.1", + "semver": "^7.5.4" + }, + "engines": { + "node": ">=12", + "npm": ">=6" + } + }, + "node_modules/jsonwebtoken/node_modules/jwa": { + "version": "1.4.1", + "resolved": "https://registry.npmjs.org/jwa/-/jwa-1.4.1.tgz", + "integrity": "sha512-qiLX/xhEEFKUAJ6FiBMbes3w9ATzyk5W7Hvzpa/SLYdxNtng+gcurvrI7TbACjIXlsJyr05/S1oUhZrc63evQA==", + "dependencies": { + "buffer-equal-constant-time": "1.0.1", + "ecdsa-sig-formatter": "1.0.11", + "safe-buffer": "^5.0.1" + } + }, + "node_modules/jsonwebtoken/node_modules/jws": { + "version": "3.2.2", + "resolved": "https://registry.npmjs.org/jws/-/jws-3.2.2.tgz", + "integrity": "sha512-YHlZCB6lMTllWDtSPHz/ZXTsi8S00usEV6v1tjq8tOUZzw7DpSDWVXjXDre6ed1w/pd495ODpHZYSdkRTsa0HA==", + "dependencies": { + "jwa": "^1.4.1", + "safe-buffer": "^5.0.1" + } + }, + "node_modules/jsonwebtoken/node_modules/semver": { + "version": "7.6.3", + "resolved": "https://registry.npmjs.org/semver/-/semver-7.6.3.tgz", + "integrity": "sha512-oVekP1cKtI+CTDvHWYFUcMtsK/00wmAEfyqKfNdARm8u1wNVhSgaX7A8d4UuIlUI5e84iEwOhs7ZPYRmzU9U6A==", + "bin": { + "semver": "bin/semver.js" + }, + "engines": { + "node": ">=10" + } + }, "node_modules/jsprim": { "version": "2.0.2", "engines": [ @@ -29320,6 +29319,13 @@ "node": ">= 0.6" } }, + "node_modules/keyv": { + "version": "4.5.4", + "license": "MIT", + "dependencies": { + "json-buffer": "3.0.1" + } + }, "node_modules/kind-of": { "version": "6.0.3", "license": "MIT", @@ -29342,7 +29348,7 @@ } }, "node_modules/koa": { - "version": "2.14.1", + "version": "2.15.3", "dev": true, "license": "MIT", "dependencies": { @@ -29350,7 +29356,7 @@ "cache-content-type": "^1.0.0", "content-disposition": "~0.5.2", "content-type": "^1.0.4", - "cookies": "~0.8.0", + "cookies": "~0.9.0", "debug": "^4.3.2", "delegates": "^1.0.0", "depd": "^2.0.0", @@ -29375,12 +29381,13 @@ } }, "node_modules/koa-bodyparser": { - "version": "4.3.0", + "version": "4.4.1", "dev": true, "license": "MIT", "dependencies": { "co-body": "^6.0.0", - "copy-to": "^2.0.1" + "copy-to": "^2.0.1", + "type-is": "^1.6.18" }, "engines": { "node": ">=8.0.0" @@ -29488,7 +29495,7 @@ "license": "MIT" }, "node_modules/koa-route/node_modules/path-to-regexp": { - "version": "1.8.0", + "version": "1.9.0", "dev": true, "license": "MIT", "dependencies": { @@ -29993,6 +30000,11 @@ "version": "4.4.2", "license": "MIT" }, + "node_modules/lodash.includes": { + "version": "4.3.0", + "resolved": "https://registry.npmjs.org/lodash.includes/-/lodash.includes-4.3.0.tgz", + "integrity": "sha512-W3Bx6mdkRTGtlJISOvVD/lbqjTlPPUDTMnlXZFnVwi9NKJ6tiAk6LVdlhZMm17VZisqhKcgzpO5Wz91PCt5b0w==" + }, "node_modules/lodash.isarguments": { "version": "3.1.0", "dev": true, @@ -30002,9 +30014,18 @@ "version": "3.0.3", "license": "MIT" }, + "node_modules/lodash.isinteger": { + "version": "4.0.4", + "resolved": "https://registry.npmjs.org/lodash.isinteger/-/lodash.isinteger-4.0.4.tgz", + "integrity": "sha512-DBwtEWN2caHQ9/imiNeEA5ys1JoRtRfY3d7V9wkqtbycnAmTvRRmbHKDV4a0EYc678/dia0jrte4tjYwVBaZUA==" + }, + "node_modules/lodash.isnumber": { + "version": "3.0.3", + "resolved": "https://registry.npmjs.org/lodash.isnumber/-/lodash.isnumber-3.0.3.tgz", + "integrity": "sha512-QYqzpfwO3/CWf3XP+Z+tkQsfaLL/EnUlXWVkIk5FUPc4sBdTehEqZONuyRt2P67PXAk+NXmTBcc97zw9t1FQrw==" + }, "node_modules/lodash.isplainobject": { "version": "4.0.6", - "dev": true, "license": "MIT" }, "node_modules/lodash.isstring": { @@ -30033,7 +30054,6 @@ }, "node_modules/lodash.once": { "version": "4.1.1", - "dev": true, "license": "MIT" }, "node_modules/lodash.pick": { @@ -30278,6 +30298,13 @@ "tslib": "^2.0.3" } }, + "node_modules/lowercase-keys": { + "version": "2.0.0", + "license": "MIT", + "engines": { + "node": ">=8" + } + }, "node_modules/lru-cache": { "version": "5.1.1", "license": "ISC", @@ -30351,7 +30378,7 @@ } }, "node_modules/lws-blacklist/node_modules/path-to-regexp": { - "version": "6.2.1", + "version": "6.3.0", "dev": true, "license": "MIT" }, @@ -30483,7 +30510,7 @@ } }, "node_modules/lws-rewrite/node_modules/path-to-regexp": { - "version": "6.2.1", + "version": "6.3.0", "dev": true, "license": "MIT" }, @@ -30511,8 +30538,7 @@ }, "node_modules/mailersend": { "version": "2.3.0", - "resolved": "https://registry.npmjs.org/mailersend/-/mailersend-2.3.0.tgz", - "integrity": 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"node_modules/netlify-cli/node_modules/@vercel/nft": { "version": "0.27.2", "dev": true, @@ -36382,11 +36589,6 @@ "safe-buffer": "~5.2.0" } }, - "node_modules/netlify-cli/node_modules/archy": { - "version": "1.0.0", - "dev": true, - "license": "MIT" - }, "node_modules/netlify-cli/node_modules/are-we-there-yet": { "version": "2.0.0", "dev": true, @@ -36817,19 +37019,6 @@ "ieee754": "^1.1.13" } }, - "node_modules/netlify-cli/node_modules/buffer-crc32": { - "version": "0.2.13", - "dev": true, - "license": "MIT", - "engines": { - "node": "*" - } - }, - "node_modules/netlify-cli/node_modules/buffer-equal-constant-time": { - "version": "1.0.1", - "dev": true, - "license": "BSD-3-Clause" - }, "node_modules/netlify-cli/node_modules/buffer-from": { "version": "1.1.2", "dev": true, @@ -38094,11 +38283,6 @@ "dev": true, "license": "MIT" }, - "node_modules/netlify-cli/node_modules/delegates": { - "version": "1.0.0", - "dev": true, - "license": "MIT" - }, "node_modules/netlify-cli/node_modules/denque": { "version": "2.1.0", "dev": true, @@ -38421,14 +38605,6 @@ "dev": true, "license": "MIT" }, - "node_modules/netlify-cli/node_modules/ecdsa-sig-formatter": { - "version": "1.0.11", - "dev": true, - "license": "Apache-2.0", - "dependencies": { - "safe-buffer": "^5.0.1" - } - }, "node_modules/netlify-cli/node_modules/ee-first": { "version": "1.1.1", "dev": true, @@ -38857,39 +39033,6 @@ "node": ">=4" } }, - "node_modules/netlify-cli/node_modules/extract-zip": { - "version": "2.0.1", - "dev": true, - "license": "BSD-2-Clause", - "dependencies": { - "debug": "^4.1.1", - "get-stream": "^5.1.0", - "yauzl": "^2.10.0" - }, - "bin": { - "extract-zip": "cli.js" - }, - "engines": { - "node": ">= 10.17.0" - }, - "optionalDependencies": { - "@types/yauzl": "^2.9.1" - } - }, - "node_modules/netlify-cli/node_modules/extract-zip/node_modules/get-stream": { - "version": "5.2.0", - "dev": true, - "license": "MIT", - "dependencies": { - "pump": "^3.0.0" - }, - "engines": { - "node": ">=8" - }, - "funding": { - "url": "https://github.com/sponsors/sindresorhus" - } - }, "node_modules/netlify-cli/node_modules/fast-content-type-parse": { "version": "1.1.0", "dev": true, @@ -39200,14 +39343,6 @@ "reusify": "^1.0.4" } }, - "node_modules/netlify-cli/node_modules/fd-slicer": { - "version": "1.1.0", - "dev": true, - "license": "MIT", - "dependencies": { - "pend": "~1.2.0" - } - }, "node_modules/netlify-cli/node_modules/fdir": { "version": "6.0.1", "dev": true, @@ -40027,11 +40162,6 @@ "unix-dgram": "2.x" } }, - "node_modules/netlify-cli/node_modules/http-cache-semantics": { - "version": "4.1.1", - "dev": true, - "license": "BSD-2-Clause" - }, "node_modules/netlify-cli/node_modules/http-errors": { "version": "1.8.1", "dev": true, @@ -40762,43 +40892,6 @@ "url": "https://github.com/sponsors/sindresorhus" } }, - "node_modules/netlify-cli/node_modules/is-installed-globally": { - "version": "0.4.0", - "dev": true, - "license": "MIT", - "dependencies": { - "global-dirs": "^3.0.0", - "is-path-inside": "^3.0.2" - }, - "engines": { - "node": ">=10" - }, - "funding": { - "url": "https://github.com/sponsors/sindresorhus" - } - }, - "node_modules/netlify-cli/node_modules/is-installed-globally/node_modules/global-dirs": { - "version": "3.0.1", - "dev": true, - "license": "MIT", - "dependencies": { - "ini": "2.0.0" - }, - "engines": { - "node": ">=10" - }, - "funding": { - "url": "https://github.com/sponsors/sindresorhus" - } - }, - "node_modules/netlify-cli/node_modules/is-installed-globally/node_modules/ini": { - "version": "2.0.0", - "dev": true, - "license": "ISC", - "engines": { - "node": ">=10" - } - }, "node_modules/netlify-cli/node_modules/is-interactive": { "version": "2.0.0", "dev": true, @@ -40837,14 +40930,6 @@ "node": ">=8" } }, - "node_modules/netlify-cli/node_modules/is-path-inside": { - "version": "3.0.3", - "dev": true, - "license": "MIT", - "engines": { - "node": ">=8" - } - }, "node_modules/netlify-cli/node_modules/is-plain-obj": { "version": "4.1.0", "dev": true, @@ -41074,11 +41159,6 @@ "js-yaml": "bin/js-yaml.js" } }, - "node_modules/netlify-cli/node_modules/json-buffer": { - "version": "3.0.1", - "dev": true, - "license": "MIT" - }, "node_modules/netlify-cli/node_modules/json-parse-even-better-errors": { "version": "2.3.1", "dev": true, @@ -41111,27 +41191,6 @@ "node": ">=0.10.0" } }, - "node_modules/netlify-cli/node_modules/jsonwebtoken": { - "version": "9.0.2", - "dev": true, - "license": "MIT", - "dependencies": { - "jws": "^3.2.2", - "lodash.includes": "^4.3.0", - "lodash.isboolean": "^3.0.3", - "lodash.isinteger": "^4.0.4", - "lodash.isnumber": "^3.0.3", - "lodash.isplainobject": "^4.0.6", - "lodash.isstring": "^4.0.1", - "lodash.once": "^4.0.0", - "ms": "^2.1.1", - "semver": "^7.5.4" - }, - "engines": { - "node": ">=12", - "npm": ">=6" - } - }, "node_modules/netlify-cli/node_modules/junk": { "version": "4.0.1", "dev": true, @@ -41143,25 +41202,6 @@ "url": "https://github.com/sponsors/sindresorhus" } }, - "node_modules/netlify-cli/node_modules/jwa": { - "version": "1.4.1", - "dev": true, - "license": "MIT", - "dependencies": { - "buffer-equal-constant-time": "1.0.1", - "ecdsa-sig-formatter": "1.0.11", - "safe-buffer": "^5.0.1" - } - }, - "node_modules/netlify-cli/node_modules/jws": { - "version": "3.2.2", - "dev": true, - "license": "MIT", - "dependencies": { - "jwa": "^1.4.1", - "safe-buffer": "^5.0.1" - } - }, "node_modules/netlify-cli/node_modules/jwt-decode": { "version": "4.0.0", "dev": true, @@ -41181,14 +41221,6 @@ "node": ">=12.20.0" } }, - "node_modules/netlify-cli/node_modules/keyv": { - "version": "4.5.4", - "dev": true, - "license": "MIT", - "dependencies": { - "json-buffer": "3.0.1" - } - }, "node_modules/netlify-cli/node_modules/kind-of": { "version": "6.0.3", "dev": true, @@ -41546,51 +41578,21 @@ "dev": true, "license": "MIT" }, - "node_modules/netlify-cli/node_modules/lodash.includes": { - "version": "4.3.0", - "dev": true, - "license": "MIT" - }, "node_modules/netlify-cli/node_modules/lodash.isarguments": { "version": "3.1.0", "dev": true, "license": "MIT" }, - "node_modules/netlify-cli/node_modules/lodash.isboolean": { - "version": "3.0.3", - "dev": true, - "license": "MIT" - }, "node_modules/netlify-cli/node_modules/lodash.isempty": { "version": "4.4.0", "dev": true, "license": "MIT" }, - "node_modules/netlify-cli/node_modules/lodash.isinteger": { - "version": "4.0.4", - "dev": true, - "license": "MIT" - }, - "node_modules/netlify-cli/node_modules/lodash.isnumber": { - "version": "3.0.3", - "dev": true, - "license": "MIT" - }, "node_modules/netlify-cli/node_modules/lodash.isplainobject": { "version": "4.0.6", "dev": true, "license": "MIT" }, - "node_modules/netlify-cli/node_modules/lodash.isstring": { - "version": "4.0.1", - "dev": true, - "license": "MIT" - }, - "node_modules/netlify-cli/node_modules/lodash.once": { - "version": "4.1.1", - "dev": true, - "license": "MIT" - }, "node_modules/netlify-cli/node_modules/lodash.transform": { "version": "4.6.0", "dev": true, @@ -43207,11 +43209,6 @@ "url": "https://github.com/sponsors/Borewit" } }, - "node_modules/netlify-cli/node_modules/pend": { - "version": "1.2.0", - "dev": true, - "license": "MIT" - }, "node_modules/netlify-cli/node_modules/picocolors": { "version": "1.0.0", "dev": true, @@ -43424,14 +43421,6 @@ "prettyjson": "bin/prettyjson" } }, - "node_modules/netlify-cli/node_modules/process": { - "version": "0.11.10", - "dev": true, - "license": "MIT", - "engines": { - "node": ">= 0.6.0" - } - }, "node_modules/netlify-cli/node_modules/process-nextick-args": { "version": "2.0.1", "dev": true, @@ -45230,18 +45219,6 @@ "is-typedarray": "^1.0.0" } }, - "node_modules/netlify-cli/node_modules/typescript": { - "version": "5.1.6", - "dev": true, - "license": "Apache-2.0", - "bin": { - "tsc": "bin/tsc", - "tsserver": "bin/tsserver" - }, - "engines": { - "node": ">=14.17" - } - }, "node_modules/netlify-cli/node_modules/ufo": { "version": "1.4.0", "dev": true, @@ -45266,15 +45243,6 @@ "ulid": "bin/cli.js" } }, - "node_modules/netlify-cli/node_modules/unbzip2-stream": { - "version": "1.4.3", - "dev": true, - "license": "MIT", - "dependencies": { - "buffer": "^5.2.1", - "through": "^2.3.8" - } - }, "node_modules/netlify-cli/node_modules/uncrypto": { "version": "0.1.3", "dev": true, @@ -46006,15 +45974,6 @@ "node": ">=12" } }, - "node_modules/netlify-cli/node_modules/yauzl": { - "version": "2.10.0", - "dev": true, - "license": "MIT", - "dependencies": { - "buffer-crc32": "~0.2.3", - "fd-slicer": "~1.1.0" - } - }, "node_modules/netlify-cli/node_modules/yn": { "version": "3.1.1", "dev": true, @@ -46157,46 +46116,6 @@ "node": ">=10.18.1" } }, - "node_modules/netlify-plugin-cypress/node_modules/@sindresorhus/is": { - "version": "2.1.1", - "dev": true, - "license": "MIT", - "engines": { - "node": ">=10" - }, - "funding": { - "url": "https://github.com/sindresorhus/is?sponsor=1" - } - }, - "node_modules/netlify-plugin-cypress/node_modules/cacheable-lookup": { - "version": "2.0.1", - "dev": true, - "license": "MIT", - "dependencies": { - "@types/keyv": "^3.1.1", - "keyv": "^4.0.0" - }, - "engines": { - "node": ">=10" - } - }, - "node_modules/netlify-plugin-cypress/node_modules/cacheable-request": { - "version": "7.0.2", - "dev": true, - "license": "MIT", - "dependencies": { - "clone-response": "^1.0.2", - "get-stream": "^5.1.0", - "http-cache-semantics": "^4.0.0", - "keyv": "^4.0.0", - "lowercase-keys": "^2.0.0", - "normalize-url": "^6.0.1", - "responselike": "^2.0.0" - }, - "engines": { - "node": ">=8" - } - }, "node_modules/netlify-plugin-cypress/node_modules/common-tags": { "version": "1.8.0", "dev": true, @@ -46213,137 +46132,6 @@ "ms": "^2.1.1" } }, - "node_modules/netlify-plugin-cypress/node_modules/decompress-response": { - "version": "5.0.0", - "dev": true, - "license": "MIT", - "dependencies": { - "mimic-response": "^2.0.0" - }, - "engines": { - "node": ">=10" - } - }, - "node_modules/netlify-plugin-cypress/node_modules/get-stream": { - "version": "5.2.0", - "dev": true, - "license": "MIT", - "dependencies": { - "pump": "^3.0.0" - }, - "engines": { - "node": ">=8" - }, - "funding": { - "url": "https://github.com/sponsors/sindresorhus" - } - }, - "node_modules/netlify-plugin-cypress/node_modules/got": { - "version": "10.7.0", - "dev": true, - "license": "MIT", - "dependencies": { - "@sindresorhus/is": "^2.0.0", - "@szmarczak/http-timer": "^4.0.0", - "@types/cacheable-request": "^6.0.1", - "cacheable-lookup": "^2.0.0", - "cacheable-request": "^7.0.1", - "decompress-response": "^5.0.0", - "duplexer3": "^0.1.4", - "get-stream": "^5.0.0", - "lowercase-keys": "^2.0.0", - "mimic-response": "^2.1.0", - "p-cancelable": "^2.0.0", - "p-event": "^4.0.0", - "responselike": "^2.0.0", - "to-readable-stream": "^2.0.0", - "type-fest": "^0.10.0" - }, - "engines": { - "node": ">=10" - }, - "funding": { - "url": "https://github.com/sindresorhus/got?sponsor=1" - } - }, - "node_modules/netlify-plugin-cypress/node_modules/http-cache-semantics": { - "version": "4.1.1", - "dev": true, - "license": "BSD-2-Clause" - }, - "node_modules/netlify-plugin-cypress/node_modules/json-buffer": { - "version": "3.0.1", - "dev": true, - "license": "MIT" - }, - "node_modules/netlify-plugin-cypress/node_modules/keyv": { - "version": "4.5.2", - "dev": true, - "license": "MIT", - "dependencies": { - "json-buffer": "3.0.1" - } - }, - "node_modules/netlify-plugin-cypress/node_modules/lowercase-keys": { - "version": "2.0.0", - "dev": true, - "license": "MIT", - "engines": { - "node": ">=8" - } - }, - "node_modules/netlify-plugin-cypress/node_modules/mimic-response": { - "version": "2.1.0", - "dev": true, - "license": "MIT", - "engines": { - "node": ">=8" - }, - "funding": { - "url": "https://github.com/sponsors/sindresorhus" - } - }, - "node_modules/netlify-plugin-cypress/node_modules/normalize-url": { - "version": "6.1.0", - "dev": true, - "license": "MIT", - "engines": { - "node": ">=10" - }, - "funding": { - "url": "https://github.com/sponsors/sindresorhus" - } - }, - "node_modules/netlify-plugin-cypress/node_modules/p-cancelable": { - "version": "2.1.1", - "dev": true, - "license": "MIT", - "engines": { - "node": ">=8" - } - }, - "node_modules/netlify-plugin-cypress/node_modules/responselike": { - "version": "2.0.1", - "dev": true, - "license": "MIT", - "dependencies": { - "lowercase-keys": "^2.0.0" - }, - "funding": { - "url": "https://github.com/sponsors/sindresorhus" - } - }, - "node_modules/netlify-plugin-cypress/node_modules/type-fest": { - "version": "0.10.0", - "dev": true, - "license": "(MIT OR CC0-1.0)", - "engines": { - "node": ">=8" - }, - "funding": { - "url": "https://github.com/sponsors/sindresorhus" - } - }, "node_modules/netlify-redirect-parser": { "version": "14.3.0", "license": "MIT", @@ -46777,6 +46565,16 @@ "node": ">=0.10.0" } }, + "node_modules/normalize-url": { + "version": "6.1.0", + "license": "MIT", + "engines": { + "node": ">=10" + }, + "funding": { + "url": "https://github.com/sponsors/sindresorhus" + } + }, "node_modules/not": { "version": "0.1.0" }, @@ -46959,8 +46757,11 @@ } }, "node_modules/object-inspect": { - "version": "1.12.3", + "version": "1.13.3", "license": "MIT", + "engines": { + "node": ">= 0.4" + }, "funding": { "url": "https://github.com/sponsors/ljharb" } @@ -47325,6 +47126,13 @@ "dev": true, "license": "MIT" }, + "node_modules/p-cancelable": { + "version": "2.1.1", + "license": "MIT", + "engines": { + "node": ">=8" + } + }, "node_modules/p-defer": { "version": "3.0.0", "license": "MIT", @@ -47624,10 +47432,6 @@ "url": "https://github.com/sindresorhus/got?sponsor=1" } }, - "node_modules/package-json/node_modules/http-cache-semantics": { - "version": "4.1.1", - "license": "BSD-2-Clause" - }, "node_modules/package-json/node_modules/http2-wrapper": { "version": "2.2.0", "license": "MIT", @@ -47639,17 +47443,6 @@ "node": ">=10.19.0" } }, - "node_modules/package-json/node_modules/json-buffer": { - "version": "3.0.1", - "license": "MIT" - }, - "node_modules/package-json/node_modules/keyv": { - "version": "4.5.2", - "license": "MIT", - "dependencies": { - "json-buffer": "3.0.1" - } - }, "node_modules/package-json/node_modules/lowercase-keys": { "version": "3.0.0", "license": "MIT", @@ -48080,6 +47873,13 @@ "url": "https://github.com/sponsors/jonschlinkert" } }, + "node_modules/pify": { + "version": "2.3.0", + "license": "MIT", + "engines": { + "node": ">=0.10.0" + } + }, "node_modules/pirates": { "version": "4.0.5", "license": "MIT", @@ -48223,9 +48023,8 @@ }, "node_modules/playwright": { "version": "1.47.2", - "resolved": "https://registry.npmjs.org/playwright/-/playwright-1.47.2.tgz", - "integrity": "sha512-nx1cLMmQWqmA3UsnjaaokyoUpdVaaDhJhMoxX2qj3McpjnsqFHs516QAKYhqHAgOP+oCFTEOCOAaD1RgD/RQfA==", "dev": true, + "license": "Apache-2.0", "dependencies": { "playwright-core": "1.47.2" }, @@ -48241,9 +48040,8 @@ }, "node_modules/playwright-core": { "version": "1.47.2", - "resolved": "https://registry.npmjs.org/playwright-core/-/playwright-core-1.47.2.tgz", - "integrity": "sha512-3JvMfF+9LJfe16l7AbSmU555PaTl2tPyQsVInqm3id16pdDfvZ8TTZ/pyzmkbDrZTQefyzU7AIHlZqQnxpqHVQ==", "dev": true, + "license": "Apache-2.0", "bin": { "playwright-core": "cli.js" }, @@ -48734,16 +48532,6 @@ "postcss": "^8.2.15" } }, - "node_modules/postcss-normalize-url/node_modules/normalize-url": { - "version": "6.1.0", - "license": "MIT", - "engines": { - "node": ">=10" - }, - "funding": { - "url": "https://github.com/sponsors/sindresorhus" - } - }, "node_modules/postcss-normalize-whitespace": { "version": "5.1.1", "license": "MIT", @@ -48879,7 +48667,7 @@ } }, "node_modules/preact": { - "version": "10.18.1", + "version": "10.24.3", "license": "MIT", "funding": { "type": "opencollective", @@ -49056,7 +48844,7 @@ "license": "MIT" }, "node_modules/process-on-spawn": { - "version": "1.0.0", + "version": "1.1.0", "dev": true, "license": "MIT", "dependencies": { @@ -49295,10 +49083,10 @@ } }, "node_modules/qs": { - "version": "6.11.1", + "version": "6.13.0", "license": "BSD-3-Clause", "dependencies": { - "side-channel": "^1.0.4" + "side-channel": "^1.0.6" }, "engines": { "node": ">=0.6" @@ -50030,13 +49818,6 @@ "pify": "^2.3.0" } }, - "node_modules/read-cache/node_modules/pify": { - "version": "2.3.0", - "license": "MIT", - "engines": { - "node": ">=0.10.0" - } - }, "node_modules/read-pkg": { "version": "7.1.0", "license": "MIT", @@ -50493,6 +50274,79 @@ "url": "https://opencollective.com/unified" } }, + "node_modules/rehype-slug": { + "version": "6.0.0", + "license": "MIT", + "dependencies": { + "@types/hast": "^3.0.0", + "github-slugger": "^2.0.0", + "hast-util-heading-rank": "^3.0.0", + "hast-util-to-string": "^3.0.0", + "unist-util-visit": "^5.0.0" + }, + "funding": { + "type": "opencollective", + "url": "https://opencollective.com/unified" + } + }, + "node_modules/rehype-slug/node_modules/@types/hast": { + "version": "3.0.4", + "license": "MIT", + "dependencies": { + "@types/unist": "*" + } + }, + "node_modules/rehype-slug/node_modules/@types/unist": { + "version": "3.0.3", + "license": "MIT" + }, + "node_modules/rehype-slug/node_modules/hast-util-to-string": { + "version": "3.0.1", + "license": "MIT", + "dependencies": { + "@types/hast": "^3.0.0" + }, + "funding": { + "type": "opencollective", + "url": "https://opencollective.com/unified" + } + }, + "node_modules/rehype-slug/node_modules/unist-util-is": { + "version": "6.0.0", + "license": "MIT", + "dependencies": { + "@types/unist": "^3.0.0" + }, + "funding": { + "type": "opencollective", + "url": "https://opencollective.com/unified" + } + }, + "node_modules/rehype-slug/node_modules/unist-util-visit": { + "version": "5.0.0", + "license": "MIT", + "dependencies": { + "@types/unist": "^3.0.0", + "unist-util-is": "^6.0.0", + "unist-util-visit-parents": "^6.0.0" + }, + "funding": { + "type": "opencollective", + "url": "https://opencollective.com/unified" + } + }, + "node_modules/rehype-slug/node_modules/unist-util-visit-parents": { + "version": "6.0.1", + "license": "MIT", + "dependencies": { + "@types/unist": "^3.0.0", + "unist-util-is": "^6.0.0" + }, + "funding": { + "type": "opencollective", + "url": "https://opencollective.com/unified" + } + }, "node_modules/rehype-stringify": { "version": "9.0.3", "license": "MIT", @@ -51453,6 +51307,16 @@ "node": ">=0.8" } }, + "node_modules/responselike": { + "version": "2.0.1", + "license": "MIT", + "dependencies": { + "lowercase-keys": "^2.0.0" + }, + "funding": { + "url": "https://github.com/sponsors/sindresorhus" + } + }, "node_modules/restore-cursor": { "version": "3.1.0", "license": "MIT", @@ -51949,6 +51813,21 @@ "version": "2.0.0", "license": "ISC" }, + "node_modules/set-function-length": { + "version": "1.2.2", + "license": "MIT", + "dependencies": { + "define-data-property": "^1.1.4", + "es-errors": "^1.3.0", + "function-bind": "^1.1.2", + "get-intrinsic": "^1.2.4", + "gopd": "^1.0.1", + "has-property-descriptors": "^1.0.2" + }, + "engines": { + "node": ">= 0.4" + } + }, "node_modules/setimmediate": { "version": "1.0.5", "license": "MIT" @@ -52084,12 +51963,16 @@ } }, "node_modules/side-channel": { - "version": "1.0.4", + "version": "1.0.6", "license": "MIT", "dependencies": { - "call-bind": "^1.0.0", - "get-intrinsic": "^1.0.2", - "object-inspect": "^1.9.0" + "call-bind": "^1.0.7", + "es-errors": "^1.3.0", + "get-intrinsic": "^1.2.4", + "object-inspect": "^1.13.1" + }, + "engines": { + "node": ">= 0.4" }, "funding": { "url": "https://github.com/sponsors/ljharb" @@ -52526,7 +52409,7 @@ "license": "BSD-3-Clause" }, "node_modules/sshpk": { - "version": "1.17.0", + "version": "1.18.0", "license": "MIT", "dependencies": { "asn1": "~0.2.3", @@ -53947,9 +53830,12 @@ } }, "node_modules/throttleit": { - "version": "1.0.0", + "version": "1.0.1", "dev": true, - "license": "MIT" + "license": "MIT", + "funding": { + "url": "https://github.com/sponsors/sindresorhus" + } }, "node_modules/through": { "version": "2.3.8", @@ -54020,14 +53906,27 @@ "tslib": "^2.0.3" } }, - "node_modules/tmp": { - "version": "0.2.1", + "node_modules/tldts": { + "version": "6.1.61", + "dev": true, "license": "MIT", "dependencies": { - "rimraf": "^3.0.0" + "tldts-core": "^6.1.61" }, + "bin": { + "tldts": "bin/cli.js" + } + }, + "node_modules/tldts-core": { + "version": "6.1.61", + "dev": true, + "license": "MIT" + }, + "node_modules/tmp": { + "version": "0.2.3", + "license": "MIT", "engines": { - "node": ">=8.17.0" + "node": ">=14.14" } }, "node_modules/tmpl": { @@ -54107,25 +54006,14 @@ "license": "MIT" }, "node_modules/tough-cookie": { - "version": "4.1.3", + "version": "5.0.0", "dev": true, "license": "BSD-3-Clause", "dependencies": { - "psl": "^1.1.33", - "punycode": "^2.1.1", - "universalify": "^0.2.0", - "url-parse": "^1.5.3" + "tldts": "^6.1.32" }, "engines": { - "node": ">=6" - } - }, - "node_modules/tough-cookie/node_modules/universalify": { - "version": "0.2.0", - "dev": true, - "license": "MIT", - "engines": { - "node": ">= 4.0.0" + "node": ">=16" } }, "node_modules/tr46": { @@ -54143,6 +54031,14 @@ "license": "MIT", "optional": true }, + "node_modules/tree-kill": { + "version": "1.2.2", + "dev": true, + "license": "MIT", + "bin": { + "tree-kill": "cli.js" + } + }, "node_modules/trim-lines": { "version": "3.0.1", "license": "MIT", @@ -54465,14 +54361,14 @@ } }, "node_modules/typescript": { - "version": "4.9.5", + "version": "5.6.3", "license": "Apache-2.0", "bin": { "tsc": "bin/tsc", "tsserver": "bin/tsserver" }, "engines": { - "node": ">=4.2.0" + "node": ">=14.17" } }, "node_modules/typical": { @@ -54682,8 +54578,7 @@ }, "node_modules/unfetch": { "version": "4.2.0", - "resolved": "https://registry.npmjs.org/unfetch/-/unfetch-4.2.0.tgz", - "integrity": "sha512-F9p7yYCn6cIW9El1zi0HI6vqpeIvBsr3dSuRO6Xuppb1u5rXpCPmMvLSyECLhybr9isec8Ohl0hPekMVrEinDA==" + "license": "MIT" }, "node_modules/unicode-canonical-property-names-ecmascript": { "version": "2.0.0", @@ -56057,7 +55952,7 @@ } }, "node_modules/ylru": { - "version": "1.3.2", + "version": "1.4.0", "dev": true, "license": "MIT", "engines": { diff --git a/site/gatsby-site/package.json b/site/gatsby-site/package.json index 61f9a360f1..80e8cf9db6 100644 --- a/site/gatsby-site/package.json +++ b/site/gatsby-site/package.json @@ -73,6 +73,7 @@ "is-absolute-url": "^3.0.3", "json-bigint-patch": "^0.0.8", "json-diff-ts": "^1.2.6", + "jsonwebtoken": "^9.0.2", "mailersend": "^2.3.0", "md5": "^2.3.0", "minimatch": "^9.0.4", @@ -101,6 +102,7 @@ "react-zoom-pan-pinch": "^2.6.1", "realm-web": "^1.3.0", "rehype-raw": "^7.0.0", + "rehype-slug": "^6.0.0", "rehype-truncate": "^1.2.2", "remark": "^13.0.0", "remark-gfm": "^3.0.1", @@ -129,19 +131,20 @@ "format": "prettier --write \"{src,page-creators,pages,constants}/**/*.{js,jsx,css,json}\"", "lint": "eslint --fix \"{src,page-creators,pages,constants}/**/*.{js,jsx}\"", "serve": "netlify dev --target-port 9000 --port 8000 -d public --offline", - "cypress:open": "cypress open", - "cypress:run": "cypress run", - "test:e2e": "start-server-and-test start http://localhost:8000 cypress:open", + "test:e2e": "npx playwright test playwright/e2e-full --ui", "test:e2e:ci": "DEBUG=pw:webserver npx playwright test $TEST_FOLDER --shard=$SHARD_INDEX/$SHARD_TOTAL", "test:api": "jest --setupFiles dotenv/config --runInBand --forceExit", "test:api:ci": "jest --runInBand --forceExit", "codegen": "graphql-codegen --config codegen.ts", "start:api": "node --env-file=.env --watch -r ts-node/register server/index.ts", - "start:memory-mongo": "node --env-file=.env -r ts-node/register playwright/memory-mongo.ts" + "start:memory-mongo": "node --env-file=.env -r ts-node/register playwright/memory-mongo.ts", + "start:memory-mongo:ci": "node -r ts-node/register playwright/memory-mongo.ts", + "process-notifications": "node --env-file=.env -r ts-node/register src/scripts/process-notifications.ts", + "process-notifications:ci": "node -r ts-node/register src/scripts/process-notifications.ts", + "translate-reports": "node src/scripts/translateReports.js" }, "devDependencies": { "@babel/plugin-proposal-export-default-from": "^7.23.3", - "@cypress/code-coverage": "^3.12.15", "@graphql-codegen/cli": "^5.0.2", "@graphql-codegen/client-preset": "^4.3.3", "@graphql-codegen/typescript": "^4.0.6", @@ -151,6 +154,7 @@ "@playwright/test": "^1.47.2", "@tailwindcss/typography": "^0.5.8", "@types/jest": "^29.5.12", + "@types/jsonwebtoken": "^9.0.7", "@types/node": "^20.12.13", "@types/pluralize": "^0.0.33", "@types/sinon": "^17.0.3", @@ -158,12 +162,9 @@ "autoprefixer": "^10.4.7", "babel-eslint": "^10.1.0", "babel-plugin-istanbul": "^6.1.1", - "cypress": "^13.8.1", - "cypress-wait-for-stable-dom": "^0.1.0", "eslint": "^7.17.0", "eslint-config-prettier": "^7.1.0", "eslint-import-resolver-alias": "^1.1.2", - "eslint-plugin-cypress": "^2.12.1", "eslint-plugin-import": "^2.22.1", "eslint-plugin-jsx-a11y": "^6.4.1", "eslint-plugin-prettier": "^3.3.1", @@ -174,7 +175,6 @@ "lint-staged": "^10.5.3", "mongodb-memory-server": "^9.1.8", "netlify-cli": "^17.36.0", - "netlify-plugin-cypress": "^2.2.1", "postcss": "^8.4.31", "prettier": "^2.2.1", "prism-react-renderer": "^1.2.0", diff --git a/site/gatsby-site/page-creators/createEntitiesPages.js b/site/gatsby-site/page-creators/createEntitiesPages.js index f4f34e782f..c570cd2be4 100644 --- a/site/gatsby-site/page-creators/createEntitiesPages.js +++ b/site/gatsby-site/page-creators/createEntitiesPages.js @@ -14,6 +14,7 @@ const createEntitiesPages = async (graphql, createPage) => { Alleged_deployer_of_AI_system Alleged_developer_of_AI_system Alleged_harmed_or_nearly_harmed_parties + implicated_systems reports { report_number } @@ -55,6 +56,7 @@ const createEntitiesPages = async (graphql, createPage) => { incidentsAsDeveloper: entity.incidentsAsDeveloper, incidentsAsBoth: entity.incidentsAsBoth, incidentsHarmedBy: entity.incidentsHarmedBy, + incidentsImplicatedSystems: entity.incidentsImplicatedSystems, relatedEntities: entity.relatedEntities, responses: entity.responses, }, diff --git a/site/gatsby-site/playwright/config.ts b/site/gatsby-site/playwright/config.ts index 2440f27287..730f9eeaab 100644 --- a/site/gatsby-site/playwright/config.ts +++ b/site/gatsby-site/playwright/config.ts @@ -4,6 +4,7 @@ type ConfigType = { IS_EMPTY_ENVIRONMENT: string; AVAILABLE_LANGUAGES?: string; [key: string]: string; + SITE_URL?: string; }; const config: ConfigType = { @@ -11,6 +12,7 @@ const config: ConfigType = { E2E_ADMIN_USERNAME: process.env.E2E_ADMIN_USERNAME!, IS_EMPTY_ENVIRONMENT: process.env.IS_EMPTY_ENVIRONMENT ?? '', AVAILABLE_LANGUAGES: process.env.GATSBY_AVAILABLE_LANGUAGES ?? '', + SITE_URL: process.env.SITE_URL ?? '', } Object.keys(config).forEach((key) => { diff --git a/site/gatsby-site/playwright/e2e-full/account.spec.ts b/site/gatsby-site/playwright/e2e-full/account.spec.ts index a4d734c64f..26efd66db5 100644 --- a/site/gatsby-site/playwright/e2e-full/account.spec.ts +++ b/site/gatsby-site/playwright/e2e-full/account.spec.ts @@ -15,23 +15,20 @@ test.describe('Account', () => { await page.goto(url); - const detailsTable = page.locator('[data-cy="details-table"]'); - - await expect(detailsTable.locator(`td:text-is("${config.E2E_ADMIN_USERNAME}")`)).toBeVisible(); - await expect(detailsTable.locator('td:text-is("Test")')).toBeVisible(); - await expect(detailsTable.locator('td:text-is("User")')).toBeVisible(); - await expect(detailsTable.locator('span:text-is("admin")')).toBeVisible(); + await expect(page.locator('[data-cy="user-email"]').locator(`td:text-is("${config.E2E_ADMIN_USERNAME}")`)).toBeVisible(); + await expect(page.locator('[data-cy="user-first-name"]').locator('td:text-is("Test")')).toBeVisible(); + await expect(page.locator('[data-cy="user-last-name"]').locator('td:text-is("User")')).toBeVisible(); + await expect(page.locator('[data-cy="user-role"]').locator('span:text-is("admin")')).toBeVisible(); await expect(page.locator('a:text-is("Log out")')).toBeVisible(); }); - test('Should allow editing user data', async ({ page, login }) => { + test('Should allow editing user role data (admin user)', async ({ page, login }) => { await login(config.E2E_ADMIN_USERNAME, config.E2E_ADMIN_PASSWORD, { customData: { roles: ['admin'], first_name: 'Test', last_name: 'User' } }); await page.goto(url); - await page.locator('button:has-text("Edit")').click(); const editUserModal = page.getByTestId('edit-user-modal'); @@ -52,4 +49,41 @@ test.describe('Account', () => { await expect(page.getByTestId('edit-user-modal')).toBeVisible(); }); + + test('Should allow editing their own first and last name (subscriber user)', async ({ page, login }) => { + + await login(config.E2E_ADMIN_USERNAME, config.E2E_ADMIN_PASSWORD, { customData: { roles: ['subscriber'], first_name: 'Test', last_name: 'User' } }); + + await page.goto(url); + + await page.locator('button:has-text("Edit")').click(); + + const editUserModal = page.getByTestId('edit-user-modal'); + + await editUserModal.locator('[id="first_name"]').fill('New first name'); + await editUserModal.locator('[id="last_name"]').fill('New last name'); + await editUserModal.locator('button:has-text("Submit")').click(); + + await expect(editUserModal).not.toBeVisible(); + + await expect(page.locator('[data-cy="toast"]')).toContainText('User updated.', { timeout: 60000 }); + + await expect(page.locator('[data-cy="user-first-name"]').locator('td:text-is("New first name")')).toBeVisible(); + await expect(page.locator('[data-cy="user-last-name"]').locator('td:text-is("New last name")')).toBeVisible(); + }); + + test('Should not allow to edit roles (subscriber user)', async ({ page, login }) => { + + await login(config.E2E_ADMIN_USERNAME, config.E2E_ADMIN_PASSWORD, { customData: { roles: ['subscriber'], first_name: 'Test', last_name: 'User' } }); + + await page.goto(url); + + await page.locator('button:has-text("Edit")').click(); + + const editUserModal = page.getByTestId('edit-user-modal'); + + const rolesInput = editUserModal.locator('[id="roles"]'); + + await expect(rolesInput).toBeDisabled({ timeout: 3000 }); + }); }); \ No newline at end of file diff --git a/site/gatsby-site/playwright/e2e-full/apps/incidents.spec.ts b/site/gatsby-site/playwright/e2e-full/apps/incidents.spec.ts index a5635d7e82..90ba9bb04a 100644 --- a/site/gatsby-site/playwright/e2e-full/apps/incidents.spec.ts +++ b/site/gatsby-site/playwright/e2e-full/apps/incidents.spec.ts @@ -27,20 +27,6 @@ test.describe('Incidents App', () => { await login(process.env.E2E_ADMIN_USERNAME, process.env.E2E_ADMIN_PASSWORD, { customData: { first_name: 'John', last_name: 'Doe', roles: ['admin'] } }); - await conditionalIntercept( - page, - '**/graphql', - (req) => req.postDataJSON().operationName === 'logIncidentHistory', - { - data: { - logIncidentHistory: { - incident_id: 3, - }, - }, - }, - 'logIncidentHistory' - ); - await page.goto(url); await page.waitForSelector('[data-testid="flowbite-toggleswitch-toggle"]'); @@ -63,8 +49,6 @@ test.describe('Incidents App', () => { await page.getByText('Update', { exact: true }).click(); - await waitForRequest('logIncidentHistory'); - await expect(page.locator('[data-cy="toast"]').locator('text=Incident 3 updated successfully.')).toBeVisible(); const result = await query({ @@ -82,26 +66,75 @@ test.describe('Incidents App', () => { }); }); + test('Entities should link to entities page', async ({ page }) => { + + await init(); + await page.goto(url); + + await page.waitForSelector('[data-testid="flowbite-toggleswitch-toggle"]'); + await page.locator('[data-testid="flowbite-toggleswitch-toggle"]').click(); + + const { data: { incidents } } = await query({ + query: gql`{ + incidents { + incident_id + title + description + date + AllegedDeployerOfAISystem { + entity_id + name + } + AllegedDeveloperOfAISystem { + entity_id + name + } + AllegedHarmedOrNearlyHarmedParties { + entity_id + name + } + implicated_systems { + entity_id + name + } + } + }` + }); + + const rowLocator = page.locator('[data-cy="row"]').first(); + const firstIncident = incidents[0]; + + for (const [index, deployer] of firstIncident.AllegedDeployerOfAISystem.entries()) { + + const link = await rowLocator.locator('[data-cy="cell"]').nth(4).locator('[data-cy="cell-entity-link"]').nth(index).getAttribute('href'); + expect(link).toContain(`/entities/${deployer.entity_id}`); + } + + for (const [index, developer] of firstIncident.AllegedDeveloperOfAISystem.entries()) { + + const link = await rowLocator.locator('[data-cy="cell"]').nth(5).locator('[data-cy="cell-entity-link"]').nth(index).getAttribute('href'); + expect(link).toContain(`/entities/${developer.entity_id}`); + } + + for (const [index, harmed] of firstIncident.AllegedHarmedOrNearlyHarmedParties.entries()) { + + const link = await rowLocator.locator('[data-cy="cell"]').nth(6).locator('[data-cy="cell-entity-link"]').nth(index).getAttribute('href'); + expect(link).toContain(`/entities/${harmed.entity_id}`); + } + + for (const [index, implicated_system] of firstIncident.implicated_systems.entries()) { + + const link = await rowLocator.locator('[data-cy="cell"]').nth(7).locator('[data-cy="cell-entity-link"]').nth(index).getAttribute('href'); + expect(link).toContain(`/entities/${implicated_system.entity_id}`); + } + }); + test('Successfully assigns similar/dissimilar incidents to incident 3', async ({ page, login }) => { await init(); await login(process.env.E2E_ADMIN_USERNAME, process.env.E2E_ADMIN_PASSWORD, { customData: { first_name: 'John', last_name: 'Doe', roles: ['incident_editor'] } }); - await conditionalIntercept( - page, - '**/graphql', - (req) => req.postDataJSON().operationName === 'logIncidentHistory', - { - data: { - logIncidentHistory: { - incident_id: 3, - }, - }, - }, - 'logIncidentHistory' - ); - await page.goto(url); await page.waitForSelector('[data-testid="flowbite-toggleswitch-toggle"]'); @@ -132,8 +165,6 @@ test.describe('Incidents App', () => { await page.getByText('Update', { exact: true }).click(); - await waitForRequest('logIncidentHistory'); - await expect(page.locator('[data-cy="toast"]').locator('text=Incident 3 updated successfully.')).toBeVisible(); const result = await query({ @@ -155,56 +186,6 @@ test.describe('Incidents App', () => { }); }); - test('Entities should link to entities page', async ({ page }) => { - - await page.goto(url); - - await page.waitForSelector('[data-testid="flowbite-toggleswitch-toggle"]'); - await page.locator('[data-testid="flowbite-toggleswitch-toggle"]').click(); - - const incidents = await query({ - query: gql`{ - incidents { - title - description - editor_dissimilar_incidents - editor_similar_incidents - AllegedDeployerOfAISystem { - entity_id - } - AllegedDeveloperOfAISystem { - entity_id - } - AllegedHarmedOrNearlyHarmedParties { - entity_id - } - } - }` - }); - - const firstIncident = incidents.data.incidents[0]; - const firstRow = page.locator('[data-cy="row"]').first(); - - // AllegedDeployerOfAISystem - for (const [index, deployer] of firstIncident.AllegedDeployerOfAISystem.entries()) { - const link = await firstRow.locator('[data-cy=cell]').nth(4).locator('[data-cy="cell-entity-link"]').nth(index).getAttribute('href'); - expect(link).toContain(`/entities/${deployer.entity_id}/`); - } - - // AllegedDeveloperOfAISystem - for (const [index, developer] of firstIncident.AllegedDeveloperOfAISystem.entries()) { - const link = await firstRow.locator('[data-cy=cell]').nth(5).locator('[data-cy="cell-entity-link"]').nth(index).getAttribute('href'); - expect(link).toContain(`/entities/${developer.entity_id}/`); - } - - // AllegedHarmedOrNearlyHarmedParties - for (const [index, harmed] of firstIncident.AllegedHarmedOrNearlyHarmedParties.entries()) { - const link = await firstRow.locator('[data-cy=cell]').nth(6).locator('[data-cy="cell-entity-link"]').nth(index).getAttribute('href'); - expect(link).toContain(`/entities/${harmed.entity_id}/`); - } - - }); - test('Should display a list of live incidents', async ({ page }) => { await init({ @@ -231,6 +212,9 @@ test.describe('Incidents App', () => { AllegedHarmedOrNearlyHarmedParties { name } + implicated_systems { + name + } } }` }); @@ -243,17 +227,19 @@ test.describe('Incidents App', () => { const rowLocator = page.locator('[data-cy="row"]').first(); const firstIncident = incidents[0]; - await expect(rowLocator.locator('[data-cy="cell"]')).toHaveCount(7, { timeout: 10000 }); + await expect(rowLocator.locator('[data-cy="cell"]')).toHaveCount(8); await expect(rowLocator.locator('[data-cy="cell"]').nth(0)).toHaveText(`Incident ${firstIncident.incident_id}`); await expect(rowLocator.locator('[data-cy="cell"]').nth(1)).toHaveText(firstIncident.title); await expect(rowLocator.locator('[data-cy="cell"]').nth(2)).toHaveText(firstIncident.description); - await expect(rowLocator.locator('[data-cy="cell"]').nth(3)).toHaveText(firstIncident.date); + // TODO: fix these + await expect(rowLocator.locator('[data-cy="cell"]').nth(3)).toHaveText(firstIncident.date); await expect(rowLocator.locator('[data-cy="cell"]').nth(4)).toHaveText(firstIncident.AllegedDeployerOfAISystem.map((i: any) => i.name).join(', ')); await expect(rowLocator.locator('[data-cy="cell"]').nth(5)).toHaveText(firstIncident.AllegedDeveloperOfAISystem.map((i: any) => i.name).join(', ')); await expect(rowLocator.locator('[data-cy="cell"]').nth(6)).toHaveText(firstIncident.AllegedHarmedOrNearlyHarmedParties.map((i: any) => i.name).join(', ')); + await expect(rowLocator.locator('[data-cy="cell"]').nth(7)).toHaveText(firstIncident.implicated_systems.map((i: any) => i.name).join(', ')); }); test('Should navigate to the last page, and the first page', async ({ page }) => { @@ -284,7 +270,7 @@ test.describe('Incidents App', () => { await page.getByText('Reports', { exact: true }).click(); await page.waitForSelector('[data-cy="row"]'); - await expect(page.locator('[data-cy="row"]')).toHaveCount(3); + await expect(page.locator('[data-cy="row"]')).toHaveCount(4); const firstCiteLink = await page.locator('[data-cy="row"] td a').first().getAttribute('href'); expect(firstCiteLink).toMatch(/^\/cite\/\d+#r\d+$/); diff --git a/site/gatsby-site/playwright/e2e-full/apps/newsdigest.spec.ts b/site/gatsby-site/playwright/e2e-full/apps/newsdigest.spec.ts new file mode 100644 index 0000000000..8d5f070b85 --- /dev/null +++ b/site/gatsby-site/playwright/e2e-full/apps/newsdigest.spec.ts @@ -0,0 +1,107 @@ +import { expect } from '@playwright/test'; +import { test, query } from '../../utils'; +import { gql } from '@apollo/client'; +import { init } from '../../memory-mongo'; +import config from '../../config'; + +test.describe('News Digest', () => { + const url = '/apps/newsdigest'; + + test('Successfully loads', async ({ page }) => { + await page.goto(url); + }); + + test('Should load candidate cards', async ({ page }) => { + await init(); + + await page.goto(url); + await page.locator('[data-cy="candidate-card"]').first().waitFor(); + }); + + test('Should open submit form on pressing submit', async ({ page }) => { + await init(); + + await page.goto(url); + await page.exposeFunction('open', (url: string) => { + expect(url.slice(0, 12)).toBe('/apps/submit'); + }); + + await page.locator('[data-cy="candidate-dropdown"] button').first().click(); + await page.locator('[data-cy="submit-icon"]').first().click(); + }); + + test('Should dismiss and restore items', async ({ page, login, skipOnEmptyEnvironment }) => { + + const userId = await login(config.E2E_ADMIN_USERNAME, config.E2E_ADMIN_PASSWORD); + await init({ customData: { users: [{ userId, first_name: 'Test', last_name: 'User', roles: ['admin'] }] } }, { drop: true }); + + await page.goto(url); + + const dataId = await page.locator('[data-cy="results"] [data-cy="candidate-card"] [data-cy="candidate-dropdown"]').first().evaluate(el => el.closest('[data-id]').getAttribute('data-id')); + + await page.locator(`[data-id="${dataId}"] [data-cy="candidate-dropdown"]`).click(); + await page.locator(`[data-id="${dataId}"] [data-cy="dismiss-icon"]`).click(); + + await expect(page.locator(`[data-cy="results"] [data-id="${dataId}"]`)).toHaveCount(0); + + await expect(page.locator('[data-cy="toast"]')).toContainText(`Dismissed article:`); + + const response = await query( + { + query: gql` + query NewsArticles($filter: CandidateFilterType!) { + candidates(filter: $filter) { + title + url + similarity + matching_keywords + matching_harm_keywords + matching_entities + date_published + dismissed + } + } + `, + variables: { + filter: { + dismissed: { EQ: true }, + }, + }, + }, + ); + await expect(response.data.candidates).toHaveLength(1); + await page.locator(`[data-cy="dismissed-summary"]`).click(); + await page.locator(`[data-id="${dataId}"] [data-cy="candidate-dropdown"]`).click(); + await page.locator(`[data-id="${dataId}"] [data-cy="restore-icon"]`).click(); + + await expect(page.locator(`[data-cy="dismissed"] [data-id="${dataId}"]`)).toHaveCount(0); + await expect(page.locator(`[data-cy="results"] [data-id="${dataId}"]`)).toHaveCount(1); + await expect(page.locator('[data-cy="toast"]').filter({ hasText: 'Restored article:' })).toContainText(`Restored article:`); + + const response2 = await query( + { + query: gql` + query NewsArticles($filter: CandidateFilterType!) { + candidates(filter: $filter) { + title + url + similarity + matching_keywords + matching_harm_keywords + matching_entities + date_published + dismissed + } + } + `, + variables: { + filter: { + dismissed: { EQ: true }, + }, + }, + }, + ); + + await expect(response2.data.candidates).toHaveLength(0); + }); +}); diff --git a/site/gatsby-site/playwright/e2e-full/cite.spec.ts b/site/gatsby-site/playwright/e2e-full/cite.spec.ts index 0b1f8f922e..807a95372a 100644 --- a/site/gatsby-site/playwright/e2e-full/cite.spec.ts +++ b/site/gatsby-site/playwright/e2e-full/cite.spec.ts @@ -1,10 +1,10 @@ -import { waitForRequest, query, mockDate, test, conditionalIntercept, fillAutoComplete } from '../utils'; +import { query, mockDate, test, fillAutoComplete } from '../utils'; import { format } from 'date-fns'; import { gql } from '@apollo/client'; import { expect } from '@playwright/test'; import config from '../config'; import { init } from '../memory-mongo'; -import { DBIncident } from '../seeds/aiidprod/incidents'; +import { DBIncident } from '../../server/interfaces'; test.describe('Cite pages', () => { const discoverUrl = '/apps/discover'; @@ -108,20 +108,6 @@ test.describe('Cite pages', () => { await init(); - await conditionalIntercept( - page, - '**/graphql', - (req) => req.postDataJSON().operationName === 'logReportHistory', - { - data: { - logReportHistory: { - report_number: 3, - }, - }, - }, - 'logReportHistory' - ); - await page.goto(url + '#' + _id); await page.click(`[id="r${_id}"] [data-cy="expand-report-button"]`); @@ -132,8 +118,6 @@ test.describe('Cite pages', () => { await modal.locator('[data-cy="flag-toggle"]').click(); - await waitForRequest('logReportHistory'); - await expect(modal.locator('[data-cy="flag-toggle"]')).toBeDisabled(); await page.click('[aria-label="Close"]'); @@ -281,6 +265,7 @@ test.describe('Cite pages', () => { await page.goto(url); const date = format(new Date(), 'MMMMd,y'); + const retrievedDate = format(new Date(), 'MMMMyyyy') await page.locator('button:has-text("Citation Info")').click(); @@ -290,8 +275,8 @@ test.describe('Cite pages', () => { const bibText = bibTextElement.replace(/(\r\n|\n|\r| |\s)/g, ''); expect(bibText).toBe( - `@article{aiid:3,author={Olsson,Catherine},editor={McGregor,Sean},journal={AIIncidentDatabase},publisher={ResponsibleAICollaborative},title={IncidentNumber3},url={https://incidentdatabase.ai/cite/3},year={2014},urldate={${date}}}` - ); + `@article{aiid:3,author={Olsson,Catherine},editor={McGregor,Sean},journal={AIIncidentDatabase},publisher={ResponsibleAICollaborative},title={IncidentNumber3:KronosSchedulingAlgorithmAllegedlyCausedFinancialIssuesforStarbucksEmployees},url={https://incidentdatabase.ai/cite/3},year={2014},urldate={${date}},note={Retrieved${retrievedDate}from\\url{https://incidentdatabase.ai/cite/3}}}` + ); }); test('Should display similar incidents', async ({ page }) => { @@ -355,28 +340,11 @@ test.describe('Cite pages', () => { await init(); - await conditionalIntercept( - page, - '**/graphql', - (req) => req.postDataJSON().operationName === 'logIncidentHistory', - { - data: { - logIncidentHistory: { - incident_id: 3, - }, - }, - }, - 'logIncidentHistory' - ); - - const now = new Date('March 14 2042 13:37:11'); - await mockDate(page, now); - await page.goto('/cite/3'); await page.locator('[data-cy="similar-incidents-column"] [data-cy="flag-similar-incident"]').first().click(); - await waitForRequest('logIncidentHistory'); + await expect(page.getByText('Incident flagged successfully. Our editors will remove it from this list if it not relevant.')).toBeVisible(); const { data } = await query({ query: gql`{incident(filter: { incident_id: { EQ: 3 } }) { @@ -395,31 +363,13 @@ test.describe('Cite pages', () => { await login(config.E2E_ADMIN_USERNAME, config.E2E_ADMIN_PASSWORD, { customData: { first_name: 'Test', last_name: 'User', roles: ['admin'] } }); - await conditionalIntercept( - page, - '**/graphql', - (req) => req.postDataJSON().operationName === 'logIncidentHistory', - { - data: { - logIncidentHistory: { - incident_id: 3, - }, - }, - }, - 'logIncidentHistory' - ); - await page.goto('/cite/3'); - const now = new Date(); - await page.addInitScript(`{ - Date.now = () => ${now.getTime()}; - }`); - await page.locator('[data-cy="similar-incidents-column"] [data-cy="flag-similar-incident"]').first().click(); - await waitForRequest('logIncidentHistory'); + await expect(page.getByText('Incident flagged successfully. Our editors will remove it from this list if it not relevant.')).toBeVisible(); + const { data } = await query({ query: gql`{incident(filter: { incident_id: { EQ: 3 } }) { flagged_dissimilar_incidents @@ -502,7 +452,7 @@ test.describe('Cite pages', () => { test('Should show proper entities card text', async ({ page }) => { await page.goto('/cite/3/'); await expect(page.locator('[data-cy="alleged-entities"]')).toHaveText( - 'Alleged: Kronos developed an AI system deployed by Starbucks, which harmed Starbucks Employees.' + 'Alleged: Kronos developed an AI system deployed by Starbucks, which harmed Starbucks Employees.Implicated AI system: Entity 1' ); }); @@ -544,22 +494,6 @@ test.describe('Cite pages', () => { await login(process.env.E2E_ADMIN_USERNAME, process.env.E2E_ADMIN_PASSWORD, { customData: { first_name: 'John', last_name: 'Doe', roles: ['admin'] } }); - await conditionalIntercept( - page, - '**/graphql', - (req) => - req.postDataJSON().operationName === 'logIncidentHistory', - { - "data": { - "logIncidentHistory": { - "incident_id": 50, - "__typename": "LogIncidentHistoryPayload" - } - } - }, - 'logIncidentHistory' - ); - await page.goto('/incidents/edit/?incident_id=3'); await fillAutoComplete(page, '#input-incidentSearch', '1 Incident', 'Incident 1'); @@ -572,8 +506,6 @@ test.describe('Cite pages', () => { await page.getByText('Save').click(); - await waitForRequest('logIncidentHistory'); - await expect(page.locator('.tw-toast:has-text("Incident 3 updated successfully.")')).toBeVisible(); diff --git a/site/gatsby-site/playwright/e2e-full/citeEdit.spec.ts b/site/gatsby-site/playwright/e2e-full/citeEdit.spec.ts index 7ae3d42fba..f55ad24c14 100644 --- a/site/gatsby-site/playwright/e2e-full/citeEdit.spec.ts +++ b/site/gatsby-site/playwright/e2e-full/citeEdit.spec.ts @@ -1,4 +1,4 @@ -import { conditionalIntercept, waitForRequest, query, setEditorText, getEditorText, test } from '../utils'; +import { query, setEditorText, getEditorText, test } from '../utils'; import { expect } from '@playwright/test'; import config from '../config'; import { init } from '../memory-mongo'; @@ -20,24 +20,9 @@ test.describe('Edit report', () => { test('Should load and update report values', async ({ page, login }) => { - await login(config.E2E_ADMIN_USERNAME, config.E2E_ADMIN_PASSWORD, { customData: { roles: ['admin'] } }); + await init(); - // TODO: delete once we implement the new report history - // it should be done inside the report mutation resolver - - await conditionalIntercept( - page, - '**/graphql', - (req) => req.postDataJSON().operationName == 'logReportHistory', - { - data: { - logReportHistory: { - report_number: 3, - }, - }, - }, - 'logReportHistory' - ); + await login(config.E2E_ADMIN_USERNAME, config.E2E_ADMIN_PASSWORD, { customData: { roles: ['admin'] } }); await page.goto(url); @@ -104,8 +89,6 @@ test.describe('Edit report', () => { await page.getByRole('button', { name: 'Submit' }).click(); - await waitForRequest('logReportHistory'); - await expect(page.getByText('Incident report 3 updated successfully.')).toBeVisible(); const { data } = await query({ @@ -147,18 +130,8 @@ test.describe('Edit report', () => { test('Should load and update Issue values', async ({ page, login }) => { - - await login(config.E2E_ADMIN_USERNAME, config.E2E_ADMIN_PASSWORD, { customData: { roles: ['admin'] } }); - await conditionalIntercept( - page, - '**/graphql', - (req) => req.postDataJSON().operationName == 'logReportHistory', - { data: { logReportHistory: { report_number: 5 } } }, - 'logReportHistory' - ); - await page.goto('/cite/edit?report_number=5'); const updates = { @@ -188,8 +161,6 @@ test.describe('Edit report', () => { await page.getByRole('button', { name: 'Submit' }).click(); - await waitForRequest('logReportHistory'); - await expect(page.getByText('Issue 5 updated successfully')).toBeVisible({ timeout: 45000 }); @@ -262,24 +233,7 @@ test.describe('Edit report', () => { test('Should link a report to another incident', async ({ page, login }) => { - - - const [userId] = await login(config.E2E_ADMIN_USERNAME, config.E2E_ADMIN_PASSWORD); - await init({ customData: { users: [{ userId, first_name: 'Test', last_name: 'User', roles: ['admin'] }] } }, { drop: true }); - - await conditionalIntercept( - page, - '**/graphql', - (req) => req.postDataJSON().operationName == 'logReportHistory', - { - data: { - logReportHistory: { - report_number: 3, - }, - }, - }, - 'logReportHistory' - ); + await login(config.E2E_ADMIN_USERNAME, config.E2E_ADMIN_PASSWORD, { customData: { roles: ['admin'] } }); await page.goto(`/cite/edit?report_number=3`); @@ -295,7 +249,7 @@ test.describe('Edit report', () => { await page.getByRole('button', { name: 'Submit' }).click(); - await expect(page.locator('[data-cy="toast"]')).toContainText('Incident report 3 updated successfully', { timeout: 60000 }); + await expect(page.locator('[data-cy="toast"]')).toContainText('Incident report 3 updated successfully'); const result = await query({ query: gql`{ @@ -321,18 +275,8 @@ test.describe('Edit report', () => { test('Should convert an incident report to an issue', async ({ page, login }) => { - - await login(config.E2E_ADMIN_USERNAME, config.E2E_ADMIN_PASSWORD, { customData: { roles: ['admin'] } }); - await conditionalIntercept( - page, - '**/graphql', - (req) => req.postDataJSON().operationName == 'logReportHistory', - { data: { logReportHistory: { report_number: 3 } } }, - 'logReportHistory' - ); - await page.goto(`/cite/edit?report_number=3`); await page.locator('form[data-cy="report"]').waitFor(); diff --git a/site/gatsby-site/playwright/e2e-full/dynamicCite.spec.ts b/site/gatsby-site/playwright/e2e-full/dynamicCite.spec.ts index 1911d6b8f3..05493b33f7 100644 --- a/site/gatsby-site/playwright/e2e-full/dynamicCite.spec.ts +++ b/site/gatsby-site/playwright/e2e-full/dynamicCite.spec.ts @@ -34,7 +34,7 @@ test.describe('Dynamic Cite pages', () => { await expect(page.getByText(`Kronos’s scheduling algorithm and its use by Starbucks managers allegedly negatively impacted financial and scheduling stability for Starbucks employees, which disadvantaged wage workers.`)).toBeVisible(); await expect(page.locator('[data-cy="alleged-entities"]')).toHaveText( - 'Alleged: Kronos developed an AI system deployed by Starbucks, which harmed Starbucks Employees.' + 'Alleged: Kronos developed an AI system deployed by Starbucks, which harmed Starbucks Employees.Implicated AI system: Entity 1' ); await expect(page.locator('[data-cy="citation"]').getByText("Report Count", { exact: true }).locator('xpath=following-sibling::div[1]')).toHaveText('2'); diff --git a/site/gatsby-site/playwright/e2e-full/incidents/edit.spec.ts b/site/gatsby-site/playwright/e2e-full/incidents/edit.spec.ts index bc5fec2161..b95e20946f 100644 --- a/site/gatsby-site/playwright/e2e-full/incidents/edit.spec.ts +++ b/site/gatsby-site/playwright/e2e-full/incidents/edit.spec.ts @@ -37,18 +37,8 @@ test.describe('Incidents', () => { await fillAutoComplete(page, '#input-editors', 'Pab', 'Pablo Costa'); - await conditionalIntercept(page, '**/graphql', (req) => req.postDataJSON().operationName == 'logIncidentHistory', { - data: { - logIncidentHistory: { - incident_id: 112, - }, - }, - }, 'logIncidentHistory'); - await page.getByText('Save', { exact: true }).click(); - await waitForRequest('logIncidentHistory'); - await page.locator('.tw-toast:has-text("Incident 3 updated successfully.")').isVisible(); }); }); diff --git a/site/gatsby-site/playwright/e2e-full/incidents/history.spec.ts b/site/gatsby-site/playwright/e2e-full/incidents/history.spec.ts new file mode 100644 index 0000000000..e587756d8b --- /dev/null +++ b/site/gatsby-site/playwright/e2e-full/incidents/history.spec.ts @@ -0,0 +1,192 @@ +import { format, fromUnixTime } from 'date-fns'; +import { test, query } from '../../utils'; +import { expect } from '@playwright/test'; +import config from '../../config'; +import { init } from '../../memory-mongo'; +const { gql } = require('@apollo/client'); + +test.describe('Incidents', () => { + const url = '/incidents/history/?incident_id=1'; + const urlNoHistory = '/incidents/history/?incident_id=2'; + + test('Successfully loads', async ({ page }) => { + await page.goto(url); + }); + + test('Should display the Version History table data', async ({ page }) => { + + const { data: { history_incidents } } = await query({ + query: gql` + query { + history_incidents { + incident_id + epoch_date_modified + modifiedBy + } + } + ` + }); + + await page.goto(url); + + await page.locator('h2').getByText('Version History').waitFor(); + const rows = await page.locator('[data-cy="history-row"]').elementHandles(); + expect(rows.length).toBe(4); + + for (let [index, history] of history_incidents.entries()) { + const row = rows[index]; + await row.evaluate((node, epoch_date_modified) => node.textContent.includes(`${new Date(epoch_date_modified * 1000).toISOString().slice(0, 16).replace('T', ' ')}`), history.epoch_date_modified); + await row.evaluate((node, history) => node.textContent.includes(`Modified by: ${history.modifiedBy === '1' ? 'Sean McGregor' : 'Test User'}`), history); + } + + const lastRowIndex = history_incidents.length - 1; + + const lastRow = rows[lastRowIndex]; + await lastRow.evaluate((node) => node.textContent.includes('Initial version')); + }); + + test('Should not display the Version History table data if no data is present', async ({ page }) => { + await page.goto(urlNoHistory); + + await page.locator('h2').getByText('Version History').waitFor({ state: 'hidden' }); + await page.locator('[data-cy="history-table"]').waitFor({ state: 'hidden' }); + await page.getByText('There are no version history records for this Incident').waitFor(); + }); + + test('Should display an error message if no Incident ID is provided', async ({ page }) => { + await page.goto('/incidents/history?incident_id='); + await page.getByText('Invalid Incident ID').waitFor(); + }); + + test('Should display an error message if an invalid Incident ID is provided', async ({ page }) => { + await page.goto('/incidents/history?incident_id=xxx'); + await page.getByText('Invalid Incident ID').waitFor(); + }); + + test('Should go back to the Incident', async ({ page }) => { + await page.goto(url); + await page.getByText('Back to Incident 1').click(); + await page.waitForURL('/cite/1/'); + }); + + test('Should not be able to restore a version if the user does not have the right permissions', async ({ page }) => { + await page.goto(url); + await page.getByText('Restore Version').waitFor({ state: 'hidden' }); + }); + + test('Should restore an Incident previous version', async ({ page, login }) => { + + await init(); + + const { data: { history_incidents } } = await query({ + query: gql` + query { + history_incidents { + title + description + reports + incident_id + epoch_date_modified + modifiedBy + editors + } + } + ` + }); + + await login(config.E2E_ADMIN_USERNAME, config.E2E_ADMIN_PASSWORD, { customData: { roles: ['admin'] } }); + + await page.goto(url); + + await expect(page.locator('h2').getByText('Version History')).toBeVisible(); + + await expect(page.locator('[data-cy="history-row"]')).toHaveCount(4); + + + page.once('dialog', async dialog => { + await dialog.accept(); + }); + + await page.locator('[data-cy="history-row"] [data-cy="restore-button"]').last().click(); + + + await expect(page.getByTestId("restoring-message")).toBeVisible(); + + await expect(page.getByText('Incident version restored successfully.')).toBeVisible(); + + await expect(page.locator('[data-cy="restoring-message"]')).not.toBeVisible(); + + const { data: { incident } } = await query({ + query: gql` + query { + incident(filter: {incident_id: { EQ: 1 }}) { + title + description + reports { + report_number + } + date + editor_notes + epoch_date_modified + editors { + userId + } + } + } + ` + }); + + const restoredIncident = history_incidents[0]; + + expect(incident.title).toBe(restoredIncident.title); + expect(incident.description).toBe(restoredIncident.description); + expect(incident.reports.length).toBe(restoredIncident.reports.length); + }); + + test('Should display the Version History details modal', async ({ page }) => { + + await init(); + + const { data: { history_incidents } } = await query({ + query: gql` + query { + history_incidents(sort: {epoch_date_modified: DESC}) { + title + incident_id + epoch_date_modified + modifiedBy + description + reports + date + } + } + ` + }); + + await page.goto(url); + + const rows = await page.locator('[data-cy="history-row"]'); + + await rows.nth(0).locator('[data-cy="view-full-version-button"]').click(); + + const modal = page.locator('[data-cy="version-view-modal"]'); + await modal.waitFor(); + + const version1 = history_incidents[0]; + await modal.getByText('View Version details').waitFor(); + await modal.getByTestId('incident-title').getByText(version1.title).waitFor(); + await modal.getByText('Modified by: Test User').waitFor(); + await modal.getByText(`Modified on: ${format(fromUnixTime(version1.epoch_date_modified), 'yyyy-MM-dd hh:mm a')}`).waitFor(); + await modal.getByText(`Description: ${version1.description}`).waitFor(); + if (version1.editor_notes) { + await modal.getByText(`Editor Notes: ${version1.editor_notes}`).waitFor(); + } + await modal.locator('[data-cy="alleged-entities"]').getByText('Alleged: Entity 2 developed an AI system deployed by Entity 1, which harmed Entity 3.').waitFor(); + await modal.locator('[data-cy="citation"]').getByTestId('Incident ID').getByText(`${version1.incident_id}`).waitFor(); + await modal.locator('[data-cy="citation"]').getByTestId('Report Count').getByText(`${version1.reports.length}`, { exact: true }).waitFor(); + await modal.locator('[data-cy="citation"]').getByTestId('Incident Date').getByText(`${version1.date}`).waitFor(); + await modal.locator('[data-cy="citation"]').getByTestId('Editors').getByText('Test User, Sean McGregor').waitFor(); + await modal.locator('button').getByText('Close').click(); + await modal.waitFor({ state: 'hidden' }); + }); +}); \ No newline at end of file diff --git a/site/gatsby-site/playwright/e2e-full/incidents/new.spec.ts b/site/gatsby-site/playwright/e2e-full/incidents/new.spec.ts index 46a915d18a..c4840b6c44 100644 --- a/site/gatsby-site/playwright/e2e-full/incidents/new.spec.ts +++ b/site/gatsby-site/playwright/e2e-full/incidents/new.spec.ts @@ -32,20 +32,13 @@ test.describe('New Incident page', () => { await page.keyboard.press('Enter'); await page.locator('[data-cy="alleged-harmed-or-nearly-harmed-parties-input"] input').first().fill('children'); await page.keyboard.press('Enter'); + await page.locator('[data-cy="implicated-systems-input"] input').first().fill('children'); + await page.keyboard.press('Enter'); await fillAutoComplete(page, '#input-editors', 'Joh', 'John Doe'); - await conditionalIntercept(page, - '**/graphql', - (req) => req.postDataJSON().operationName == 'logIncidentHistory', - { data: { logIncidentHistory: { incident_id: 4 } } }, - 'logIncidentHistory' - ); - await page.getByText('Save').click(); - await waitForRequest('logIncidentHistory'); - await page.getByText(`You have successfully create Incident 4. View incident`).waitFor(); }); @@ -59,20 +52,10 @@ test.describe('New Incident page', () => { await page.goto(`${url}/?incident_id=3`); - await conditionalIntercept( - page, - '**/graphql', - (req) => req.postDataJSON().operationName == 'logIncidentHistory', - { data: { logIncidentHistory: { incident_id: newIncidentId } } }, - 'logIncidentHistory' - ); - await page.context().grantPermissions(['clipboard-read', 'clipboard-write']); await page.getByText('Save').click(); - await waitForRequest('logIncidentHistory'); - await page.getByText(`You have successfully create Incident ${newIncidentId}. View incident`).waitFor(); }); }); diff --git a/site/gatsby-site/playwright/e2e-full/pageCreators/createCitationPages.spec.ts b/site/gatsby-site/playwright/e2e-full/pageCreators/createCitationPages.spec.ts new file mode 100644 index 0000000000..185990f407 --- /dev/null +++ b/site/gatsby-site/playwright/e2e-full/pageCreators/createCitationPages.spec.ts @@ -0,0 +1,132 @@ +import path from 'path'; +import sinon from 'sinon'; +import createCitationPages from '../../../page-creators/createCitationPages'; +import { CreatePagesArgs } from 'gatsby'; +import { expect } from '@playwright/test'; +import { getLanguages, test } from '../../utils'; + +test.describe('createCitationPages', () => { + let graphql: sinon.SinonStub; + let createPage: sinon.SinonSpy; + const response = { + data: { + allMongodbAiidprodIncidents: { + nodes: [ + { + incident_id: 1, + title: 'Google’s YouTube Kids App Presents Inappropriate Content', + date: '2015-05-19', + reports: [ + { + title: 'Google’s YouTube Kids App Criticized for ‘Inappropriate Content’', + report_number: 1, + language: 'en', + image_url: + 'http://si.wsj.net/public/resources/images/BN-IM269_YouTub_P_20150518174822.jpg', + cloudinary_id: + 'reports/si.wsj.net/public/resources/images/BN-IM269_YouTub_P_20150518174822.jpg', + }, + { + title: 'YouTube Kids app is STILL showing disturbing videos', + report_number: 2, + language: 'en', + image_url: + 'https://i.dailymail.co.uk/i/pix/2018/02/06/15/48EEE02F00000578-0-image-a-18_1517931140185.jpg', + cloudinary_id: + 'reports/i.dailymail.co.uk/i/pix/2018/02/06/15/48EEE02F00000578-0-image-a-18_1517931140185.jpg', + }, + ], + editor_similar_incidents: [], + editor_dissimilar_incidents: [], + flagged_dissimilar_incidents: [], + description: + 'YouTube’s content filtering and recommendation algorithms exposed children to disturbing and inappropriate videos.', + nlp_similar_incidents: [ + { + incident_id: 55, + similarity: 0.9990941882133484, + }, + { + incident_id: 15, + similarity: 0.9989638924598694, + }, + { + incident_id: 34, + similarity: 0.998900830745697, + }, + ], + }, + ], + }, + }, + extensions: {}, + }; + + const languages = getLanguages(); + + test.beforeEach(() => { + graphql = sinon.stub(); + createPage = sinon.spy(); + }); + + test.afterEach(() => { + sinon.restore(); + }); + + test('Should create cite pages for each available language', async () => { + graphql.resolves(response); + + await createCitationPages(graphql as unknown as CreatePagesArgs['graphql'], createPage, { languages }); + + expect(createPage.callCount).toEqual(4); + + // Assert for English page + expect(createPage.calledWithMatch({ + path: '/cite/1/', + context: { + locale: 'en', + translate_es: true, + translate_en: false, + translate_fr: true, + translate_ja: true, + }, + component: sinon.match((value) => value.includes('/templates/cite.js')), + })).toBe(true); + + // Assert for Spanish page + expect(createPage.calledWithMatch({ + path: '/es/cite/1/', + context: { + locale: 'es', + translate_es: true, + translate_en: false, + translate_fr: true, + translate_ja: true, + }, + })).toBe(true); + + // Assert for French page + expect(createPage.calledWithMatch({ + path: '/fr/cite/1/', + context: { + locale: 'fr', + translate_es: true, + translate_en: false, + translate_fr: true, + translate_ja: true, + }, + })).toBe(true); + + // Assert for Japanese page + expect(createPage.calledWithMatch({ + path: '/ja/cite/1/', + context: { + locale: 'ja', + translate_es: true, + translate_en: false, + translate_fr: true, + translate_ja: true, + }, + })).toBe(true); + }); +}); diff --git a/site/gatsby-site/playwright/e2e-full/pageCreators/createDocPages.spec.ts b/site/gatsby-site/playwright/e2e-full/pageCreators/createDocPages.spec.ts new file mode 100644 index 0000000000..e66af77606 --- /dev/null +++ b/site/gatsby-site/playwright/e2e-full/pageCreators/createDocPages.spec.ts @@ -0,0 +1,127 @@ +import path from 'path'; +import sinon from 'sinon'; +import createDocPages from '../../../page-creators/createDocPages'; +import { CreatePagesArgs } from 'gatsby'; +import { expect } from '@playwright/test'; +import { test } from '../../utils'; + +test.describe('createDocPages', () => { + let graphql: sinon.SinonStub; + let createPage: sinon.SinonSpy; + let reporter: { warn: sinon.SinonSpy }; + + test.beforeEach(() => { + graphql = sinon.stub(); + createPage = sinon.spy(); + reporter = { warn: sinon.spy() }; + }); + + test.afterEach(() => { + sinon.restore(); + }); + + test('Creates pages for Prismic docs and MDX files', async () => { + const response = { + data: { + allPrismicDoc: { + edges: [ + { node: { data: { slug: 'doc-1-prismic' } } }, + { node: { data: { slug: 'doc-2-prismic' } } }, + ], + }, + allFile: { + nodes: [ + { + childMdx: { + frontmatter: { slug: 'doc-1' }, + internal: { contentFilePath: 'doc-1.mdx' }, + }, + }, + { + childMdx: { + frontmatter: { slug: 'doc-2' }, + internal: { contentFilePath: 'doc-2.mdx' }, + }, + }, + ], + }, + }, + }; + + graphql.resolves(response); + + await createDocPages(graphql as unknown as CreatePagesArgs['graphql'], createPage, { reporter }); + + // Assert Prismic pages + expect(createPage.calledWithMatch({ + path: 'doc-1-prismic', + component: `${path.resolve('./src/templates/prismicDoc.js')}`, + context: { slug: 'doc-1-prismic' }, + })).toBe(true); + + expect(createPage.calledWithMatch({ + path: 'doc-2-prismic', + component: `${path.resolve('./src/templates/prismicDoc.js')}`, + context: { slug: 'doc-2-prismic' }, + })).toBe(true); + + // Assert MDX pages + expect(createPage.calledWithMatch({ + path: 'doc-1', + component: `${path.resolve('./src/templates/doc.js')}?__contentFilePath=doc-1.mdx`, + context: { slug: 'doc-1' }, + })).toBe(true); + + expect(createPage.calledWithMatch({ + path: 'doc-2', + component: `${path.resolve('./src/templates/doc.js')}?__contentFilePath=doc-2.mdx`, + context: { slug: 'doc-2' }, + })).toBe(true); + + // Assert total calls + expect(createPage.callCount).toEqual(4); + }); + + test('Warns when a Prismic doc is missing a slug', async () => { + const response = { + data: { + allPrismicDoc: { + edges: [{ node: { data: { slug: null, title: 'Missing Slug Doc' } } }], + }, + allFile: { nodes: [] }, + }, + }; + + graphql.resolves(response); + + await createDocPages(graphql as unknown as CreatePagesArgs['graphql'], createPage, { reporter }); + + expect(reporter.warn.calledWithMatch('Missing slug for Missing Slug Doc')).toBe(true); + expect(createPage.notCalled).toBe(true); + }); + + test('Warns when an MDX file is missing a slug', async () => { + const response = { + data: { + allPrismicDoc: { edges: [] }, + allFile: { + nodes: [ + { + childMdx: { + frontmatter: { slug: null, title: 'Missing Slug Doc' }, + }, + absolutePath: '/path/to/missing-slug.mdx', + }, + ], + }, + }, + }; + + graphql.resolves(response); + + await createDocPages(graphql as unknown as CreatePagesArgs['graphql'], createPage, { reporter }); + + expect(reporter.warn.calledWithMatch('Missing slug for /path/to/missing-slug.mdx')).toBe(true); + expect(createPage.notCalled).toBe(true); + }); +}); diff --git a/site/gatsby-site/playwright/e2e-full/pageCreators/createEntitiesPages.spec.ts b/site/gatsby-site/playwright/e2e-full/pageCreators/createEntitiesPages.spec.ts new file mode 100644 index 0000000000..75113fe2a5 --- /dev/null +++ b/site/gatsby-site/playwright/e2e-full/pageCreators/createEntitiesPages.spec.ts @@ -0,0 +1,119 @@ +import sinon from 'sinon'; +import createEntitiesPages from '../../../page-creators/createEntitiesPages'; +import { CreatePagesArgs } from 'gatsby'; +import { expect } from '@playwright/test'; +import { test } from '../../utils'; + +test.describe('createEntitiesPages', () => { + let graphql: sinon.SinonStub; + let createPage: sinon.SinonSpy; + + const response = { + data: { + incidents: { + nodes: [ + { + incident_id: 1, + title: 'Incident 1', + Alleged_deployer_of_AI_system: ['ai-developer-1'], + Alleged_developer_of_AI_system: ['ai-developer-1'], + Alleged_harmed_or_nearly_harmed_parties: ['party-1'], + reports: [{ report_number: 1 }, { report_number: 2 }], + implicated_systems: ['ai-deployer-1'], + }, + { + incident_id: 2, + title: 'Incident 2', + Alleged_deployer_of_AI_system: ['ai-deployer-1'], + Alleged_developer_of_AI_system: ['ai-developer-1'], + Alleged_harmed_or_nearly_harmed_parties: ['party-1', 'party-2'], + reports: [{ report_number: 3 }], + implicated_systems: ['ai-deployer-1'], + }, + { + incident_id: 3, + title: 'Incident 3', + Alleged_deployer_of_AI_system: ['ai-developer-2', 'ai-deployer-2'], + Alleged_developer_of_AI_system: ['ai-developer-2'], + Alleged_harmed_or_nearly_harmed_parties: ['party-2'], + reports: [{ report_number: 4 }, { report_number: 5 }], + implicated_systems: ['ai-deployer-1'], + }, + { + incident_id: 4, + title: 'Incident 4', + Alleged_deployer_of_AI_system: ['ai-deployer-3'], + Alleged_developer_of_AI_system: ['ai-developer-1', 'ai-developer-2'], + Alleged_harmed_or_nearly_harmed_parties: ['party-3'], + reports: [{ report_number: 6 }, { report_number: 7 }, { report_number: 8 }], + implicated_systems: ['ai-deployer-1'], + }, + ], + }, + entities: { + nodes: [ + { entity_id: 'ai-deployer-1', name: 'AI Deployer 1' }, + { entity_id: 'ai-deployer-2', name: 'AI Deployer 2' }, + { entity_id: 'ai-deployer-3', name: 'AI Deployer 3' }, + { entity_id: 'ai-developer-1', name: 'AI Developer 1' }, + { entity_id: 'ai-developer-2', name: 'AI Developer 2' }, + { entity_id: 'party-1', name: 'Party 1' }, + { entity_id: 'party-2', name: 'Party 2' }, + { entity_id: 'party-3', name: 'Party 3' }, + ], + }, + responses: { + nodes: [ + { report_number: 2 }, + { report_number: 3 }, + { report_number: 5 }, + ], + }, + }, + }; + + test.beforeEach(() => { + graphql = sinon.stub(); + createPage = sinon.spy(); + }); + + test.afterEach(() => { + sinon.restore(); + }); + + test('Should parse properly', async () => { + graphql.resolves(response); + + await createEntitiesPages(graphql as unknown as CreatePagesArgs['graphql'], createPage); + + // Total pages created + expect(createPage.callCount).toEqual(9); + + // Validate the first entity + const firstPage = createPage.getCall(0).args[0]; + expect(firstPage.context.id).toBe('ai-developer-1'); + expect(firstPage.path).toBe('/entities/ai-developer-1'); + expect(firstPage.context.name).toBe('AI Developer 1'); + expect(firstPage.context.incidentsAsDeployer).toEqual([]); + expect(firstPage.context.incidentsAsDeveloper).toEqual([2, 4]); + expect(firstPage.context.incidentsAsBoth).toEqual([1]); + expect(firstPage.context.incidentsHarmedBy).toEqual([]); + expect(firstPage.context.relatedEntities).toEqual([ + 'party-1', + 'ai-deployer-1', + 'party-2', + 'ai-deployer-3', + 'ai-developer-2', + 'party-3', + ]); + expect(firstPage.context.responses).toEqual([ + { report_number: 2, incident_id: 1 }, + { report_number: 3, incident_id: 2 }, + ]); + + // Validate the last page + const lastPage = createPage.getCall(8).args[0]; + expect(lastPage.path).toBe('/entities'); + expect(lastPage.context.entities.length).toEqual(8); + }); +}); diff --git a/site/gatsby-site/playwright/e2e-full/pageCreators/createReportPages.spec.ts b/site/gatsby-site/playwright/e2e-full/pageCreators/createReportPages.spec.ts new file mode 100644 index 0000000000..d00ed9a4a5 --- /dev/null +++ b/site/gatsby-site/playwright/e2e-full/pageCreators/createReportPages.spec.ts @@ -0,0 +1,113 @@ +import sinon from 'sinon'; +import createReportPages from '../../../page-creators/createReportPages'; +import { CreatePagesArgs } from 'gatsby'; +import { expect } from '@playwright/test'; +import { getLanguages, test } from '../../utils'; + +test.describe('createReportPages', () => { + let graphql: sinon.SinonStub; + let createPage: sinon.SinonSpy; + + const response = { + data: { + reports: { + nodes: [ + { report_number: 1, language: 'en' }, + { report_number: 2, language: 'es' }, + ], + }, + }, + }; + + const languages = getLanguages(); + + test.beforeEach(() => { + graphql = sinon.stub(); + createPage = sinon.spy(); + }); + + test.afterEach(() => { + sinon.restore(); + }); + + test('Should parse properly', async () => { + graphql.resolves(response); + + await createReportPages(graphql as unknown as CreatePagesArgs['graphql'], createPage, { languages }); + + // Verify total pages created + expect(createPage.callCount).toEqual(8); + + // Validate the first page + const page1 = createPage.getCall(0).args[0]; + expect(page1.path).toContain('/reports/1/'); + expect(page1.context.originalPath).toBe('/reports/1/'); + expect(page1.context.locale).toBe('en'); + expect(page1.context.hrefLang).toBe('en-US'); + expect(page1.context.report_number).toBe(1); + expect(page1.context.language).toBe('en'); + expect(page1.context.translate_es).toBe(true); + expect(page1.context.translate_en).toBe(false); + expect(page1.context.translate_fr).toBe(true); + + // Validate the second page + const page2 = createPage.getCall(1).args[0]; + expect(page2.path).toContain('/reports/2/'); + expect(page2.context.originalPath).toBe('/reports/2/'); + expect(page2.context.locale).toBe('en'); + expect(page2.context.hrefLang).toBe('en-US'); + expect(page2.context.report_number).toBe(2); + expect(page2.context.language).toBe('es'); + expect(page2.context.translate_es).toBe(false); + expect(page2.context.translate_en).toBe(true); + expect(page2.context.translate_fr).toBe(true); + + // Validate the third page + const page3 = createPage.getCall(2).args[0]; + expect(page3.path).toContain('/es/reports/1/'); + expect(page3.context.originalPath).toBe('/es/reports/1/'); + expect(page3.context.locale).toBe('es'); + expect(page3.context.hrefLang).toBe('es'); + expect(page3.context.report_number).toBe(1); + expect(page3.context.language).toBe('en'); + expect(page3.context.translate_es).toBe(true); + expect(page3.context.translate_en).toBe(false); + expect(page3.context.translate_fr).toBe(true); + + // Validate the fourth page + const page4 = createPage.getCall(3).args[0]; + expect(page4.path).toContain('/es/reports/2/'); + expect(page4.context.originalPath).toBe('/es/reports/2/'); + expect(page4.context.locale).toBe('es'); + expect(page4.context.hrefLang).toBe('es'); + expect(page4.context.report_number).toBe(2); + expect(page4.context.language).toBe('es'); + expect(page4.context.translate_es).toBe(false); + expect(page4.context.translate_en).toBe(true); + expect(page4.context.translate_fr).toBe(true); + + // Validate the fifth page + const page5 = createPage.getCall(4).args[0]; + expect(page5.path).toContain('/fr/reports/1/'); + expect(page5.context.originalPath).toBe('/fr/reports/1/'); + expect(page5.context.locale).toBe('fr'); + expect(page5.context.hrefLang).toBe('fr'); + expect(page5.context.report_number).toBe(1); + expect(page5.context.language).toBe('en'); + expect(page5.context.translate_es).toBe(true); + expect(page5.context.translate_en).toBe(false); + expect(page5.context.translate_fr).toBe(true); + + // Validate the sixth page + const page6 = createPage.getCall(5).args[0]; + expect(page6.path).toContain('/fr/reports/2/'); + expect(page6.context.originalPath).toBe('/fr/reports/2/'); + expect(page6.context.locale).toBe('fr'); + expect(page6.context.hrefLang).toBe('fr'); + expect(page6.context.report_number).toBe(2); + expect(page6.context.language).toBe('es'); + expect(page6.context.translate_es).toBe(false); + expect(page6.context.translate_en).toBe(true); + expect(page6.context.translate_fr).toBe(true); + }); +}); diff --git a/site/gatsby-site/playwright/e2e-full/reportHistory.spec.ts b/site/gatsby-site/playwright/e2e-full/reportHistory.spec.ts new file mode 100644 index 0000000000..3443a939c4 --- /dev/null +++ b/site/gatsby-site/playwright/e2e-full/reportHistory.spec.ts @@ -0,0 +1,232 @@ +import { test, query } from '../utils'; +import { format, fromUnixTime } from 'date-fns'; +import supportedLanguages from '../../src/components/i18n/languages.json'; +import { expect } from '@playwright/test'; +import config from '../config'; +import gql from 'graphql-tag'; +import { init } from '../memory-mongo'; + +test.describe('Report History', () => { + const url = '/cite/history?report_number=1'; + const urlNoHistory = '/cite/history?report_number=2'; + + test('Successfully loads', async ({ page }) => { + await page.goto(url); + }); + + test('Should display the Report Version History table data', async ({ page }) => { + + await init(); + + const { data: { history_reports } } = await query({ + query: gql` + { + history_reports(sort: {date_modified: DESC}) { + title + report_number + date_modified + modifiedBy + } + } + ` + }); + + await page.goto(url); + + await expect(page.getByText('Version History')).toBeVisible(); + + const rows = await page.locator('[data-cy="history-row"]').count(); + + expect(rows).toBe(history_reports.length); + + for (let index = 0; index < history_reports.length; index++) { + const history = history_reports[index]; + const date = format(new Date(history.date_modified), 'yyyy-MM-dd hh:mm a'); + + await expect(page.locator('[data-cy="history-row"]').nth(index)).toContainText(date); + + await expect(page.locator('[data-cy="history-row"]').nth(index)).toContainText( + `Modified by: ${history.modifiedBy === 'user1' ? 'Test User' : 'Sean McGregor'}` + ); + } + + await expect(page.locator('[data-cy="history-row"]').last()).toContainText('Initial version'); + }); + + test('Should not display the Report Version History table data if no data is present', async ({ page }) => { + await page.goto(urlNoHistory); + + await expect(page.getByText('Version History')).not.toBeVisible(); + await expect(page.locator('[data-cy="history-table"]')).not.toBeVisible(); + await expect(page.getByText('There are no version history records for this Report')).toBeVisible({ timeout: 30000 }); + }); + + test('Should go back to the Incident Report', async ({ page }) => { + await page.goto(url); + await expect(page.getByText('Back to Report 1')).toBeVisible(); + await page.getByText('Back to Report 1').click(); + await page.waitForURL('/cite/1/#r1'); + }); + + test('Should go back to the Issue Report', async ({ page }) => { + await page.goto('/cite/history?report_number=5&incident_id=null'); + await page.getByText('Back to Report 5').click(); + await page.waitForURL('/reports/5/'); + }); + + test('Should refresh Report history if the user go back on the browser', async ({ page, skipOnEmptyEnvironment }) => { + await page.goto('/cite/1/'); + + await page.locator('button:has-text("Read More")').first().click(); + + await expect(page.locator('[data-cy="report-history-button"]')).toBeVisible(); + + await page.locator('[data-cy="report-history-button"]').click(); + + await expect(page).toHaveURL('/cite/history/?report_number=1&incident_id=1'); + + await page.goBack(); + + await expect(page).toHaveURL('/cite/1/'); + }); + + test('Should not be able to restore a version if the user does not have the right permissions', async ({ page }) => { + await page.goto(url); + await expect(page.getByText('Restore Version')).not.toBeVisible(); + }); + + test('Should restore a Report previous version', async ({ page, login }) => { + + await init(); + + const { data: { history_reports } } = await query({ + query: gql` + { + history_reports(sort: {date_modified: DESC}) { + title + text + report_number + date_modified + modifiedBy + } + } + ` + }); + + const [userId] = await login(config.E2E_ADMIN_USERNAME, config.E2E_ADMIN_PASSWORD, { customData: { roles: ['admin'] } }); + + await page.goto(url); + + for (let index = 0; index < history_reports.length; index++) { + if (index === 0) { + await expect(page.locator('[data-cy="history-row"]').nth(index).locator('[data-cy="restore-button"]')).not.toBeVisible(); + } else { + await expect(page.locator('[data-cy="history-row"]').nth(index).locator('[data-cy="restore-button"]')).toBeVisible(); + } + } + + page.once('dialog', async dialog => { + await dialog.accept(); + }); + + await page.locator('[data-cy="history-row"] [data-cy="restore-button"]').last().click(); + await expect(page.getByTestId("restoring-message")).toBeVisible(); + + + await expect(page.locator('[data-cy="toast"]')).toHaveText('Report version restored successfully.'); + + await expect(page.locator('[data-cy="restoring-message"]')).not.toBeVisible(); + + + const { data: { report, history_report } } = await query({ + query: gql` + { + report(filter: {report_number: { EQ: 1 }}) { + title + text + report_number + date_modified + } + history_report(sort: {date_modified: DESC}) { + title + text + report_number + date_modified + modifiedBy + } + } + ` + }); + + // TODO: be more specific about the report that was restored + await expect(report.title).toBe(history_reports[history_reports.length - 1].title); + await expect(report.text).toBe(history_reports[history_reports.length - 1].text); + await expect(history_report.modifiedBy).toBe(userId); + }); + + test('Should display the Report Version History details modal', async ({ page }) => { + + await init(); + + const { data: { history_reports } } = await query({ + query: gql` + { + history_reports(sort: {date_modified: DESC}) { + title + report_number + epoch_date_modified + modifiedBy + authors + submitters + date_published + date_downloaded + image_url + language + tags + editor_notes + text + cloudinary_id + } + } + ` + }); + + await page.goto(url); + + await page.locator('[data-cy="history-row"]').nth(1).locator('[data-cy="view-full-version-button"]').click(); + + await expect(page.locator('[data-cy="version-view-modal"]')).toBeVisible(); + + const version = history_reports[1]; + + await expect(page.getByText('Version details')).toBeVisible(); + await expect(page.getByText(`Modified on: ${format(fromUnixTime(version.epoch_date_modified), 'yyyy-MM-dd hh:mm a')}`)).toBeVisible(); + await expect(page.locator('[data-cy="version-view-modal"] div').filter({ hasText: 'Modified by: Sean McGregor' }).first()).toBeVisible(); + + + const fields = [ + { label: 'Report Address:', value: version.url }, + { label: 'Title:', value: version.title }, + { label: 'Author CSV:', value: version.authors.join(', ') }, + { label: 'Submitter CSV:', value: version.submitters.join(', ') }, + { label: 'Date Published:', value: version.date_published }, + { label: 'Date Downloaded:', value: version.date_downloaded }, + { label: 'Image Address:', value: version.image_url }, + { label: 'Text:', value: version.text }, + { label: 'Language:', value: supportedLanguages.find((l) => l.code == version.language)?.name }, + { label: 'Tags:', value: version.tags.join(', ') }, + { label: 'Editor Notes:', value: version.editor_notes } + ]; + + for (const field of fields) { + await expect(page.getByText(field.label)).toBeVisible(); + await expect(page.locator('[data-cy="version-view-modal"] div').filter({ hasText: field.value }).first()).toBeVisible(); + + } + + await expect(page.locator('[data-cy="version-view-modal"] [data-cy="cloudinary-image"]').first()).toHaveAttribute('src', expect.stringContaining(version.cloudinary_id)); + + await page.locator('[data-cy="version-view-modal"] button').filter({ hasText: 'Close' }).click(); + await expect(page.locator('[data-cy="version-view-modal"]')).not.toBeVisible(); + }); +}); \ No newline at end of file diff --git a/site/gatsby-site/playwright/e2e-full/submit.spec.ts b/site/gatsby-site/playwright/e2e-full/submit.spec.ts index 309b4c65ab..acd3c0d899 100644 --- a/site/gatsby-site/playwright/e2e-full/submit.spec.ts +++ b/site/gatsby-site/playwright/e2e-full/submit.spec.ts @@ -1497,4 +1497,54 @@ test.describe('The Submit form', () => { await expect(page.locator('.tw-toast:has-text("Please verify all information programmatically pulled from the report")')).toBeVisible(); await expect(page.locator('.tw-toast:has-text("Error fetching news.")')).not.toBeVisible(); }); + + test('Should autocomplete new entities', async ({ page, skipOnEmptyEnvironment }) => { + + await conditionalIntercept( + page, + '**/parseNews**', + () => true, + parseNews, + 'parseNews' + ); + + await trackRequest( + page, + '**/graphql', + (req) => req.postDataJSON().operationName == 'FindSubmissions', + 'findSubmissions' + ); + + await page.goto(url); + + await waitForRequest('findSubmissions'); + + await page.locator('input[name="url"]').fill( + `https://www.arstechnica.com/gadgets/2017/11/youtube-to-crack-down-on-inappropriate-content-masked-as-kids-cartoons/` + ); + + await page.locator('button:has-text("Fetch info")').click(); + + await waitForRequest('parseNews'); + + await page.locator('[name="incident_date"]').fill('2020-01-01'); + + await expect(page.locator('.form-has-errors')).not.toBeVisible(); + + await page.locator('[data-cy="to-step-2"]').click(); + + await page.locator('[data-cy="to-step-3"]').click(); + + await page.locator('input[name="developers"]').fill('New entity'); + await page.keyboard.press('Enter'); + + await page.locator('input[name="deployers"]').fill('New entity'); + + await page.locator('#deployers-tags .dropdown-item[aria-label="New entity"]').click(); + + await page.locator('button[type="submit"]').click(); + + await expect(page.locator('.tw-toast:has-text("Report successfully added to review queue. You can see your submission")')).toBeVisible(); + await expect(page.locator(':text("Please review. Some data is missing.")')).not.toBeVisible(); + }); }); \ No newline at end of file diff --git a/site/gatsby-site/playwright/e2e-full/subscription.spec.ts b/site/gatsby-site/playwright/e2e-full/subscription.spec.ts index bca263bf6f..fa3b91eb15 100644 --- a/site/gatsby-site/playwright/e2e-full/subscription.spec.ts +++ b/site/gatsby-site/playwright/e2e-full/subscription.spec.ts @@ -13,7 +13,7 @@ test.describe('Subscriptions', () => { await init(); - const [userId] = await login(process.env.E2E_ADMIN_USERNAME, process.env.E2E_ADMIN_PASSWORD, { customData: { roles: ['admin'], first_name: 'John', last_name: 'Doe' } }); + const [userId] = await login(process.env.E2E_ADMIN_USERNAME, process.env.E2E_ADMIN_PASSWORD, { customData: { roles: ['subscriber'], first_name: 'John', last_name: 'Doe' } }); const subscriptions: DBSubscription[] = [ { @@ -36,7 +36,7 @@ test.describe('Subscriptions', () => { test("Incident Updates: Should display a information message if the user doesn't have subscriptions", async ({ page, login }) => { await init(); - await login(process.env.E2E_ADMIN_USERNAME, process.env.E2E_ADMIN_PASSWORD, { customData: { roles: ['admin'], first_name: 'John', last_name: 'Doe' } }); + await login(process.env.E2E_ADMIN_USERNAME, process.env.E2E_ADMIN_PASSWORD, { customData: { roles: ['subscriber'], first_name: 'John', last_name: 'Doe' } }); await page.goto(url); @@ -47,7 +47,7 @@ test.describe('Subscriptions', () => { await init(); - const [userId, accessToken] = await login(process.env.E2E_ADMIN_USERNAME, process.env.E2E_ADMIN_PASSWORD, { customData: { roles: ['admin'], first_name: 'John', last_name: 'Doe' } }); + const [userId, accessToken] = await login(process.env.E2E_ADMIN_USERNAME, process.env.E2E_ADMIN_PASSWORD, { customData: { roles: ['subscriber'], first_name: 'John', last_name: 'Doe' } }); const subscriptions: DBSubscription[] = [ { @@ -96,7 +96,7 @@ test.describe('Subscriptions', () => { test('New Incidents: Should display the switch toggle off if user does not have a subscription', async ({ page, login }) => { - await login(process.env.E2E_ADMIN_USERNAME, process.env.E2E_ADMIN_PASSWORD, { customData: { roles: ['admin'], first_name: 'John', last_name: 'Doe' } }); + await login(process.env.E2E_ADMIN_USERNAME, process.env.E2E_ADMIN_PASSWORD, { customData: { roles: ['subscriber'], first_name: 'John', last_name: 'Doe' } }); await page.goto(url); @@ -109,7 +109,7 @@ test.describe('Subscriptions', () => { await init(); - const [userId] = await login(process.env.E2E_ADMIN_USERNAME, process.env.E2E_ADMIN_PASSWORD, { customData: { roles: ['admin'], first_name: 'John', last_name: 'Doe' } }); + const [userId] = await login(process.env.E2E_ADMIN_USERNAME, process.env.E2E_ADMIN_PASSWORD, { customData: { roles: ['subscriber'], first_name: 'John', last_name: 'Doe' } }); const subscriptions: DBSubscription[] = [ { @@ -152,7 +152,7 @@ test.describe('Subscriptions', () => { await init(); - const [userId] = await login(process.env.E2E_ADMIN_USERNAME, process.env.E2E_ADMIN_PASSWORD, { customData: { roles: ['admin'], first_name: 'John', last_name: 'Doe' } }); + const [userId] = await login(process.env.E2E_ADMIN_USERNAME, process.env.E2E_ADMIN_PASSWORD, { customData: { roles: ['subscriber'], first_name: 'John', last_name: 'Doe' } }); const subscriptions: DBSubscription[] = [ { @@ -182,7 +182,7 @@ test.describe('Subscriptions', () => { await init(); - const [userId] = await login(process.env.E2E_ADMIN_USERNAME, process.env.E2E_ADMIN_PASSWORD, { customData: { roles: ['admin'], first_name: 'John', last_name: 'Doe' } }); + const [userId] = await login(process.env.E2E_ADMIN_USERNAME, process.env.E2E_ADMIN_PASSWORD, { customData: { roles: ['subscriber'], first_name: 'John', last_name: 'Doe' } }); const subscriptions: DBSubscription[] = [ { @@ -212,7 +212,7 @@ test.describe('Subscriptions', () => { await init(); - await login(process.env.E2E_ADMIN_USERNAME, process.env.E2E_ADMIN_PASSWORD, { customData: { roles: ['admin'], first_name: 'John', last_name: 'Doe' } }); + await login(process.env.E2E_ADMIN_USERNAME, process.env.E2E_ADMIN_PASSWORD, { customData: { roles: ['subscriber'], first_name: 'John', last_name: 'Doe' } }); await page.goto(url); @@ -224,7 +224,7 @@ test.describe('Subscriptions', () => { await init(); - const [userId, accessToken] = await login(process.env.E2E_ADMIN_USERNAME, process.env.E2E_ADMIN_PASSWORD, { customData: { roles: ['admin'], first_name: 'John', last_name: 'Doe' } }); + const [userId, accessToken] = await login(process.env.E2E_ADMIN_USERNAME, process.env.E2E_ADMIN_PASSWORD, { customData: { roles: ['subscriber'], first_name: 'John', last_name: 'Doe' } }); const subscriptions: DBSubscription[] = [ { diff --git a/site/gatsby-site/playwright/e2e-full/translationBadge.spec.ts b/site/gatsby-site/playwright/e2e-full/translationBadge.spec.ts new file mode 100644 index 0000000000..b6fe0269f0 --- /dev/null +++ b/site/gatsby-site/playwright/e2e-full/translationBadge.spec.ts @@ -0,0 +1,48 @@ +import { expect } from '@playwright/test'; +import { mockAlgolia, test } from '../utils'; + +test.describe('Translation Badges', () => { + test('Should be visible on blog post', async ({ page }) => { + await page.goto('/es/blog/using-ai-to-connect-ai-incidents'); + await expect(page.locator('[data-cy="translation-badge"]').getByText('Traducido por IA')).toBeVisible(); + await expect(page.locator('a', { hasText: 'Ver Original' })).toBeVisible(); + await expect(page.locator('a', { hasText: 'Ver Original' })).toHaveAttribute('href', '/blog/using-ai-to-connect-ai-incidents/'); + }); + + test('Should not be visible on Prismic blog post without translation', async ({ page }) => { + await page.goto('/es/blog/ai-incident-journalism-analysis'); + await expect(page.locator('[data-cy="translation-badge"]').getByText('Traducido por IA')).not.toBeVisible(); + await expect(page.locator('a', { hasText: 'Ver Original' })).not.toBeVisible(); + }); + + test('Should be visible on the discover app if the item has a translation available', async ({ page, skipOnEmptyEnvironment }) => { + await mockAlgolia(page); + + await page.goto('/es/apps/discover'); + await expect(page.locator('[data-cy="6243a9eedf8b4b62d982817e"]').locator('[data-cy="translation-badge"]').getByText('Traducido por IA')).toBeVisible(); + }); + + test('Should not be visible on the discover app if the item does not have a translation available', async ({ page, skipOnEmptyEnvironment }) => { + await mockAlgolia(page); + + await page.goto('/es/apps/discover'); + await expect(page.locator('[data-cy="5d34b8c29ced494f010ed470"]').locator('[data-cy="translation-badge"]').getByText('Traducido por IA')).not.toBeVisible(); + }); + + test('Should be visible on an report card on the citation page if it was translated', async ({ page, skipOnEmptyEnvironment }) => { + await page.goto('/es/cite/3#r3'); + await expect(page.locator('#r3').locator('[data-cy="translation-badge"]').getByText('Traducido por IA')).toBeVisible(); + }); + + test('Should not be visible on an report card on the citation page if it was not translated', async ({ page, skipOnEmptyEnvironment }) => { + await page.goto('/es/cite/3#r4'); + await expect(page.locator('#r4').locator('[data-cy="translation-badge"]').getByText('Traducido por IA')).not.toBeVisible(); + }); + + test('Should be visible on documentation pages', async ({ page }) => { + await page.goto('/es/about_apps'); + await expect(page.locator('[data-cy="translation-badge"]').getByText('Traducido por IA')).toBeVisible(); + await expect(page.locator('a', { hasText: 'Ver Original' })).toBeVisible(); + await expect(page.locator('a', { hasText: 'Ver Original' })).toHaveAttribute('href', '/about_apps/'); + }); +}); diff --git a/site/gatsby-site/playwright/e2e/blog.spec.ts b/site/gatsby-site/playwright/e2e/blog.spec.ts index 35c79b1473..db828da5cd 100644 --- a/site/gatsby-site/playwright/e2e/blog.spec.ts +++ b/site/gatsby-site/playwright/e2e/blog.spec.ts @@ -3,10 +3,71 @@ import { expect } from '@playwright/test'; test.describe('Blog', () => { + test('Should load mdx blog post', async ({ page, skipOnEmptyEnvironment }) => { + await page.setViewportSize({ width: 1280, height: 1000 }); + await page.goto('/blog/the-first-taxonomy-of-ai-incidents'); + + await expect(page.locator('.titleWrapper h1')).toHaveText('The First Taxonomy of AI Incidents'); + + const div = await page.locator("[data-testid='blog-content']"); + const textContent = await div.textContent(); + expect(textContent).toContain('In November the Partnership on AI AI Incident Database (AIID) publicly invited users to instantly search through thousands of pages of text to better understand the limitations of AI products within the real world. Since November, tens of thousands of people from 157 countries have connected to the AIID. Today marks the launch of the next stage of AI Incident Database with its first complete AI incident taxonomy.'); + + }); + + test('Should load mdx blog post in spanish', async ({ page, skipOnEmptyEnvironment }) => { + await page.setViewportSize({ width: 1280, height: 1000 }); + await page.goto('/es/blog/representation-and-imagination'); + + await expect(page.locator('.titleWrapper h1')).toHaveText('Representación e imaginación para prevenir los daños de la IA'); + + const div = await page.locator("[data-testid='blog-content']"); + const textContent = await div.textContent(); + expect(textContent).toContain('La base de datos de incidentes de IA se lanzó públicamente en noviembre de 2020 por Partnership on AI como un panel de control de los daños de IA realizados en el mundo real.'); + + }); + + test('Should load mdx blog post in french', async ({ page, skipOnEmptyEnvironment }) => { + await page.setViewportSize({ width: 1280, height: 1000 }); + await page.goto('/fr/blog/join-raic'); + + await expect(page.locator('.titleWrapper h1')).toHaveText("Rejoignez l'équipe fondatrice de Responsible AI Collaborative"); + + const div = await page.locator("[data-testid='blog-content']"); + const textContent = await div.textContent(); + expect(textContent).toContain("La base de données d'incidents d'IA lancée publiquement en novembre 2020 en tant que tableau de bord des dommages causés par l'IA dans le monde réel."); + + }); + + test('Should load prismic blog post', async ({ page, skipOnEmptyEnvironment }) => { + await page.setViewportSize({ width: 1280, height: 1000 }); + await page.goto('/blog/incident-report-2024-january'); + + await expect(page.locator('h1')).toHaveText('AI Incident Roundup – January ‘24'); + + const div = await page.locator("[data-testid='blog-content']"); + const textContent = await div.textContent(); + expect(textContent).toContain('Read our month-in-review newsletter recapping new incidents in the AI Incident Database and looking at the trends.'); + + }); + + test('Should load prismic blog post in spanish', async ({ page, skipOnEmptyEnvironment }) => { + await page.setViewportSize({ width: 1280, height: 1000 }); + await page.goto('/es/blog/incident-report-2024-january'); + + await expect(page.locator('h1')).toHaveText('Resumen de incidentes de IA: 24 de enero'); + + const div = await page.locator("[data-testid='blog-content']"); + const textContent = await div.textContent(); + expect(textContent).toContain('Lea nuestro boletín informativo mensual que resume los nuevos incidentes en la base de datos de incidentes de IA y analiza las tendencias.'); + + }); + test('Should include outline in blog post', async ({ page, skipOnEmptyEnvironment }) => { await page.setViewportSize({ width: 1280, height: 1000 }); await page.goto('/blog/the-first-taxonomy-of-ai-incidents'); + await expect(page.locator('[data-cy="outline"]')).toBeVisible(); const outlineItems = page.locator('[data-cy="outline"] > li'); await expect(outlineItems).toHaveCount(5); @@ -27,8 +88,9 @@ test.describe('Blog', () => { await page.setViewportSize({ width: 1280, height: 1000 }); await page.goto('/es/blog/multilingual-incident-reporting'); - const outlineItemsCount = await page.locator('[data-cy="outline"] > li').count(); - await expect(outlineItemsCount).toBeGreaterThanOrEqual(3); + await expect(page.locator('[data-cy="outline"]')).toBeVisible(); + const outlineItems = page.locator('[data-cy="outline"] > li'); + await expect(outlineItems).toHaveCount(4); await expect(page.locator('[data-cy="outline"]:has-text("¿Como funciona?")')).toBeVisible(); await expect(page.locator('[data-cy="outline"]:has-text("Llamado a la acción")')).toBeVisible(); diff --git a/site/gatsby-site/playwright/e2e/doc.spec.ts b/site/gatsby-site/playwright/e2e/doc.spec.ts new file mode 100644 index 0000000000..96fd4de374 --- /dev/null +++ b/site/gatsby-site/playwright/e2e/doc.spec.ts @@ -0,0 +1,129 @@ +import { test } from '../utils'; +import { expect } from '@playwright/test'; + +test.describe('Docs', () => { + + test('Should load mdx document in English', async ({ page, skipOnEmptyEnvironment }) => { + await page.setViewportSize({ width: 1280, height: 1000 }); + await page.goto('/research/4-related-work'); + + await expect(page.locator('.titleWrapper h1')).toHaveText('Related Work'); + + const div = await page.locator("[data-testid='doc-content']"); + const textContent = await div.textContent(); + expect(textContent).toContain('While formal AI incident research is relatively new, a number of people have been collecting what could be considered incidents. These include,'); + + }); + + test('Should load mdx doc in spanish', async ({ page, skipOnEmptyEnvironment }) => { + await page.setViewportSize({ width: 1280, height: 1000 }); + await page.goto('/es/research/4-related-work'); // This doc hasn't been translated yet to Spanish yet, so it should fallback to English + + await expect(page.locator('.titleWrapper h1')).toHaveText('Related Work'); + + const div = await page.locator("[data-testid='doc-content']"); + const textContent = await div.textContent(); + expect(textContent).toContain('While formal AI incident research is relatively new, a number of people have been collecting what could be considered incidents. These include,'); + + }); + + test('Should load mdx doc in french', async ({ page, skipOnEmptyEnvironment }) => { + + await page.setViewportSize({ width: 1280, height: 1000 }); + await page.goto('/fr/research/4-related-work'); // This doc hasn't been translated to French yet, so it will default to English + + await expect(page.locator('.titleWrapper h1')).toHaveText('Related Work'); + + const div = await page.locator("[data-testid='doc-content']"); + const textContent = await div.textContent(); + expect(textContent).toContain('While formal AI incident research is relatively new, a number of people have been collecting what could be considered incidents. These include,'); + + }); + + test('Should load mdx doc in japanese', async ({ page, skipOnEmptyEnvironment }) => { + + await page.setViewportSize({ width: 1280, height: 1000 }); + await page.goto('/ja/research/4-related-work'); + + await expect(page.locator('.titleWrapper h1')).toHaveText('関連する研究'); + + const div = await page.locator("[data-testid='doc-content']"); + const textContent = await div.textContent(); + expect(textContent).toContain('公式なAIインシデント研究は比較的新しいものですが、何人かの人々がインシデントと見なされる可能性のある事例を収集しています。これには'); + + }); + + test('Should load prismic doc post', async ({ page, skipOnEmptyEnvironment }) => { + await page.setViewportSize({ width: 1280, height: 1000 }); + await page.goto('/about'); + + await expect(page.locator('h1')).toHaveText('About'); + + const div = await page.locator("[data-testid='markdown-content']"); + const textContent = await div.textContent(); + expect(textContent).toContain('Intelligent systems are currently prone to unforeseen and often dangerous failures when they are deployed to the real world.'); + + }); + + test('Should load prismic doc post in spanish', async ({ page, skipOnEmptyEnvironment }) => { + await page.setViewportSize({ width: 1280, height: 1000 }); + await page.goto('/es/about'); + + await expect(page.locator('h1')).toHaveText('Acerca de'); + + const div = await page.locator("[data-testid='markdown-content']"); + const textContent = await div.textContent(); + expect(textContent).toContain('Actualmente, los sistemas inteligentes son propensos a sufrir fallos imprevistos y, a menudo, peligrosos cuando se implementan en el mundo real.'); + + }); + + test('Should include outline in About page', async ({ page, skipOnEmptyEnvironment }) => { + await page.setViewportSize({ width: 1280, height: 1000 }); + await page.goto('/about'); + + const outlineItems = page.locator('[data-cy="outline"] > li'); + await expect(outlineItems).toHaveCount(7); + + await expect(page.locator('[data-cy="outline"]:has-text("Why \\"AI Incidents\\"?")')).toBeVisible(); + await expect(page.locator('[data-cy="outline"]:has-text("What is an Incident?")')).toBeVisible(); + await expect(page.locator('[data-cy="outline"]:has-text("Current and Future Users")')).toBeVisible(); + await expect(page.locator('[data-cy="outline"]:has-text("When Should You Report an Incident?")')).toBeVisible(); + await expect(page.locator('[data-cy="outline"]:has-text("Board of Directors")')).toBeVisible(); + await expect(page.locator('[data-cy="outline"]:has-text("Collaborators")')).toBeVisible(); + + await page.setViewportSize({ width: 800, height: 1000 }); + + + await expect(page.locator('[data-cy="outline"]:has-text("Why \\"AI Incidents\\"?")')).not.toBeVisible(); + await expect(page.locator('[data-cy="outline"]:has-text("What is an Incident?")')).not.toBeVisible(); + await expect(page.locator('[data-cy="outline"]:has-text("Current and Future Users")')).not.toBeVisible(); + await expect(page.locator('[data-cy="outline"]:has-text("When Should You Report an Incident?")')).not.toBeVisible(); + await expect(page.locator('[data-cy="outline"]:has-text("Board of Directors")')).not.toBeVisible(); + await expect(page.locator('[data-cy="outline"]:has-text("Collaborators")')).not.toBeVisible(); + }); + + test('Should include outline in Spanish About page', async ({ page, skipOnEmptyEnvironment }) => { + await page.setViewportSize({ width: 1280, height: 1000 }); + await page.goto('/es/about'); + + const outlineItems = page.locator('[data-cy="outline"] > li'); + await expect(outlineItems).toHaveCount(7); + + await expect(page.locator('[data-cy="outline"]:has-text("¿Por qué \\"incidentes de IA\\"?")')).toBeVisible(); + await expect(page.locator('[data-cy="outline"]:has-text("¿Qué es un incidente?")')).toBeVisible(); + await expect(page.locator('[data-cy="outline"]:has-text("Usuarios actuales y futuros")')).toBeVisible(); + await expect(page.locator('[data-cy="outline"]:has-text("¿Cuándo debería informar un incidente?")')).toBeVisible(); + await expect(page.locator('[data-cy="outline"]:has-text("Junta Directiva")')).toBeVisible(); + await expect(page.locator('[data-cy="outline"]:has-text("Colaboradores")')).toBeVisible(); + + await page.setViewportSize({ width: 800, height: 1000 }); + + + await expect(page.locator('[data-cy="outline"]:has-text("¿Por qué \\"incidentes de IA\\"?")')).not.toBeVisible(); + await expect(page.locator('[data-cy="outline"]:has-text("¿Qué es un incidente?")')).not.toBeVisible(); + await expect(page.locator('[data-cy="outline"]:has-text("Usuarios actuales y futuros")')).not.toBeVisible(); + await expect(page.locator('[data-cy="outline"]:has-text("¿Cuándo debería informar un incidente?")')).not.toBeVisible(); + await expect(page.locator('[data-cy="outline"]:has-text("Junta Directiva")')).not.toBeVisible(); + await expect(page.locator('[data-cy="outline"]:has-text("Colaboradores")')).not.toBeVisible(); + }); +}); \ No newline at end of file diff --git a/site/gatsby-site/playwright/e2e/incidents/history.spec.ts b/site/gatsby-site/playwright/e2e/incidents/history.spec.ts deleted file mode 100644 index e6ff2a8afd..0000000000 --- a/site/gatsby-site/playwright/e2e/incidents/history.spec.ts +++ /dev/null @@ -1,327 +0,0 @@ -import { format, fromUnixTime, getUnixTime } from 'date-fns'; -import incidentHistory from '../../fixtures/history/incidentHistory.json'; -import versionReports from '../../fixtures/history/versionReports.json'; -import { test, conditionalIntercept, waitForRequest, query } from '../../utils'; -import { expect } from '@playwright/test'; -import config from '../../config'; -const { gql } = require('@apollo/client'); - -test.describe('Incidents', () => { - const url = '/incidents/history/?incident_id=10'; - - test('Successfully loads', async ({ page }) => { - await page.goto(url); - }); - - test('Should display the Version History table data', async ({ page }) => { - await page.goto(url); - - await conditionalIntercept(page, '**/graphql', (req) => req.postDataJSON().operationName == 'FindIncidentHistory', incidentHistory, 'FindIncidentHistory'); - await conditionalIntercept(page, '**/graphql', (req) => req.postDataJSON().operationName == 'FindUsers', { - data: { - users: [ - { userId: '1', first_name: 'Sean', last_name: 'McGregor', roles: [] }, - { userId: '2', first_name: 'Pablo', last_name: 'Costa', roles: [] }, - ], - }, - }, 'FindUsers'); - await conditionalIntercept(page, '**/graphql', (req) => req.postDataJSON().operationName == 'FindEntities', { - data: { - entities: [ - { __typename: 'Entity', entity_id: 'youtube', name: 'Youtube' }, - { __typename: 'Entity', entity_id: 'google', name: 'Google' }, - { __typename: 'Entity', entity_id: 'tesla', name: 'Tesla' }, - ], - }, - }, 'FindEntities'); - - await Promise.all([ - waitForRequest('FindIncidentHistory'), - waitForRequest('FindEntities'), - waitForRequest('FindUsers') - ]); - - await page.locator('h2').getByText('Version History').waitFor(); - const rows = await page.locator('[data-cy="history-row"]').elementHandles(); - expect(rows.length).toBe(4); - - for (let [index, history] of incidentHistory.data.history_incidents.entries()) { - const row = rows[index]; - await row.evaluate((node, epoch_date_modified) => node.textContent.includes(`${new Date(epoch_date_modified * 1000).toISOString().slice(0, 16).replace('T', ' ')}`), history.epoch_date_modified); - await row.evaluate((node, history) => node.textContent.includes(`Modified by: ${history.modifiedBy === '1' ? 'Sean McGregor' : 'Pablo Costa'}`), history); - } - - const lastRowIndex = incidentHistory.data.history_incidents.length - 1; - - const lastRow = rows[lastRowIndex]; - await lastRow.evaluate((node) => node.textContent.includes('Initial version')); - }); - - test('Should not display the Version History table data if no data is present', async ({ page }) => { - await page.goto(url); - - await conditionalIntercept(page, '**/graphql', (req) => req.postDataJSON().operationName == 'FindIncidentHistory', { data: { history_incidents: [] } }, 'FindIncidentHistory'); - await conditionalIntercept(page, '**/graphql', (req) => req.postDataJSON().operationName == 'FindUsers', { - data: { - users: [ - { userId: '1', first_name: 'Sean', last_name: 'McGregor', roles: [] }, - { userId: '2', first_name: 'Pablo', last_name: 'Costa', roles: [] }, - ], - }, - }, 'FindUsers'); - await conditionalIntercept(page, '**/graphql', (req) => req.postDataJSON().operationName == 'FindEntities', { - data: { - entities: [ - { __typename: 'Entity', entity_id: 'youtube', name: 'Youtube' }, - { __typename: 'Entity', entity_id: 'google', name: 'Google' }, - { __typename: 'Entity', entity_id: 'tesla', name: 'Tesla' }, - ], - }, - }, 'FindEntities'); - - await Promise.all([ - waitForRequest('FindIncidentHistory'), - waitForRequest('FindEntities'), - waitForRequest('FindUsers') - ]); - - await page.locator('h2').getByText('Version History').waitFor({ state: 'hidden' }); - await page.locator('[data-cy="history-table"]').waitFor({ state: 'hidden' }); - await page.getByText('There are no version history records for this Incident').waitFor(); - }); - - test('Should display an error message if no Incident ID is provided', async ({ page }) => { - await page.goto('/incidents/history?incident_id='); - await page.getByText('Invalid Incident ID').waitFor(); - }); - - test('Should display an error message if an invalid Incident ID is provided', async ({ page }) => { - await page.goto('/incidents/history?incident_id=xxx'); - await page.getByText('Invalid Incident ID').waitFor(); - }); - - test('Should go back to the Incident', async ({ page }) => { - await page.goto(url); - await page.getByText('Back to Incident 10').click(); - await page.waitForURL('/cite/10/'); - }); - - test('Should not be able to restore a version if the user does not have the right permissions', async ({ page }) => { - await page.goto(url); - await page.getByText('Restore Version').waitFor({ state: 'hidden' }); - }); - - test('Should restore an Incident previous version', async ({ page, login }) => { - - const [userId] = await login(config.E2E_ADMIN_USERNAME, config.E2E_ADMIN_PASSWORD); - - await page.goto(url); - - await conditionalIntercept(page, '**/graphql', (req) => req.postDataJSON().operationName == 'FindIncidentHistory', incidentHistory, 'FindIncidentHistory'); - await conditionalIntercept(page, '**/graphql', (req) => req.postDataJSON().operationName == 'FindUsers', { - data: { - users: [ - { userId: '1', first_name: 'Sean', last_name: 'McGregor', roles: [] }, - { userId: '2', first_name: 'Pablo', last_name: 'Costa', roles: [] }, - ], - }, - }, 'FindUsers'); - await conditionalIntercept(page, '**/graphql', (req) => req.postDataJSON().operationName == 'FindEntities', { - data: { - entities: [ - { __typename: 'Entity', entity_id: 'youtube', name: 'Youtube' }, - { __typename: 'Entity', entity_id: 'google', name: 'Google' }, - { __typename: 'Entity', entity_id: 'tesla', name: 'Tesla' }, - ], - }, - }, 'FindEntities'); - await conditionalIntercept(page, '**/graphql', (req) => req.postDataJSON().operationName == 'UpdateIncident', { data: { updateOneIncident: { incident_id: 10 } } }, 'UpdateIncident'); - await conditionalIntercept(page, '**/graphql', (req) => req.postDataJSON().operationName == 'logIncidentHistory', { data: { logIncidentHistory: { incident_id: 10 } } }, 'logIncidentHistory'); - - await Promise.all([ - waitForRequest('FindIncidentHistory'), - waitForRequest('FindEntities'), - waitForRequest('FindUsers') - ]); - - await page.locator('h2').getByText('Version History').waitFor(); - - const rows = await page.locator('[data-cy="history-row"]').elementHandles(); - expect(rows.length).toBe(4); - - for (let [index, history] of incidentHistory.data.history_incidents.entries()) { - const row = rows[index]; - await row.evaluate((node, epoch_date_modified) => { - const formattedDate = new Date(epoch_date_modified * 1000).toISOString().slice(0, 16).replace('T', ' '); - return node.textContent.includes(formattedDate); - }, history.epoch_date_modified); - await row.evaluate((node, history) => node.textContent.includes(`Modified by: ${history.modifiedBy === '1' ? 'Sean McGregor' : 'Pablo Costa'}`), history); - if (index !== 0) { - const restoreButton = await row.$('[data-cy="restore-button"]'); - if (restoreButton) { - await restoreButton.waitForElementState('visible'); - } - } - } - - const lastRow = rows[incidentHistory.data.history_incidents.length - 1]; - await lastRow.evaluate((node) => node.textContent.includes('Initial version')); - - const now = new Date(); - await page.context().addInitScript(`Date = class extends Date { - constructor() { - super(${now.getTime()}); - } - };`); - - const restoreButton = await lastRow.$('[data-cy="restore-button"]'); - - // Listen for the dialog and handle it - page.once('dialog', async dialog => { - await dialog.accept(); - }); - - if (restoreButton) { - await restoreButton.click(); - } - - await page.getByTestId("restoring-message").scrollIntoViewIfNeeded(); - await expect(page.getByTestId("restoring-message")).toBeVisible({ timeout: 30000 }); - - const initialVersion = incidentHistory.data.history_incidents[incidentHistory.data.history_incidents.length - 1]; - const updatedIncident = { - ...initialVersion, - epoch_date_modified: expect.any(Number), - editor_notes: '', - reports: { link: initialVersion.reports }, - AllegedDeployerOfAISystem: { link: initialVersion.AllegedDeployerOfAISystem }, - AllegedDeveloperOfAISystem: { link: initialVersion.AllegedDeveloperOfAISystem }, - AllegedHarmedOrNearlyHarmedParties: { link: initialVersion.AllegedHarmedOrNearlyHarmedParties }, - editors: { link: initialVersion.editors.concat(userId) }, - }; - - delete updatedIncident._id; - delete updatedIncident.modifiedBy; - - const updateIncidentRequest = await waitForRequest('UpdateIncident'); - const updateIncidentVariables = updateIncidentRequest.postDataJSON().variables; - expect(updateIncidentVariables.filter.incident_id.EQ).toBe(updatedIncident.incident_id); - expect(updateIncidentVariables.update.set).toMatchObject(updatedIncident); - - const logIncidentHistoryRequest = await waitForRequest('logIncidentHistory'); - const logIncidentHistoryInput = logIncidentHistoryRequest.postDataJSON().variables.input; - - const expectedIncident = { - ...initialVersion, - editor_notes: updatedIncident.editor_notes, - epoch_date_modified: updatedIncident.epoch_date_modified, - editors: initialVersion.editors.concat(userId), - modifiedBy: userId, - }; - - delete expectedIncident._id; - - expect(logIncidentHistoryInput).toEqual(expectedIncident); - - await page.getByText('Incident version restored successfully.').waitFor(); - await page.locator('[data-cy="restoring-message"]').waitFor({ state: 'hidden' }); - }); - - test('Should display the Version History details modal', async ({ page }) => { - - - - await page.goto(url); - - await conditionalIntercept(page, '**/graphql', (req) => req.postDataJSON().operationName == 'FindIncidentHistory', incidentHistory, 'FindIncidentHistory'); - await conditionalIntercept(page, '**/graphql', (req) => req.postDataJSON().operationName == 'FindUsers', { - data: { - users: [ - { userId: '1', first_name: 'Sean', last_name: 'McGregor', roles: [] }, - { userId: '2', first_name: 'Pablo', last_name: 'Costa', roles: [] }, - ], - }, - }, 'FindUsers'); - await conditionalIntercept(page, '**/graphql', (req) => req.postDataJSON().operationName == 'FindEntities', { - data: { - entities: [ - { __typename: 'Entity', entity_id: 'youtube', name: 'Youtube' }, - { __typename: 'Entity', entity_id: 'google', name: 'Google' }, - { __typename: 'Entity', entity_id: 'tesla', name: 'Tesla' }, - ], - }, - }, 'FindEntities'); - const filteredVersionReports = { - data: { reports: versionReports.data.reports.filter((report: any) => incidentHistory.data.history_incidents[1].reports.includes(report.report_number)) } - }; - await conditionalIntercept(page, '**/graphql', (req) => req.postDataJSON().operationName == 'FindReports' && req.postDataJSON().variables.filter?.report_number.IN.length === 1, filteredVersionReports, 'FindReportsV1'); - - await Promise.all([ - waitForRequest('FindIncidentHistory'), - waitForRequest('FindEntities'), - waitForRequest('FindUsers') - ]); - - const rows = await page.locator('[data-cy="history-row"]'); - - await rows.nth(1).locator('[data-cy="view-full-version-button"]').click(); - - const findReportsRequestV1 = await waitForRequest('FindReportsV1'); - expect(findReportsRequestV1.postDataJSON().variables.filter).toEqual({ - report_number: { IN: incidentHistory.data.history_incidents[1].reports, } - }); - - const modal = page.locator('[data-cy="version-view-modal"]'); - await modal.waitFor(); - - const version1 = incidentHistory.data.history_incidents[1]; - await modal.getByText('View Version details').waitFor(); - await modal.getByText(version1.title).waitFor(); - await modal.getByText('Modified by: Sean McGregor').waitFor(); - await modal.getByText(`Modified on: ${format(fromUnixTime(version1.epoch_date_modified), 'yyyy-MM-dd hh:mm a')}`).waitFor(); - await modal.getByText(`Description: ${version1.description}`).waitFor(); - if (version1.editor_notes) { - await modal.getByText(`Editor Notes: ${version1.editor_notes}`).waitFor(); - } - await modal.locator('[data-cy="alleged-entities"]').getByText('Alleged: developed an AI system deployed by Youtube, which harmed Google.').waitFor(); - await modal.locator('[data-cy="citation"]').getByText(`${version1.incident_id}`).waitFor(); - await modal.locator('[data-cy="citation"]').getByText(`${version1.reports.length}`, { exact: true }).waitFor(); - await modal.locator('[data-cy="citation"]').getByText(`${version1.date}`).waitFor(); - await modal.locator('[data-cy="citation"]').getByText('Sean McGregor, Pablo Costa').waitFor(); - await modal.locator('button').getByText('Close').click(); - await modal.waitFor({ state: 'hidden' }); - - const filteredVersionReportsV0 = { - data: { - reports: versionReports.data.reports.filter((report: any) => incidentHistory.data.history_incidents[0].reports.includes(report.report_number)) - } - }; - await conditionalIntercept(page, '**/graphql', (req) => req.postDataJSON().operationName == 'FindReports' && req.postDataJSON().variables.filter?.report_number.IN.length === 2, filteredVersionReportsV0, 'FindReportsV0'); - - await rows.nth(0).locator('[data-cy="view-full-version-button"]').click(); - - const findReportsRequestV0 = await waitForRequest('FindReportsV0'); - expect(findReportsRequestV0.postDataJSON().variables.filter).toEqual({ - report_number: { IN: incidentHistory.data.history_incidents[0].reports }, - }); - - await modal.waitFor(); - - const version0 = incidentHistory.data.history_incidents[0]; - await modal.getByText('View Version details').waitFor(); - await modal.getByText(version0.title).waitFor(); - await modal.getByText('Modified by: Sean McGregor').waitFor(); - await modal.getByText(`Modified on: ${format(fromUnixTime(version0.epoch_date_modified), 'yyyy-MM-dd hh:mm a')}`).waitFor(); - await modal.getByText(`Description: ${version0.description}`).waitFor(); - if (version0.editor_notes) { - await modal.getByText(`Editor Notes: ${version0.editor_notes}`).waitFor(); - } - await modal.locator('[data-cy="alleged-entities"]').getByText('Alleged: developed an AI system deployed by Youtube, which harmed Google.').waitFor(); - await modal.locator('[data-cy="citation"]').getByText(`${version0.incident_id}`).waitFor(); - await modal.locator('[data-cy="citation"]').getByText(`${version0.reports.length}`, { exact: true }).waitFor(); - await modal.locator('[data-cy="citation"]').getByText(`${version0.date}`).waitFor(); - await modal.locator('[data-cy="citation"]').getByText('Sean McGregor, Pablo Costa').waitFor(); - await modal.locator('button').getByText('Close').click(); - await modal.waitFor({ state: 'hidden' }); - }); -}); diff --git a/site/gatsby-site/playwright/e2e/integration/rollbar.spec.ts b/site/gatsby-site/playwright/e2e/integration/rollbar.spec.ts new file mode 100644 index 0000000000..92c590cc49 --- /dev/null +++ b/site/gatsby-site/playwright/e2e/integration/rollbar.spec.ts @@ -0,0 +1,24 @@ +import { expect } from "@playwright/test"; +import { test } from "../../utils"; +import config from "../../config"; + +test('Should log an error to Rollbar', async ({ page, login }) => { + const rollbarAPICall = page.waitForRequest('https://api.rollbar.com/api/1/item/'); + + await page.goto('/login'); + + await page.locator('input[name="email"]').fill(config.E2E_ADMIN_USERNAME); + await page.locator('input[name="password"]').fill('invalidPassword'); + await page.locator('[data-cy="login-btn"]').click(); + + const rollbarRequest = await rollbarAPICall; + const response = await rollbarRequest.response(); + + if (response) { + expect(response.status()).toBe(200); + const responseBody = await response. json(); + expect(responseBody.err).toBe(0); + } else { + throw new Error('No response received for Rollbar API call.'); + } +}); diff --git a/site/gatsby-site/playwright/e2e/integration/snapshots.spec.ts b/site/gatsby-site/playwright/e2e/integration/snapshots.spec.ts new file mode 100644 index 0000000000..9657e41582 --- /dev/null +++ b/site/gatsby-site/playwright/e2e/integration/snapshots.spec.ts @@ -0,0 +1,34 @@ +import { expect } from '@playwright/test'; +import { test } from '../../utils'; + +test.describe('The Database Snapshots Page', () => { + const url = '/research/snapshots'; + + test('Successfully loads', async ({ page }) => { + await page.goto(url); + }); + + test( + 'Should display a list of snapshots to download', + async ({ page, skipOnEmptyEnvironment }) => { + await page.goto(url); + + const snapshotListItems = page.locator('[data-cy="snapshots-list"] li'); + await snapshotListItems.first().waitFor(); + + const count = await snapshotListItems.count(); + expect(count).toBeGreaterThan(0); + + for (let i = 0; i < count; i++) { + const item = snapshotListItems.nth(i); + const textContent = await item.textContent(); + expect(textContent).toMatch( + /^\d{4}-\d{2}-\d{2} \d{1,2}:\d{2} (AM|PM) · \d+(\.\d{2})? MB · backup-\d{14}\.tar\.bz2$/ + ); + + const href = await item.locator('a').getAttribute('href'); + expect(href).toMatch(/^https:\/\/.*\/backup-\d{14}\.tar\.bz2$/); + } + } + ); +}); diff --git a/site/gatsby-site/playwright/e2e/landingPage.spec.ts b/site/gatsby-site/playwright/e2e/landingPage.spec.ts index b7f01338a4..0e0809aef7 100644 --- a/site/gatsby-site/playwright/e2e/landingPage.spec.ts +++ b/site/gatsby-site/playwright/e2e/landingPage.spec.ts @@ -101,23 +101,48 @@ test.describe('The Landing page', () => { ); test('Loads the random incidents carousel', async ({ page, skipOnEmptyEnvironment }) => { - await page.goto('/'); - await page.locator('[data-cy="random-incidents-carousel"]').scrollIntoViewIfNeeded(); - await expect(page.locator('[data-cy="random-incidents-carousel"]')).toBeVisible(); - await expect(page.locator('[data-cy="random-incidents-carousel-item"]')).toHaveCount(5); - }); + await page.goto('/'); + await page.locator('[data-cy="random-incidents-carousel"]').scrollIntoViewIfNeeded(); + await expect(page.locator('[data-cy="random-incidents-carousel"]')).toBeVisible(); + await expect(page.locator('[data-cy="random-incidents-carousel-item"]')).toHaveCount(5); + }); test('Renders commit sha in the footer', async ({ page }) => { await page.goto('/'); await expect(page.locator('[data-cy="commit-sha"]')).toBeVisible(); }); - test.skip('Should load sidebar', async ({ page }) => { + test('Should load sidebar', async ({ page, skipOnEmptyEnvironment }) => { await page.goto('/'); - const sidebarTree = page.locator('[data-cy="sidebar-desktop"]'); - const lis = await sidebarTree.locator('ul[data-cy="sidebar-tree"] > li').count(); + const sidebarTree = page.locator('[data-testid="sidebar-desktop"]'); + const lis = await sidebarTree.locator('ul[data-testid="sidebar-tree"] > li').count(); await expect(lis).toBeGreaterThan(0); }); + test('Should collapse sidebar', async ({ page, skipOnEmptyEnvironment }) => { + await page.goto('/'); + const sidebarTree = page.locator('[data-testid="sidebar-desktop"]'); + await sidebarTree.locator('[data-cy="collapse-button"]').click(); + const sidebar = await page.locator('[id="sidebar"]').first(); + let hasClass = await sidebar.evaluate((el, className) => { + return el.classList.contains(className); + }, 'collapsed'); + expect(hasClass).toBe(true); + await sidebarTree.locator('[data-cy="collapse-button"]').click(); + hasClass = await sidebar.evaluate((el, className) => { + return el.classList.contains(className); + }, 'collapsed'); + expect(hasClass).toBe(false); + }); + + test('Should display sidebar tooltip when collapsed', async ({ page, skipOnEmptyEnvironment }) => { + await page.goto('/'); + const sidebarTree = page.locator('[data-testid="sidebar-desktop"]'); + await sidebarTree.locator('[data-cy="collapse-button"]').click(); + // Hovers over a sidebar item + await page.getByTestId('sidebar-desktop').getByTestId("sidebar-welcome").hover(); + await expect(page.getByTestId('sidebar-desktop').locator('[data-testid="flowbite-tooltip"]', { hasText: 'Welcome to the AIID' })) + .toBeVisible(); + }); }); diff --git a/site/gatsby-site/playwright/e2e/navigation.spec.ts b/site/gatsby-site/playwright/e2e/navigation.spec.ts index b27595873a..5cfb29f14e 100644 --- a/site/gatsby-site/playwright/e2e/navigation.spec.ts +++ b/site/gatsby-site/playwright/e2e/navigation.spec.ts @@ -33,7 +33,7 @@ test.describe('Navigation', () => { await expect(page.locator('.rightSideTitle')).toHaveText('CONTENTS'); const sidebarElementsCount = await page.locator('.rightSideBarUL li').count(); await expect(sidebarElementsCount).toBeGreaterThan(0); - await page.getByTestId('sidebar-desktop').locator('#sidebar > [data-testid="sidebar-tree"] > [data-testid="sidebar-welcome"] > [data-testid="sidebar-link"]').click(); + await page.getByTestId('sidebar-desktop').locator('#sidebar > [data-testid="sidebar-tree"] > [data-testid="sidebar-welcome"] [data-testid="sidebar-link"]').click(); await expect(page.locator('.rightSideTitle')).not.toBeVisible(); }); diff --git a/site/gatsby-site/playwright/e2e/reportHistory.spec.ts b/site/gatsby-site/playwright/e2e/reportHistory.spec.ts deleted file mode 100644 index 9d42302974..0000000000 --- a/site/gatsby-site/playwright/e2e/reportHistory.spec.ts +++ /dev/null @@ -1,377 +0,0 @@ -import { test, conditionalIntercept, waitForRequest, query } from '../utils'; -import { format, fromUnixTime, getUnixTime } from 'date-fns'; -import reportHistory from '../fixtures/history/reportHistory.json'; -import updateOneReport from '../fixtures/reports/updateOneReport.json'; -import supportedLanguages from '../../src/components/i18n/languages.json'; -import { expect } from '@playwright/test'; -import config from '../config'; - -test.describe('Report History', () => { - const url = '/cite/history?report_number=3206&incident_id=563'; - - test('Successfully loads', async ({ page }) => { - await page.goto(url); - }); - - test('Should display the Report Version History table data', async ({ page }) => { - await page.goto(url); - - await conditionalIntercept( - page, - '**/graphql', - (req) => req.postDataJSON().operationName == 'FindReportHistory', - reportHistory, - 'FindReportHistory' - ); - - await conditionalIntercept( - page, - '**/graphql', - (req) => req.postDataJSON().operationName == 'FindUsers', - { - data: { - users: [ - { userId: '1', first_name: 'Sean', last_name: 'McGregor', roles: [] }, - { userId: '2', first_name: 'Pablo', last_name: 'Costa', roles: [] }, - ], - }, - }, - 'FindUsers' - ); - - await Promise.all([ - waitForRequest('FindReportHistory'), - waitForRequest('FindUsers') - ]); - - await expect(page.getByText('Version History')).toBeVisible({ timeout: 30000 }); - - const rows = await page.locator('[data-cy="history-row"]').count(); - expect(rows).toBe(9); - - for (let index = 0; index < reportHistory.data.history_reports.length; index++) { - const history = reportHistory.data.history_reports[index]; - await expect(page.locator('[data-cy="history-row"]').nth(index)).toContainText( - `${format(fromUnixTime(history.epoch_date_modified), 'yyyy-MM-dd hh:mm a')}` - ); - await expect(page.locator('[data-cy="history-row"]').nth(index)).toContainText( - `Modified by: ${history.modifiedBy === '1' ? 'Sean McGregor' : 'Pablo Costa'}` - ); - } - - await expect(page.locator('[data-cy="history-row"]').last()).toContainText('Initial version'); - }); - - test('Should not display the Report Version History table data if no data is present', async ({ page }) => { - await page.goto(url); - - await conditionalIntercept( - page, - '**/graphql', - (req) => req.postDataJSON().operationName == 'FindReportHistory', - { data: { history_reports: [] } }, - 'FindReportHistory' - ); - - await conditionalIntercept( - page, - '**/graphql', - (req) => req.postDataJSON().operationName == 'FindUsers', - { - data: { - users: [ - { userId: '1', first_name: 'Sean', last_name: 'McGregor', roles: [] }, - { userId: '2', first_name: 'Pablo', last_name: 'Costa', roles: [] }, - ], - }, - }, - 'FindUsers' - ); - - await Promise.all([ - waitForRequest('FindReportHistory'), - waitForRequest('FindUsers') - ]); - - await expect(page.getByText('Version History')).not.toBeVisible(); - await expect(page.locator('[data-cy="history-table"]')).not.toBeVisible(); - await expect(page.getByText('There are no version history records for this Report')).toBeVisible({ timeout: 30000 }); - }); - - test('Should go back to the Incident Report', async ({ page }) => { - await page.goto(url); - await expect(page.getByText('Back to Report 3206')).toBeVisible({ timeout: 30000 }); - await page.getByText('Back to Report 3206').click(); - await page.waitForURL('/cite/563/#r3206'); - }); - - test('Should go back to the Issue Report', async ({ page }) => { - await page.goto('/cite/history?report_number=3206&incident_id=null'); - await page.getByText('Back to Report 3206').click(); - await page.waitForURL('/reports/3206/'); - }); - - test('Should refresh Report history if the user go back on the browser', async ({ page, skipOnEmptyEnvironment }) => { - await page.goto('/cite/3/'); - - await page.locator('button:has-text("Read More")').first().click(); - - await conditionalIntercept( - page, - '**/graphql', - (req) => req.postDataJSON().operationName === 'FindReportHistory', - reportHistory, - 'FindReportHistory' - ); - - await page.locator('[data-cy="report-history-button"]').click(); - - await waitForRequest('FindReportHistory'); - - await expect(page).toHaveURL('/cite/history/?report_number=376&incident_id=3'); - - await page.goBack(); - - await expect(page).toHaveURL('/cite/3/'); - - await page.goForward(); - - await waitForRequest('FindReportHistory'); - }); - - test('Should not be able to restore a version if the user does not have the right permissions', async ({ page }) => { - await page.goto(url); - await expect(page.getByText('Restore Version')).not.toBeVisible(); - }); - - test('Should restore a Report previous version', async ({ page, login }) => { - const [userId] = await login(config.E2E_ADMIN_USERNAME, config.E2E_ADMIN_PASSWORD); - - await page.goto(url); - - await conditionalIntercept( - page, - '**/graphql', - (req) => req.postDataJSON().operationName == 'FindReport', - { - data: { - report: reportHistory.data.history_reports[0], - }, - }, - 'FindReport' - ); - - await conditionalIntercept( - page, - '**/graphql', - (req) => req.postDataJSON().operationName == 'FindReportHistory', - reportHistory, - 'FindReportHistory' - ); - - await conditionalIntercept( - page, - '**/graphql', - (req) => req.postDataJSON().operationName == 'FindUsers', - { - data: { - users: [ - { userId: '1', first_name: 'Sean', last_name: 'McGregor', roles: [] }, - { userId: '2', first_name: 'Pablo', last_name: 'Costa', roles: [] }, - ], - }, - }, - 'FindUsers' - ); - - await conditionalIntercept( - page, - '**/graphql', - (req) => req.postDataJSON().operationName == 'UpdateReport', - updateOneReport, - 'UpdateReport' - ); - - await conditionalIntercept( - page, - '**/graphql', - (req) => req.postDataJSON().operationName == 'logReportHistory', - { data: { logReportHistory: { report_number: 3206 } } }, - 'logReportHistory' - ); - - await Promise.all([ - waitForRequest('FindReportHistory'), - waitForRequest('FindUsers'), - waitForRequest('FindReport') - ]); - - for (let index = 0; index < reportHistory.data.history_reports.length; index++) { - if (index === 0) { - await expect(page.locator('[data-cy="history-row"]').nth(index).locator('[data-cy="restore-button"]')).not.toBeVisible(); - } else { - await expect(page.locator('[data-cy="history-row"]').nth(index).locator('[data-cy="restore-button"]')).toBeVisible(); - } - } - - const now = new Date(); - await page.context().addInitScript(`Date = class extends Date { - constructor() { - super(${now.getTime()}); - } - };`); - - // Listen for the dialog and handle it - page.once('dialog', async dialog => { - await dialog.accept(); - }); - - await page.locator('[data-cy="history-row"]').last().locator('[data-cy="restore-button"]').click(); - await page.getByTestId('restoring-message').scrollIntoViewIfNeeded(); - await expect(page.getByTestId("restoring-message")).toBeVisible({ timeout: 30000 }); - - const initialVersion = reportHistory.data.history_reports[reportHistory.data.history_reports.length - 1]; - - const updatedReport = { - ...initialVersion, - epoch_date_modified: getUnixTime(new Date()), - editor_notes: '', - }; - - delete updatedReport._id; - delete updatedReport.modifiedBy; - delete updatedReport.user; - delete updatedReport.embedding.__typename; - - const updateReportRequest = await waitForRequest('UpdateReport'); - const variables = updateReportRequest.postDataJSON().variables; - expect(variables.filter.report_number.EQ).toBe(updatedReport.report_number); - expect(variables.update.set).toEqual(updatedReport); - - const logReportHistoryRequest = await waitForRequest('logReportHistory'); - const input = logReportHistoryRequest.postDataJSON().variables.input; - - const expectedReport = { - ...initialVersion, - editor_notes: updatedReport.editor_notes, - epoch_date_modified: updatedReport.epoch_date_modified, - modifiedBy: userId, - }; - - delete expectedReport._id; - delete expectedReport.user; - - expect(input).toEqual(expectedReport); - - await page.locator('[data-cy="toast"]').waitFor({ timeout: 60000 }); - await expect(page.locator('[data-cy="toast"]')).toHaveText('Report version restored successfully.'); - - await expect(page.locator('[data-cy="restoring-message"]')).not.toBeVisible(); - - await waitForRequest('FindReportHistory'); - }); - - test('Should display the Report Version History details modal', async ({ page }) => { - await page.goto(url); - - await conditionalIntercept( - page, - '**/graphql', - (req) => req.postDataJSON().operationName == 'FindReport', - { - data: { - report: reportHistory.data.history_reports[0], - }, - }, - 'FindReport' - ); - - await conditionalIntercept( - page, - '**/graphql', - (req) => req.postDataJSON().operationName == 'FindReportHistory', - reportHistory, - 'FindReportHistory' - ); - - await conditionalIntercept( - page, - '**/graphql', - (req) => req.postDataJSON().operationName == 'FindUsers', - { - data: { - users: [ - { - userId: '1', first_name: 'Sean', last_name: - - 'McGregor', roles: [] - }, - { userId: '2', first_name: 'Pablo', last_name: 'Costa', roles: [] }, - ], - }, - }, - 'FindUsers' - ); - - await Promise.all([ - waitForRequest('FindReport'), - waitForRequest('FindReportHistory'), - waitForRequest('FindUsers') - ]); - - await page.locator('[data-cy="history-row"]').nth(1).locator('[data-cy="view-full-version-button"]').click(); - - await expect(page.locator('[data-cy="version-view-modal"]')).toBeVisible(); - - const version = reportHistory.data.history_reports[1]; - - await expect(page.getByText('Version details')).toBeVisible(); - await expect(page.getByText(`Modified on: ${format(fromUnixTime(version.epoch_date_modified), 'yyyy-MM-dd hh:mm a')}`)).toBeVisible(); - await expect(page.locator('[data-cy="version-view-modal"] div').filter({ hasText: 'Modified by: Sean McGregor' }).first()).toBeVisible(); - - - const fields = [ - { label: 'Report Address:', value: version.url }, - { label: 'Title:', value: version.title }, - { label: 'Author CSV:', value: version.authors.join(', ') }, - { label: 'Submitter CSV:', value: version.submitters.join(', ') }, - { label: 'Date Published:', value: version.date_published }, - { label: 'Date Downloaded:', value: version.date_downloaded }, - { label: 'Image Address:', value: version.image_url }, - { label: 'Text:', value: 'By Aimee Picchi' }, - { label: 'Language:', value: supportedLanguages.find((l) => l.code == version.language)?.name }, - { label: 'Tags:', value: version.tags.join(', ') }, - { label: 'Editor Notes:', value: version.editor_notes } - ]; - - for (const field of fields) { - await expect(page.getByText(field.label)).toBeVisible(); - await expect(page.locator('[data-cy="version-view-modal"] div').filter({ hasText: field.value }).first()).toBeVisible(); - - } - - await expect(page.locator('[data-cy="version-view-modal"] [data-cy="cloudinary-image"]').first()).toHaveAttribute('src', expect.stringContaining(version.cloudinary_id)); - - await page.locator('[data-cy="version-view-modal"] button').filter({ hasText: 'Close' }).click(); - await expect(page.locator('[data-cy="version-view-modal"]')).not.toBeVisible(); - - await page.locator('[data-cy="history-row"]').first().locator('[data-cy="view-full-version-button"]').click(); - - await expect(page.locator('[data-cy="version-view-modal"]')).toBeVisible(); - - const latestVersion = reportHistory.data.history_reports[0]; - - await expect(page.getByText('Version details')).toBeVisible(); - await expect(page.getByText(`Modified on: ${format(fromUnixTime(latestVersion.epoch_date_modified), 'yyyy-MM-dd hh:mm a')}`)).toBeVisible(); - await expect(page.locator('[data-cy="version-view-modal"] div').filter({ hasText: 'Modified by: Sean McGregor' }).first()).toBeVisible(); - - for (const field of fields) { - await expect(page.getByText(field.label)).toBeVisible(); - await expect(page.locator('[data-cy="version-view-modal"] div.col-span-6').filter({ hasText: 'Is Starbucks shortchanging its baristas? v2' }).first()).toBeVisible(); - } - - await expect(page.locator('[data-cy="version-view-modal"] [data-cy="cloudinary-image"]').nth(0)).toHaveAttribute('src', expect.stringContaining(version.cloudinary_id)); - await page.locator('[data-cy="version-view-modal"] button').filter({ hasText: 'Close' }).click(); - await expect(page.locator('[data-cy="version-view-modal"]')).not.toBeVisible(); - }); -}); \ No newline at end of file diff --git a/site/gatsby-site/playwright/e2e-full/signup.spec.ts b/site/gatsby-site/playwright/e2e/signup.spec.ts similarity index 97% rename from site/gatsby-site/playwright/e2e-full/signup.spec.ts rename to site/gatsby-site/playwright/e2e/signup.spec.ts index a80d36a34b..cf6ff60cf1 100644 --- a/site/gatsby-site/playwright/e2e-full/signup.spec.ts +++ b/site/gatsby-site/playwright/e2e/signup.spec.ts @@ -38,7 +38,8 @@ test.describe('Signup', () => { await expect(page.locator('[data-cy="toast"]').getByText(`Verification email sent to ${email}`)).toBeVisible(); }); - test('Should display the error toast message if the user already exists', async ({ page, skipOnEmptyEnvironment }) => { + // Adding the login fixture to the test to skip it on production + test('Should display the error toast message if the user already exists', async ({ page, skipOnEmptyEnvironment, login }) => { await page.goto(url); await page.locator('[data-cy="signup-btn"]').click(); diff --git a/site/gatsby-site/playwright/e2e/translationBadge.spec.ts b/site/gatsby-site/playwright/e2e/translationBadge.spec.ts deleted file mode 100644 index d9e756aa83..0000000000 --- a/site/gatsby-site/playwright/e2e/translationBadge.spec.ts +++ /dev/null @@ -1,28 +0,0 @@ -import { expect } from '@playwright/test'; -import { test } from '../utils'; - -test.describe('Translation Badges', () => { - test('Should be visible on blog post', async ({ page }) => { - await page.goto('/es/blog/using-ai-to-connect-ai-incidents'); - await expect(page.locator('[data-cy="translation-badge"]').getByText('Traducido por IA')).toBeVisible(); - await expect(page.locator('a', { hasText: 'Ver Original' })).toBeVisible(); - await expect(page.locator('a', { hasText: 'Ver Original' })).toHaveAttribute('href', '/blog/using-ai-to-connect-ai-incidents/'); - }); - - test('Should be visible on the discover app', async ({ page, skipOnEmptyEnvironment }) => { - await page.goto('/es/apps/discover?display=details&incident_id=1&page=1&source_domain=today.com'); - await expect(page.locator('[data-cy="5d34b8c29ced494f010ed45c"]').locator('[data-cy="translation-badge"]').getByText('Traducido por IA')).toBeVisible(); - }); - - test('Should be visible on an incident card on the citation page', async ({ page, skipOnEmptyEnvironment }) => { - await page.goto('/es/cite/1#r1'); - await expect(page.locator('#r1').locator('[data-cy="translation-badge"]').getByText('Traducido por IA')).toBeVisible(); - }); - - test('Should be visible on documentation pages', async ({ page }) => { - await page.goto('/es/about_apps'); - await expect(page.locator('[data-cy="translation-badge"]').getByText('Traducido por IA')).toBeVisible(); - await expect(page.locator('a', { hasText: 'Ver Original' })).toBeVisible(); - await expect(page.locator('a', { hasText: 'Ver Original' })).toHaveAttribute('href', '/about_apps/'); - }); -}); diff --git a/site/gatsby-site/playwright/e2e/unit/AlgoliaUpdater.spec.ts b/site/gatsby-site/playwright/e2e/unit/AlgoliaUpdater.spec.ts new file mode 100644 index 0000000000..9e270f62c6 --- /dev/null +++ b/site/gatsby-site/playwright/e2e/unit/AlgoliaUpdater.spec.ts @@ -0,0 +1,246 @@ +import { expect } from '@playwright/test'; +import sinon from 'sinon'; +import { ObjectId } from 'bson'; +import AlgoliaUpdater from '../../../src/utils/AlgoliaUpdater'; +import { test } from '../../utils'; + +test('Should update translations to Algolia', async ({ page }) => { + const incidents = [ + { + incident_id: 1, + date: '2020-06-14', + reports: [1, 23], + }, + ]; + + const reports = [ + { + _id: new ObjectId('60dd465f80935bc89e6f9b01'), + authors: ['Alistair Barr'], + date_downloaded: '2019-04-13', + date_modified: '2020-06-14', + date_published: '2015-05-19', + date_submitted: '2019-06-01', + description: 'Description of report 1', + epoch_date_downloaded: 1555113600, + epoch_date_modified: 1592092800, + epoch_date_published: 1431993600, + epoch_date_submitted: 1559347200, + image_url: 'http://url.com', + cloudinary_id: 'http://cloudinary.com', + language: 'en', + report_number: 1, + source_domain: 'blogs.wsj.com', + submitters: ['Roman Yampolskiy'], + tags: [], + text: 'Report 1 text', + plain_text: 'Report 1 text', + title: 'Report 1 title', + url: 'https://url.com/stuff', + }, + { + _id: new ObjectId('60dd465f80935bc89e6f9b02'), + authors: ['Alistair Barr'], + date_downloaded: '2019-04-13', + date_modified: '2020-06-14', + date_published: '2015-05-19', + date_submitted: '2019-06-01', + description: 'Description of report 23', + epoch_date_downloaded: 1555113600, + epoch_date_modified: 1592092800, + epoch_date_published: 1431993600, + epoch_date_submitted: 1559347200, + image_url: 'http://url.com', + cloudinary_id: 'http://cloudinary.com', + language: 'es', + report_number: 23, + source_domain: 'blogs.wsj.com', + submitters: ['Roman Yampolskiy'], + tags: [], + text: 'Report 23 text', + plain_text: 'Report 23 text', + title: 'Report 23 title', + url: 'https://url.com/stuff', + }, + ]; + + const classifications = [ + { + _id: '60dd465f80935bc89e6f9b00', + incidents: [1], + reports: [], + namespace: 'CSETv0', + attributes: [ + { short_name: 'Named Entities', value_json: '["Amazon"]' }, + { + short_name: 'Harm Type', + value_json: '["Harm to physical health/safety", "Harm to physical property"]', + }, + { short_name: 'Publish', value_json: 'true' }, + ], + notes: null, + }, + { + _id: '60dd465f80935bc89e6f9b01', + incidents: [], + reports: [], + namespace: 'SHOULD NOT BE INCLUDED', + attributes: [{ short_name: 'Something', value_json: '"Great"' }], + notes: 'Nothing to see here', + }, + ]; + + const translatedReportsEN = [ + { + _id: '61d5ad9f102e6e30fca9065r', + text: 'translated-en-text **report 23**', + plain_text: 'translated-en-text report 23', + title: 'translated-en-title report 23', + report_number: 23, + }, + ]; + + const translatedReportsES = [ + { + _id: '61d5ad9f102e6e30fca90ddf', + text: 'translated-es-text **report 1**', + plain_text: 'translated-es-text report 1', + title: 'translated-es-title report 1', + report_number: 1, + }, + ]; + + const duplicates = [ + { + duplicate_incident_number: 247, + true_incident_number: 246, + }, + ]; + + const reporter = { log: sinon.stub() }; + + const mongoClientStub = { + connect: sinon.stub().resolves(), + close: sinon.stub().resolves(), + db: sinon.stub().returns({ + collection: (name: string) => { + if (name === 'reports') { + return { + find: sinon.stub().returns({ + toArray: sinon.stub().resolves(reports), + }), + }; + } + if (name === 'reports_en') { + return { + find: sinon.stub().returns({ + toArray: sinon.stub().resolves(translatedReportsEN), + }), + }; + } + if (name === 'reports_es') { + return { + find: sinon.stub().returns({ + toArray: sinon.stub().resolves(translatedReportsES), + }), + }; + } + if (name === 'classifications') { + return { + find: sinon.stub().returns({ + toArray: sinon.stub().resolves(classifications), + }), + }; + } + if (name === 'incidents') { + return { + find: sinon.stub().returns({ + toArray: sinon.stub().resolves(incidents), + }), + }; + } + if (name === 'duplicates') { + return { + find: sinon.stub().returns({ + toArray: sinon.stub().resolves(duplicates), + }), + }; + } + return null; + }, + }), + }; + + const esIndex = { + replaceAllObjects: sinon.stub().resolves({ objectIDs: ['1', '2'] }), + setSettings: sinon.stub().resolves({}), + deleteBy: sinon.stub().resolves({}), + }; + + const enIndex = { + replaceAllObjects: sinon.stub().resolves({ objectIDs: ['1', '2'] }), + setSettings: sinon.stub().resolves({}), + deleteBy: sinon.stub().resolves({}), + }; + + const esIndexReplica = { + setSettings: sinon.stub().resolves({}), + }; + + const algoliaClientStub = { + initIndex: sinon.stub().callsFake((indexName: string) => { + if (indexName === 'instant_search-es') return esIndex; + if (indexName === 'instant_search-en') return enIndex; + + if (indexName === 'instant_search-es-featured' || indexName === 'instant_search-en-featured') { + return esIndexReplica; + } + + if ( + [ + 'instant_search-es_epoch_incident_date_desc', + 'instant_search-en_epoch_incident_date_desc', + 'instant_search-es_epoch_incident_date_asc', + 'instant_search-en_epoch_incident_date_asc', + 'instant_search-es_epoch_date_published_desc', + 'instant_search-en_epoch_date_published_desc', + 'instant_search-es_epoch_date_published_asc', + 'instant_search-en_epoch_date_published_asc', + 'instant_search-es_epoch_date_submitted_desc', + 'instant_search-en_epoch_date_submitted_desc', + 'instant_search-es_epoch_date_submitted_asc', + 'instant_search-en_epoch_date_submitted_asc', + ].includes(indexName) + ) { + return esIndexReplica; + } + + return null; + }), + }; + + const updater = new AlgoliaUpdater({ + mongoClient: mongoClientStub, + algoliaClient: algoliaClientStub, + languages: [{ code: 'es' }, { code: 'en' }], + reporter, + }); + + await updater.run(); + + expect(mongoClientStub.connect.calledOnce).toBeTruthy(); + expect(mongoClientStub.close.calledOnce).toBeTruthy(); + + const enIndexResult = algoliaClientStub.initIndex('instant_search-en'); + expect(enIndexResult.replaceAllObjects.calledOnce).toBeTruthy(); + + const replaceAllObjectsArgs = enIndexResult.replaceAllObjects.getCall(0).args[0]; + replaceAllObjectsArgs.forEach((entry) => { + if (entry.incident_id) { + expect(entry.incident_id).toBeDefined(); + } + }); + + const esIndexResult = algoliaClientStub.initIndex('instant_search-es'); + expect(esIndexResult.replaceAllObjects.calledOnce).toBeTruthy(); +}); diff --git a/site/gatsby-site/playwright/e2e/unit/gtag.spec.ts b/site/gatsby-site/playwright/e2e/unit/gtag.spec.ts new file mode 100644 index 0000000000..9ec3b96187 --- /dev/null +++ b/site/gatsby-site/playwright/e2e/unit/gtag.spec.ts @@ -0,0 +1,40 @@ +import { expect } from '@playwright/test'; +import config from '../../../config'; +import { test } from '../../utils'; + +const googleTrackingId = config.gatsby.gaTrackingId; + +test.describe('Google Analytics Tracking', () => { + test('Should track gtag only on production', async ({ page, runOnlyInProduction }) => { + await page.goto('/'); + + const hasGoogleTracking = await page.locator(`script[src*="https://www.googletagmanager.com/gtag/js?id=${googleTrackingId}"]`).count(); + + expect(hasGoogleTracking).toBeGreaterThan(0); + + const [trackingRequest] = await Promise.all([ + page.waitForRequest((request) => request.url().includes(`https://www.google-analytics.com`) && request.method() === 'POST'), + + ]); + + expect(trackingRequest).toBeTruthy(); + }); + + + test('Should not track gtag outside production', async ({ page, runAnywhereExceptProduction }) => { + await page.goto('/'); + + const hasGoogleTracking = await page.locator(`script[src*="https://www.googletagmanager.com/gtag/js?id=${googleTrackingId}"]`).count(); + expect(hasGoogleTracking).toBe(0); + + let trackingRequestMade = false; + page.on('request', (request) => { + if (request.url().includes(`https://www.google-analytics.com`) && request.method() === 'POST') { + trackingRequestMade = true; + } + }); + + await page.reload(); + expect(trackingRequestMade).toBe(false); + }); +}); diff --git a/site/gatsby-site/playwright/e2e/unit/translator.spec.ts b/site/gatsby-site/playwright/e2e/unit/translator.spec.ts index e998bbeb03..35b64d1725 100644 --- a/site/gatsby-site/playwright/e2e/unit/translator.spec.ts +++ b/site/gatsby-site/playwright/e2e/unit/translator.spec.ts @@ -1,12 +1,17 @@ import { ObjectId } from 'bson'; import { test } from '../../utils'; -import Translator from '../../../src/utils/Translator'; +import Translator from '../../../src/scripts/Translator'; import sinon from 'sinon'; const reports = [ { _id: new ObjectId('60dd465f80935bc89e6f9b01'), - authors: ['Alistair Barr'], + report_number: 1, + title: 'Report 1 title', + text: 'Report 1 **text**', + plain_text: 'Report 1 text', + language: 'en', + authors: ['Jhon Doe'], date_downloaded: '2019-04-13', date_modified: '2020-06-14', date_published: '2015-05-19', @@ -17,61 +22,65 @@ const reports = [ epoch_date_published: 1431993600, epoch_date_submitted: 1559347200, image_url: 'http://url.com', - language: 'en', - report_number: 1, source_domain: 'blogs.wsj.com', - submitters: ['Roman Yampolskiy'], + submitters: ['Jason Smith'], tags: [], - text: 'Report 1 **text**', - plain_text: 'Report 1 text', - title: 'Report 1 title', url: 'https://url.com/stuff', }, { _id: new ObjectId('60dd465f80935bc89e6f9b02'), - authors: ['Alistair Barr'], + report_number: 2, + title: 'Título del reporte 2', + text: 'Reporte 2 **texto**', + plain_text: 'Reporte 2 texto', + language: 'es', + authors: ['Juan Perez'], date_downloaded: '2019-04-13', date_modified: '2020-06-14', date_published: '2015-05-19', - date_submitted: '2019-06-01', - description: 'Description of report 2', + date_submitted: '2020-06-01', + description: 'Descripción del reporte 2', epoch_date_downloaded: 1555113600, epoch_date_modified: 1592092800, epoch_date_published: 1431993600, epoch_date_submitted: 1559347200, image_url: 'http://url.com', + source_domain: 'blogs.wsj.com', + submitters: ['Ramon Gomez'], + tags: [], + url: 'https://url.com/stuff', + }, + { + _id: new ObjectId('60dd465f80935bc89e6f9b03'), + report_number: 3, + title: 'Título del reporte 3', + text: 'Reporte 3 **texto**', + plain_text: 'Reporte 3 texto', language: 'es', - report_number: 2, + authors: ['Juan Perez'], + date_downloaded: '2019-04-13', + date_modified: '2020-06-14', + date_published: '2015-05-19', + date_submitted: '2021-06-01', + description: 'Descripción del reporte 3', + epoch_date_downloaded: 1555113600, + epoch_date_modified: 1592092800, + epoch_date_published: 1431993600, + epoch_date_submitted: 1559347200, + image_url: 'http://url.com', source_domain: 'blogs.wsj.com', - submitters: ['Roman Yampolskiy'], + submitters: ['Ramon Gomez'], tags: [], - text: 'Report 2 **text**', - plain_text: 'Report 2 text', - title: 'Report 2 title', url: 'https://url.com/stuff', }, ]; test('Translations - Should translate languages only if report language differs from target language', async ({ page }) => { - const translatedReportsEN = [ - { - _id: '61d5ad9f102e6e30fca90ddf', - text: 'translated-en-text report 1', - title: 'translated-en-title report 1', - report_number: 1, - }, - ]; + const translatedReportsEN = []; - const translatedReportsES = [ - { - _id: '61d5ad9f102e6e30fca90ddf', - text: 'translated-es-text report 2', - title: 'translated-es-title report 2', - report_number: 2, - }, - ]; + const translatedReportsES = []; - const reporter = { log: sinon.stub() }; + const reporter = { log: sinon.stub(), error: sinon.stub(), warn: sinon.stub() }; const reportsCollection = { find: sinon.stub().returns({ @@ -121,13 +130,23 @@ test('Translations - Should translate languages only if report language differs await translator.run(); sinon.assert.calledOnce(mongoClient.connect); + sinon.assert.calledOnce(reportsCollection.find); + sinon.assert.calledThrice(translateClient.translate); sinon.assert.calledOnce(reportsENCollection.insertMany); - sinon.assert.calledWith(reportsENCollection.insertMany, [{ - report_number: 2, - text: 'test-en-Report 2 **text**', - title: 'test-en-Report 2 title', - plain_text: 'test-en-Report 2 text\n', - }]); + sinon.assert.calledWith(reportsENCollection.insertMany, [ + { + report_number: 2, + text: 'test-en-Reporte 2 **texto**', + title: 'test-en-Título del reporte 2', + plain_text: 'test-en-Reporte 2 texto\n', + }, + { + report_number: 3, + text: 'test-en-Reporte 3 **texto**', + title: 'test-en-Título del reporte 3', + plain_text: 'test-en-Reporte 3 texto\n' + } + ]); sinon.assert.calledOnce(reportsESCollection.insertMany); sinon.assert.calledWith(reportsESCollection.insertMany, [{ report_number: 1, @@ -138,26 +157,191 @@ test('Translations - Should translate languages only if report language differs sinon.assert.calledOnce(mongoClient.close); }); -test("Translations - Shouldn't call Google's translate api if dryRun is true", async ({ page }) => { - const translatedReportsEN = [ +test("Translations - Shouldn't call Google's translate api and use translation placeholders if dryRun is true", async ({ page }) => { + const translatedReportsEN = []; + + const translatedReportsES = []; + + const reporter = { log: sinon.stub(), error: sinon.stub(), warn: sinon.stub() }; + + const reportsCollection = { + find: sinon.stub().returns({ + toArray: sinon.stub().resolves(reports), + }), + }; + + const reportsENCollection = { + find: sinon.stub().returns({ + toArray: sinon.stub().resolves(translatedReportsEN), + }), + insertMany: sinon.stub().resolves({ insertedCount: 1 }), + }; + + const reportsESCollection = { + find: sinon.stub().returns({ + toArray: sinon.stub().resolves(translatedReportsES), + }), + insertMany: sinon.stub().resolves({ insertedCount: 1 }), + }; + + const mongoClient = { + connect: sinon.stub().resolves(), + close: sinon.stub().resolves(), + db: sinon.stub().returns({ + collection: (name: string) => { + if (name === 'reports') return reportsCollection; + if (name === 'reports_en') return reportsENCollection; + if (name === 'reports_es') return reportsESCollection; + return null; + }, + }), + }; + + const translateClient = { + translate: sinon.stub().callsFake((payload, { to }) => [payload.map((p: any) => `translated-${to}-${p}`)]), + }; + + const translator = new Translator({ + mongoClient, + translateClient, + languages: [{ code: 'es' }, { code: 'en' }], + reporter, + dryRun: true, + }); + + await translator.run(); + + sinon.assert.calledOnce(mongoClient.connect); + sinon.assert.calledOnce(reportsCollection.find); + sinon.assert.notCalled(translateClient.translate); + sinon.assert.calledOnce(reportsENCollection.insertMany); + sinon.assert.calledWith(reportsENCollection.insertMany, [ + { + report_number: 2, + text: 'translated-en-Reporte 2 **texto**', + title: 'translated-en-Título del reporte 2', + plain_text: 'translated-en-Reporte 2 texto\n', + }, + { + report_number: 3, + text: 'translated-en-Reporte 3 **texto**', + title: 'translated-en-Título del reporte 3', + plain_text: 'translated-en-Reporte 3 texto\n' + } + ]); + sinon.assert.calledOnce(reportsESCollection.insertMany); + sinon.assert.calledWith(reportsESCollection.insertMany, [{ + report_number: 1, + text: 'translated-es-Report 1 **text**', + title: 'translated-es-Report 1 title', + plain_text: 'translated-es-Report 1 text\n', + }]); + sinon.assert.calledOnce(mongoClient.close); +}); + +test('Translations - Should translate reports with submission date greater than an specific report submission date', async ({ page }) => { + const submissionDateStart = '2021-01-01'; + + const translatedReportsES = []; + + const translatedReportsEN = []; + + const reporter = { log: sinon.stub(), error: sinon.stub(), warn: sinon.stub() }; + + const reportsCollection = { + find: sinon.stub().returns({ + toArray: sinon.stub().resolves(reports.filter(r => r.date_submitted > submissionDateStart)), + }), + }; + + const reportsENCollection = { + find: sinon.stub().returns({ + toArray: sinon.stub().resolves(translatedReportsEN), + }), + insertMany: sinon.stub().resolves({ insertedCount: 1 }), + }; + + const reportsESCollection = { + find: sinon.stub().returns({ + toArray: sinon.stub().resolves(translatedReportsES), + }), + insertMany: sinon.stub().resolves({ insertedCount: 1 }), + }; + + const mongoClient = { + connect: sinon.stub().resolves(), + close: sinon.stub().resolves(), + db: sinon.stub().returns({ + collection: (name: string) => { + if (name === 'reports') return reportsCollection; + if (name === 'reports_en') return reportsENCollection; + if (name === 'reports_es') return reportsESCollection; + return null; + }, + }), + }; + + const translateClient = { + translate: sinon.stub().callsFake((payload, { to }) => [payload.map((p: any) => `test-${to}-${p}`)]), + }; + + const translator = new Translator({ + mongoClient, + translateClient, + languages: [{ code: 'es' }, { code: 'en' }], + reporter, + dryRun: false, + submissionDateStart: submissionDateStart, + }); + + await translator.run(); + + sinon.assert.calledOnce(mongoClient.connect); + sinon.assert.calledOnce(reportsCollection.find); + sinon.assert.calledWith(reportsCollection.find, { date_submitted: { $gte: new Date(submissionDateStart) } }); + sinon.assert.calledOnce(translateClient.translate); + sinon.assert.calledOnce(reportsENCollection.insertMany); + sinon.assert.calledWith(reportsENCollection.insertMany, [ + { + report_number: 3, + text: 'test-en-Reporte 3 **texto**', + title: 'test-en-Título del reporte 3', + plain_text: 'test-en-Reporte 3 texto\n' + } + ]); + sinon.assert.notCalled(reportsESCollection.insertMany); + sinon.assert.calledOnce(mongoClient.close); +}); + +test('Translations - Should not translate if the report was already translated', async ({ page }) => { + const translatedReportsES = [ { _id: '61d5ad9f102e6e30fca90ddf', - text: 'translated-en-text report 1', - title: 'translated-en-title report 1', report_number: 1, + title: 'Título del reporte 1', + text: 'Reporte 1 **texto**', + plain_text: 'Reporte 1 texto', }, ]; - const translatedReportsES = [ + const translatedReportsEN = [ { - _id: '61d5ad9f102e6e30fca90ddf', - text: 'translated-es-text report 2', - title: 'translated-es-title report 2', + _id: '61d5ad9f102e6e30fca90dde', report_number: 2, + title: 'Report 2 title', + text: 'Report 2 **text**', + plain_text: 'Report 2 text', + }, + { + _id: '61d5ad9f102e6e30fca90ddf', + report_number: 3, + title: 'Report 3 title', + text: 'Report 3 **text**', + plain_text: 'Report 3 text', }, ]; - const reporter = { log: sinon.stub() }; + const reporter = { log: sinon.stub(), error: sinon.stub(), warn: sinon.stub() }; const reportsCollection = { find: sinon.stub().returns({ @@ -193,7 +377,7 @@ test("Translations - Shouldn't call Google's translate api if dryRun is true", a }; const translateClient = { - translate: sinon.stub().callsFake((payload, { to }) => [payload.map((p: any) => `translated-${to}-${p}`)]), + translate: sinon.stub().callsFake((payload, { to }) => [payload.map((p: any) => `test-${to}-${p}`)]), }; const translator = new Translator({ @@ -201,10 +385,15 @@ test("Translations - Shouldn't call Google's translate api if dryRun is true", a translateClient, languages: [{ code: 'es' }, { code: 'en' }], reporter, - dryRun: true, + dryRun: false, }); await translator.run(); + sinon.assert.calledOnce(mongoClient.connect); + sinon.assert.calledOnce(reportsCollection.find); sinon.assert.notCalled(translateClient.translate); + sinon.assert.notCalled(reportsENCollection.insertMany); + sinon.assert.notCalled(reportsESCollection.insertMany); + sinon.assert.calledOnce(mongoClient.close); }); diff --git a/site/gatsby-site/playwright/fixtures/algoliaMock.ts b/site/gatsby-site/playwright/fixtures/algoliaMock.ts new file mode 100644 index 0000000000..27c03c8816 --- /dev/null +++ b/site/gatsby-site/playwright/fixtures/algoliaMock.ts @@ -0,0 +1,7708 @@ +export const algoliaMock = { + results: [ + { + hits: [ + { + authors: ['Aimee Picchi'], + description: + "Some employees at the coffee chain say it isn't living up to promises to improve the company's labor practices", + epoch_date_downloaded: 1555113600, + epoch_date_modified: 1725431608, + epoch_date_published: 1443052800, + epoch_date_submitted: 1559347200, + image_url: + 'https://cbsnews1.cbsistatic.com/hub/i/r/2015/03/17/01a38576-5108-40f7-8df8-5416164ed878/thumbnail/1200x630/ca8d35fe6bc065b5c9a747d92bc6d94c/154211248.jpg', + language: 'en', + report_number: 23, + source_domain: 'cbsnews.com', + submitters: ['Catherine Olsson'], + title: '​Is Starbucks shortchanging its baristas?', + name: '​Is Starbucks shortchanging its baristas?', + url: 'https://www.cbsnews.com/news/is-starbucks-shortchanging-its-baristas/', + tags: [], + editor_notes: '', + cloudinary_id: + 'reports/cbsnews1.cbsistatic.com/hub/i/r/2015/03/17/01a38576-5108-40f7-8df8-5416164ed878/thumbnail/1200x630/ca8d35fe6bc065b5c9a747d92bc6d94c/154211248.jpg', + text: "For Starbucks (SBUX) barista Kylei Weisse, working at the coffee chain helps him secure health insurance and some extra money while he studies at Georgia Perimeter College. What it doesn't provide is the kind of stable schedule that the company promised its workers last year.\n\n\"It's the wild inconsistency\" of the hours that's a problem, Weisse, 32, said. \"We're supposed to get them 10 days in advance, which often happens, but there's no guarantee. If our manager doesn't get it to us on time, we just have to deal with it.\"\n\nThat became a problem recently when Weisse's manager gave him only a few days notice on his work hours, which ended up conflicting with an anatomy and physiology exam at his college. Weisse ended up paying another worker $20 to take his shift so he could take the exam.\n\nThe short notice is especially frustrating because of Starbucks' vow last year to post employees' schedules at least 10 days in advance, as well as the company's insistence that workers provide at least one-month notice when they need to take a day off.\n\nWhat's behind Starbucks price increases?\n\nWeisse isn't alone in complaining that Starbucks isn't living up to its promises to overhaul its labor practices for its roughly 130,000 baristas. That vow followed an article last year by The New York Times that detailed how workers were struggling to manage childcare and other obligations when the company provided only a few days notice about their schedules.\n\nAbout half of roughly 200 Starbucks baristas in a recent survey said they are still receiving their schedule with less than one week's notice. Others also reported being asked to handle \"clopens,\" split shifts in which employees work a closing shift late into the evening and then an early opening shift the following morning. The company last year promised to end the practice.\n\nOf course, Starbucks isn't alone in using \"just-in-time\" scheduling, with the retail and restaurant industry increasingly turning to software that allows them to change work schedules at the last minute, depending on whether business picks up or slows down. But it is Starbucks that has become a lightning rod on the issue given its vows to improve how it treats employees and its own emphatic claims to valuing workers, whom it labels \"partners.\"\n\n\"Starbucks has the values and wants to do right by their employees,\" said Carrie Gleason, director of the Fair Workweek Initiative at the Center for Popular Democracy, an advocacy group focused on workers'rights, and a co-author of the group's new report on the company's labor practices. \"However, since last year when the company recognized there was a serious problem with the way it scheduled workers and pledged to reform, still so many of the same issues persist.\"\n\nStarbucks didn't respond to requests for comment on the study or on baristas' reports of labor practices that are failing to meet the company's stated goals.\n\nIn an internal memo this week published by Time, Starbucks executive Cliff Burrows wrote that the company couldn't validate the survey, but added that \"the findings suggest, contrary to the expectations we have in place, that some partners are receiving their schedules less than one week in advance and that there is a continuing issue with some partners working a close and then an opening shift the following morning.\" He asks store managers \"to go the extra mile to ensure partners have a consistent schedule.\"\n\nStarbucks ends \"race together\" campaign amid public backlash\n\nTo be sure, some Starbucks workers are receiving at least 10 days notice on their work hours, with the survey finding that about one-third receive two weeks notice and another 18 percent get their schedules three weeks in advance. But that leaves almost half of workers who only receive one week's notice, making it more difficult from them manage other obligations, such as school, family commitments or other jobs.\n\nClopens remain a problem, as well. About 60 percent of workers who have to handle a clopen receive seven or fewer hours of rest between a closing and an opening shift, the study found.\n\nThat's prompted one former Starbucks employee to start a petition to end the practice of scheduling clopens. Ciara Moran noted in her petition that she sometimes was only able to get four or five hours of sleep on the days she was scheduled for clopens. She said she quit her job because she doubted whether it was possible to get ahead given the demands on workers.\n\nEven if Starbucks stuck with its policy of providing eight hours between shifts, that's not enough time, especially given that many workers in the service sector have long commutes, the study said.\n\nAnother issue singled out by the report is Starbucks' practices on sick time. Since paid time off is only available to workers with at least a year on the job, about 40 percent of employees in the survey said they had dealt with barriers in taking sick days.\n\nIn a perfect world, Weisse said he'd like to receive his schedule either a month or a", + mongodb_id: '5d34b8c29ced494f010ed470', + featured: 2, + flag: true, + is_incident_report: true, + is_translated: false, + namespaces: ['CSETv0', 'GMF', 'CSETv1_Annotator-1', 'CSETv1'], + classifications: [ + 'CSETv0:Location:Global', + 'CSETv0:Near Miss:Unclear/unknown', + 'CSETv0:Named Entities:Starbucks', + 'CSETv0:Named Entities:Kronos', + 'CSETv0:Technology Purveyor:Starbucks', + 'CSETv0:Intent:Unclear', + 'CSETv0:Severity:Negligible', + 'CSETv0:Harm Type:Psychological harm', + 'CSETv0:Infrastructure Sectors:Food and agriculture', + 'GMF:Known AI Technical Failure:Underspecification', + 'GMF:Potential AI Technical Failure:Tuning Issues', + 'GMF:Potential AI Technical Failure:Misconfigured Aggregation', + 'GMF:Potential AI Technology:Regression', + 'GMF:Potential AI Technology:Diverse Data', + 'GMF:Known AI Goal:Market Forecasting', + 'GMF:Known AI Goal:Scheduling', + 'CSETv1_Annotator-1:Physical Objects:no', + 'CSETv1_Annotator-1:Entertainment Industry:no', + 'CSETv1_Annotator-1:Report, Test, or Study of data:no', + 'CSETv1_Annotator-1:Deployed:yes', + 'CSETv1_Annotator-1:Producer Test in Controlled Conditions:no', + 'CSETv1_Annotator-1:Producer Test in Operational Conditions:no', + 'CSETv1_Annotator-1:User Test in Controlled Conditions:no', + 'CSETv1_Annotator-1:User Test in Operational Conditions:no', + 'CSETv1_Annotator-1:Tangible Harm:tangible harm definitively occurred', + 'CSETv1_Annotator-1:AI System:yes', + 'CSETv1_Annotator-1:AI Harm Level:AI tangible harm event', + 'CSETv1_Annotator-1:Impact on Critical Services:no', + 'CSETv1_Annotator-1:Rights Violation:no', + 'CSETv1_Annotator-1:Involving Minor:no', + 'CSETv1_Annotator-1:Detrimental Content:no', + 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'CSETv1:Public Sector Deployment:no', + 'CSETv1:Autonomy Level:Autonomy2', + 'CSETv1:Intentional Harm:No. Not intentionally designed to perform harm', + 'CSETv1:Special Interest Intangible Harm:no', + 'CSETv1:Date of Incident Year:2014', + 'CSETv1:Multiple AI Interaction:no', + 'CSETv1:Embedded:no', + 'CSETv1:Location Country (two letters):US', + 'CSETv1:Location Region:North America', + 'CSETv1:Data Inputs:schedules', + 'CSETv1:Data Inputs:worker profiles', + 'CSETv1:Data Inputs:store traffic', + 'CSETv1:AI Task:scheduling', + 'CSETv1:AI Task:productivity optimization', + 'CSETv1:AI Task:predict store traffic', + ], + CSETv0: { + Location: 'Global', + 'Near Miss': 'Unclear/unknown', + 'Named Entities': ['Starbucks', 'Kronos'], + 'Technology Purveyor': ['Starbucks'], + Intent: 'Unclear', + Severity: 'Negligible', + 'Harm Type': ['Psychological harm'], + 'Lives Lost': false, + 'Harm Distribution Basis': [], + 'Infrastructure Sectors': ['Food and agriculture'], + 'Financial Cost': '', + 'System Developer': [], + 'Sector of Deployment': [''], + 'Public Sector Deployment': false, + 'Nature of End User': '', + 'Level of Autonomy': '', + 'Relevant AI functions': [], + 'AI Techniques': [], + 'AI Applications': [], + 'Physical System': [], + 'Problem Nature': [], + }, + GMF: { + 'Known AI Technical Failure': ['Underspecification'], + 'Known AI Technical Failure Snippets': [ + { + attributes: [ + { + short_name: 'Snippet Text', + value_json: + '"“You’re waiting on your job to control your life,” she said, with the scheduling software used by her employer dictating everything from “how much sleep Gavin will get to what groceries I’ll be able to buy this month.”"', + }, + { + short_name: 'Related Classifications', + value_json: '["Underspecification"]', + }, + ], + }, + { + attributes: [ + { + short_name: 'Snippet Text', + value_json: + '"Along with virtually every major retail and restaurant chain, Starbucks relies on software that choreographs workers in precise, intricate ballets, using sales patterns and other data to determine which of its 130,000 baristas are needed in its thousands of locations and exactly when."', + }, + { + short_name: 'Related Classifications', + value_json: '["Underspecification"]', + }, + ], + }, + { + attributes: [ + { + short_name: 'Snippet Text', + value_json: + '"Among other changes, the company said it would end the practice of \\"clopening,\\" when an employee responsible for closing a store late at night is also assigned to open it early in the morning."', + }, + { + short_name: 'Related Classifications', + value_json: '["Underspecification"]', + }, + ], + }, + { + attributes: [ + { + short_name: 'Snippet Text', + value_json: + '"In a follow-up piece, the author, Jodi Kantor, points directly to Kronos\' scheduling software as the root of the problem."', + }, + { + short_name: 'Related Classifications', + value_json: '["Underspecification"]', + }, + ], + }, + ], + 'Potential AI Technical Failure': ['Tuning Issues', 'Misconfigured Aggregation'], + 'Potential AI Technical Failure Snippets': [ + { + attributes: [ + { + short_name: 'Snippet Text', + value_json: + '"In addition, Kronos is improving a feature meant to help give employees more control over their schedules: Though the software already incorporates employee availability and preferences into its scheduling calculations, improvements to a shift-swapping feature on its employee-facing web and mobile apps will theoretically allow employees to work around conflicts among themselves."', + }, + { + short_name: 'Related Classifications', + value_json: '["Tuning Issues","Misconfigured Aggregation"]', + }, + ], + }, + ], + 'Potential AI Technology': ['Regression', 'Diverse Data'], + 'Potential AI Technology Snippets': [ + { + attributes: [ + { + short_name: 'Snippet Text', + value_json: + '"“You’re waiting on your job to control your life,” she said, with the scheduling software used by her employer dictating everything from “how much sleep Gavin will get to what groceries I’ll be able to buy this month.”"', + }, + { + short_name: 'Related Classifications', + value_json: '["Regression"]', + }, + ], + }, + { + attributes: [ + { + short_name: 'Snippet Text', + value_json: + '"In a follow-up piece, the author, Jodi Kantor, points directly to Kronos\' scheduling software as the root of the problem."', + }, + { + short_name: 'Related Classifications', + value_json: '["Diverse Data"]', + }, + ], + }, + ], + 'Known AI Goal': ['Market Forecasting', 'Scheduling'], + 'Known AI Goal Snippets': [], + }, + 'CSETv1_Annotator-1': { + 'Physical Objects': 'no', + 'Entertainment Industry': 'no', + 'Report, Test, or Study of data': 'no', + Deployed: 'yes', + 'Producer Test in Controlled Conditions': 'no', + 'Producer Test in Operational Conditions': 'no', + 'User Test in Controlled Conditions': 'no', + 'User Test in Operational Conditions': 'no', + 'Tangible Harm': 'tangible harm definitively occurred', + 'AI System': 'yes', + 'AI Harm Level': 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'', + 'AI Task': ['scheduling', 'productivity optimization'], + }, + CSETv1: { + 'Harm Distribution Basis': ['none'], + 'Sector of Deployment': ['accommodation and food service activities'], + 'Physical Objects': 'no', + 'Entertainment Industry': 'no', + 'Report, Test, or Study of data': 'no', + Deployed: 'yes', + 'Producer Test in Controlled Conditions': 'no', + 'Producer Test in Operational Conditions': 'no', + 'User Test in Controlled Conditions': 'no', + 'User Test in Operational Conditions': 'no', + 'Tangible Harm': 'tangible harm definitively occurred', + 'AI System': 'yes', + 'AI Harm Level': 'AI tangible harm event', + 'Impact on Critical Services': 'no', + 'Rights Violation': 'no', + 'Involving Minor': 'no', + 'Detrimental Content': 'no', + 'Protected Characteristic': 'no', + 'Clear link to Technology': 'yes', + 'Annotator’s AI special interest intangible harm assessment': 'no', + 'Public Sector Deployment': 'no', + 'Autonomy Level': 'Autonomy2', + 'Intentional Harm': 'No. Not intentionally designed to perform harm', + 'AI tools and methods': '', + 'Special Interest Intangible Harm': 'no', + 'Date of Incident Year': '2014', + 'Multiple AI Interaction': 'no', + Embedded: 'no', + 'Location Country (two letters)': 'US', + 'Location Region': 'North America', + 'Infrastructure Sectors': [], + 'Operating Conditions': '', + 'Lives Lost': 0, + Injuries: 0, + 'Data Inputs': ['schedules', 'worker profiles', 'store traffic'], + 'Physical System Type': '', + 'AI Task': ['scheduling', 'productivity optimization', 'predict store traffic'], + }, + incident_id: 1, + incident_date: '2014-08-14', + epoch_incident_date: 1407974400, + incident_title: + 'Kronos Scheduling Algorithm Allegedly Caused Financial Issues for Starbucks Employees', + incident_description: + 'Kronos’s scheduling algorithm and its use by Starbucks managers allegedly negatively impacted financial and scheduling stability for Starbucks employees, which disadvantaged wage workers.', + objectID: '23', + _snippetResult: { + description: { + value: + "Some employees at the coffee chain say it isn't living up to promises to improve the company's labor practices", + matchLevel: 'none', + }, + text: { + value: + 'For Starbucks (SBUX) barista Kylei Weisse, working at the coffee chain helps him secure health', + matchLevel: 'none', + }, + }, + _highlightResult: { + description: { + value: + "Some employees at the coffee chain say it isn't living up to promises to improve the company's labor practices", + matchLevel: 'none', + matchedWords: [], + }, + title: { + value: '​Is Starbucks shortchanging its baristas?', + matchLevel: 'none', + matchedWords: [], + }, + text: { + value: + "For Starbucks (SBUX) barista Kylei Weisse, working at the coffee chain helps him secure health insurance and some extra money while he studies at Georgia Perimeter College. What it doesn't provide is the kind of stable schedule that the company promised its workers last year.\n\n\"It's the wild inconsistency\" of the hours that's a problem, Weisse, 32, said. \"We're supposed to get them 10 days in advance, which often happens, but there's no guarantee. If our manager doesn't get it to us on time, we just have to deal with it.\"\n\nThat became a problem recently when Weisse's manager gave him only a few days notice on his work hours, which ended up conflicting with an anatomy and physiology exam at his college. Weisse ended up paying another worker $20 to take his shift so he could take the exam.\n\nThe short notice is especially frustrating because of Starbucks' vow last year to post employees' schedules at least 10 days in advance, as well as the company's insistence that workers provide at least one-month notice when they need to take a day off.\n\nWhat's behind Starbucks price increases?\n\nWeisse isn't alone in complaining that Starbucks isn't living up to its promises to overhaul its labor practices for its roughly 130,000 baristas. That vow followed an article last year by The New York Times that detailed how workers were struggling to manage childcare and other obligations when the company provided only a few days notice about their schedules.\n\nAbout half of roughly 200 Starbucks baristas in a recent survey said they are still receiving their schedule with less than one week's notice. Others also reported being asked to handle \"clopens,\" split shifts in which employees work a closing shift late into the evening and then an early opening shift the following morning. The company last year promised to end the practice.\n\nOf course, Starbucks isn't alone in using \"just-in-time\" scheduling, with the retail and restaurant industry increasingly turning to software that allows them to change work schedules at the last minute, depending on whether business picks up or slows down. But it is Starbucks that has become a lightning rod on the issue given its vows to improve how it treats employees and its own emphatic claims to valuing workers, whom it labels \"partners.\"\n\n\"Starbucks has the values and wants to do right by their employees,\" said Carrie Gleason, director of the Fair Workweek Initiative at the Center for Popular Democracy, an advocacy group focused on workers'rights, and a co-author of the group's new report on the company's labor practices. \"However, since last year when the company recognized there was a serious problem with the way it scheduled workers and pledged to reform, still so many of the same issues persist.\"\n\nStarbucks didn't respond to requests for comment on the study or on baristas' reports of labor practices that are failing to meet the company's stated goals.\n\nIn an internal memo this week published by Time, Starbucks executive Cliff Burrows wrote that the company couldn't validate the survey, but added that \"the findings suggest, contrary to the expectations we have in place, that some partners are receiving their schedules less than one week in advance and that there is a continuing issue with some partners working a close and then an opening shift the following morning.\" He asks store managers \"to go the extra mile to ensure partners have a consistent schedule.\"\n\nStarbucks ends \"race together\" campaign amid public backlash\n\nTo be sure, some Starbucks workers are receiving at least 10 days notice on their work hours, with the survey finding that about one-third receive two weeks notice and another 18 percent get their schedules three weeks in advance. But that leaves almost half of workers who only receive one week's notice, making it more difficult from them manage other obligations, such as school, family commitments or other jobs.\n\nClopens remain a problem, as well. About 60 percent of workers who have to handle a clopen receive seven or fewer hours of rest between a closing and an opening shift, the study found.\n\nThat's prompted one former Starbucks employee to start a petition to end the practice of scheduling clopens. Ciara Moran noted in her petition that she sometimes was only able to get four or five hours of sleep on the days she was scheduled for clopens. She said she quit her job because she doubted whether it was possible to get ahead given the demands on workers.\n\nEven if Starbucks stuck with its policy of providing eight hours between shifts, that's not enough time, especially given that many workers in the service sector have long commutes, the study said.\n\nAnother issue singled out by the report is Starbucks' practices on sick time. Since paid time off is only available to workers with at least a year on the job, about 40 percent of employees in the survey said they had dealt with barriers in taking sick days.\n\nIn a perfect world, Weisse said he'd like to receive his schedule either a month or a", + matchLevel: 'none', + matchedWords: [], + }, + incident_title: { + value: + 'Kronos Scheduling Algorithm Allegedly Caused Financial Issues for Starbucks Employees', + matchLevel: 'none', + matchedWords: [], + }, + incident_description: { + value: + 'Kronos’s scheduling algorithm and its use by Starbucks managers allegedly negatively impacted financial and scheduling stability for Starbucks employees, which disadvantaged wage workers.', + matchLevel: 'none', + matchedWords: [], + }, + }, + }, + { + authors: ['Marietje Schaake'], + description: + '😱 Wait! What? Just when you think you’ve seen it all…. Meta’s chatbot replied to the question askedby my colleague \n@kingjen\n: ’Who is a terrorist?’ with my (given) name! That’s right, not Bin Laden ', + epoch_date_downloaded: 1661472000, + epoch_date_modified: 1661731200, + epoch_date_published: 1661385600, + epoch_date_submitted: 1661472000, + image_url: 'https://pbs.twimg.com/media/Fa8rYYVWIAAI0xN?format=jpg', + language: 'en', + report_number: 1967, + source_domain: 'twitter.com', + submitters: ['Cesar Varela'], + title: 'Tweet: @MarietjeSchaake', + name: 'Tweet: @MarietjeSchaake', + url: 'https://twitter.com/MarietjeSchaake/status/1562515297688399873', + tags: [], + editor_notes: '', + cloudinary_id: 'reports/pbs.twimg.com/media/Fa8rYYVWIAAI0xN?format=jpg', + text: '😱 Wait! What? Just when you think you’ve seen it all…. Meta’s chatbot replied to the question askedby my colleague\n@kingjen\n: ’Who is a terrorist?’ with my (given) name! That’s right, not Bin Laden or the Unabomber, but me… How did that happen? What are Meta’s sources?!\n', + mongodb_id: '630c8eb443fe03f46cc8bcc4', + featured: 1, + is_incident_report: true, + is_translated: false, + namespaces: ['CSETv1_Annotator-1', 'CSETv1'], + classifications: [ + 'CSETv1_Annotator-1:Harm Distribution Basis:none', + 'CSETv1_Annotator-1:Sector of Deployment:information and communication', + 'CSETv1_Annotator-1:Physical Objects:no', + 'CSETv1_Annotator-1:Entertainment Industry:no', + 'CSETv1_Annotator-1:Report, Test, or Study of data:no', + 'CSETv1_Annotator-1:Deployed:yes', + 'CSETv1_Annotator-1:Producer Test in Controlled Conditions:no', + 'CSETv1_Annotator-1:Producer Test in Operational Conditions:no', + 'CSETv1_Annotator-1:User Test in Controlled Conditions:no', + 'CSETv1_Annotator-1:User Test in Operational Conditions:yes', + 'CSETv1_Annotator-1:Tangible Harm:no tangible harm, near-miss, or issue', + 'CSETv1_Annotator-1:AI System:yes', + 'CSETv1_Annotator-1:AI Harm Level:none', + 'CSETv1_Annotator-1:Impact on Critical Services:no', + 'CSETv1_Annotator-1:Rights Violation:no', + 'CSETv1_Annotator-1:Involving Minor:no', + 'CSETv1_Annotator-1:Detrimental Content:yes', + 'CSETv1_Annotator-1:Protected Characteristic:no', + 'CSETv1_Annotator-1:Clear link to Technology:yes', + 'CSETv1_Annotator-1:Annotator’s AI special interest intangible harm assessment:yes', + 'CSETv1_Annotator-1:Public Sector Deployment:no', + 'CSETv1_Annotator-1:Autonomy Level:Autonomy1', + 'CSETv1_Annotator-1:Intentional Harm:No. Not intentionally designed to perform harm', + 'CSETv1_Annotator-1:AI tools and methods:large language models', + 'CSETv1_Annotator-1:Special Interest Intangible Harm:yes', + 'CSETv1_Annotator-1:Date of Incident Year:2022', + 'CSETv1_Annotator-1:Multiple AI Interaction:no', + 'CSETv1_Annotator-1:Embedded:no', + 'CSETv1_Annotator-1:Location Region:Global', + 'CSETv1_Annotator-1:Data Inputs:user prompts', + 'CSETv1_Annotator-1:Data Inputs:user queries', + 'CSETv1_Annotator-1:Data Inputs:text', + 'CSETv1_Annotator-1:AI Task:text generation', + 'CSETv1:Harm Distribution Basis:unclear', + 'CSETv1:Sector of Deployment:information and communication', + 'CSETv1:Physical Objects:no', + 'CSETv1:Entertainment Industry:no', + 'CSETv1:Report, Test, or Study of data:no', + 'CSETv1:Deployed:yes', + 'CSETv1:Producer Test in Controlled Conditions:no', + 'CSETv1:Producer Test in Operational Conditions:no', + 'CSETv1:User Test in Controlled Conditions:no', + 'CSETv1:User Test in Operational Conditions:yes', + 'CSETv1:Tangible Harm:no tangible harm, near-miss, or issue', + 'CSETv1:AI System:yes', + 'CSETv1:AI Harm Level:none', + 'CSETv1:Impact on Critical Services:no', + 'CSETv1:Rights Violation:no', + 'CSETv1:Involving Minor:no', + 'CSETv1:Detrimental Content:yes', + 'CSETv1:Protected Characteristic:no', + 'CSETv1:Clear link to Technology:yes', + 'CSETv1:Annotator’s AI special interest intangible harm assessment:yes', + 'CSETv1:Public Sector Deployment:no', + 'CSETv1:Autonomy Level:Autonomy1', + 'CSETv1:Intentional Harm:No. Not intentionally designed to perform harm', + 'CSETv1:AI tools and methods:large language models', + 'CSETv1:Special Interest Intangible Harm:yes', + 'CSETv1:Date of Incident Year:2022', + 'CSETv1:Multiple AI Interaction:no', + 'CSETv1:Embedded:no', + 'CSETv1:Location Region:Global', + 'CSETv1:Data Inputs:user prompts', + 'CSETv1:Data Inputs:user queries', + 'CSETv1:Data Inputs:text', + 'CSETv1:AI Task:text generation', + ], + 'CSETv1_Annotator-1': { + 'Harm Distribution Basis': ['none'], + 'Sector of Deployment': ['information and communication'], + 'Physical Objects': 'no', + 'Entertainment Industry': 'no', + 'Report, Test, or Study of data': 'no', + Deployed: 'yes', + 'Producer Test in Controlled Conditions': 'no', + 'Producer Test in Operational Conditions': 'no', + 'User Test in Controlled Conditions': 'no', + 'User Test in Operational Conditions': 'yes', + 'Tangible Harm': 'no tangible harm, near-miss, or issue', + 'AI System': 'yes', + 'AI Harm Level': 'none', + 'Impact on Critical Services': 'no', + 'Rights Violation': 'no', + 'Involving Minor': 'no', + 'Detrimental Content': 'yes', + 'Protected Characteristic': 'no', + 'Clear link to Technology': 'yes', + 'Annotator’s AI special interest intangible harm assessment': 'yes', + 'Public Sector Deployment': 'no', + 'Autonomy Level': 'Autonomy1', + 'Intentional Harm': 'No. Not intentionally designed to perform harm', + 'AI tools and methods': ['large language models'], + 'Special Interest Intangible Harm': 'yes', + 'Date of Incident Year': '2022', + 'Multiple AI Interaction': 'no', + Embedded: 'no', + 'Location Country (two letters)': '', + 'Location Region': 'Global', + 'Infrastructure Sectors': [], + 'Operating Conditions': '', + 'Lives Lost': 0, + Injuries: 0, + 'Data Inputs': ['user prompts', 'user queries', 'text'], + 'Physical System Type': '', + 'AI Task': ['text generation'], + }, + CSETv1: { + 'Harm Distribution Basis': ['unclear'], + 'Sector of Deployment': ['information and communication'], + 'Physical Objects': 'no', + 'Entertainment Industry': 'no', + 'Report, Test, or Study of data': 'no', + Deployed: 'yes', + 'Producer Test in Controlled Conditions': 'no', + 'Producer Test in Operational Conditions': 'no', + 'User Test in Controlled Conditions': 'no', + 'User Test in Operational Conditions': 'yes', + 'Tangible Harm': 'no tangible harm, near-miss, or issue', + 'AI System': 'yes', + 'AI Harm Level': 'none', + 'Impact on Critical Services': 'no', + 'Rights Violation': 'no', + 'Involving Minor': 'no', + 'Detrimental Content': 'yes', + 'Protected Characteristic': 'no', + 'Clear link to Technology': 'yes', + 'Annotator’s AI special interest intangible harm assessment': 'yes', + 'Public Sector Deployment': 'no', + 'Autonomy Level': 'Autonomy1', + 'Intentional Harm': 'No. Not intentionally designed to perform harm', + 'AI tools and methods': ['large language models'], + 'Special Interest Intangible Harm': 'yes', + 'Date of Incident Year': '2022', + 'Multiple AI Interaction': 'no', + Embedded: 'no', + 'Location Country (two letters)': '', + 'Location Region': 'Global', + 'Infrastructure Sectors': [], + 'Operating Conditions': '', + 'Lives Lost': 0, + Injuries: 0, + 'Data Inputs': ['user prompts', 'user queries', 'text'], + 'Physical System Type': '', + 'AI Task': ['text generation'], + }, + incident_id: 2, + incident_date: '2022-08-25', + epoch_incident_date: 1661385600, + incident_title: 'BlenderBot 3 Cited Dutch Politician as a Terrorist', + incident_description: + 'Meta’s conversational AI BlenderBot 3, when prompted “who is a terrorist,“ responded with an incumbent Dutch politician’s name, who was confused about its association.', + objectID: '1967', + _snippetResult: { + description: { + value: + '😱 Wait! What? Just when you think you’ve seen it all…. Meta’s chatbot replied to the question askedby my colleague \n@kingjen\n: ’Who is a terrorist?’ with my (given) name! That’s right, not Bin Laden ', + matchLevel: 'none', + }, + text: { + value: '😱 Wait! What? Just when you think you’ve seen it all…. Meta’s chatbot', + matchLevel: 'none', + }, + }, + _highlightResult: { + description: { + value: + '😱 Wait! What? Just when you think you’ve seen it all…. Meta’s chatbot replied to the question askedby my colleague \n@kingjen\n: ’Who is a terrorist?’ with my (given) name! That’s right, not Bin Laden ', + matchLevel: 'none', + matchedWords: [], + }, + title: { + value: 'Tweet: @MarietjeSchaake', + matchLevel: 'none', + matchedWords: [], + }, + text: { + value: + '😱 Wait! What? Just when you think you’ve seen it all…. Meta’s chatbot replied to the question askedby my colleague\n@kingjen\n: ’Who is a terrorist?’ with my (given) name! That’s right, not Bin Laden or the Unabomber, but me… How did that happen? What are Meta’s sources?!\n', + matchLevel: 'none', + matchedWords: [], + }, + incident_title: { + value: 'BlenderBot 3 Cited Dutch Politician as a Terrorist', + matchLevel: 'none', + matchedWords: [], + }, + incident_description: { + value: + 'Meta’s conversational AI BlenderBot 3, when prompted “who is a terrorist,“ responded with an incumbent Dutch politician’s name, who was confused about its association.', + matchLevel: 'none', + matchedWords: [], + }, + }, + }, + { + authors: ['William Douglas Heaven'], + description: + 'Cuando el covid-19 golpeó Europa en marzo de 2020, los hospitales se sumieron en una crisis sanitaria que aún se entendía mal. “Los médicos realmente no tenían idea de cómo manejar a estos pacientes”, dice Laure Wyn', + epoch_date_downloaded: 1648598400, + epoch_date_modified: 1648598400, + epoch_date_published: 1627603200, + epoch_date_submitted: 1648598400, + image_url: + 'https://wp.technologyreview.com/wp-content/uploads/2021/07/AP_20178810286930.jpg?resize=1200,600', + language: 'en', + report_number: 1551, + source_domain: 'technologyreview.com', + submitters: ['Anonymous'], + title: 'Cientos de herramientas de IA se han creado para detectar el covid. Ninguna de ellas ayudó.', + name: 'Cientos de herramientas de IA se han creado para detectar el covid. Ninguna de ellas ayudó.', + url: 'https://www.technologyreview.com/2021/07/30/1030329/machine-learning-ai-failed-covid-hospital-diagnosis-pandemic/', + tags: [], + editor_notes: '', + cloudinary_id: + 'reports/wp.technologyreview.com/wp-content/uploads/2021/07/AP_20178810286930.jpg?resize=1200,600', + text: 'Cuando el covid-19 golpeó Europa en marzo de 2020, los hospitales se sumieron en una crisis sanitaria que aún se entendía mal. “Los médicos realmente no tenían idea de cómo manejar a estos pacientes”, dice Laure Wynants, una epidemióloga de la Universidad de Maastricht en los Países Bajos, que estudia herramientas predictivas.\n\nPero había datos provenientes de China, que tenía una ventaja de cuatro meses en la carrera para vencer la pandemia. Si los algoritmos de aprendizaje automático pudieran entrenarse con esos datos para ayudar a los médicos a entender lo que estaban viendo y tomar decisiones, podría salvar vidas. “Pensé, `Si hay algún momento en que la IA podría demostrar su utilidad, es ahora`”, dice Wynants. “Tenía mis esperanzas altas.”\n\nNunca sucedió, pero no por falta de esfuerzo. Equipos de investigación de todo el mundo se ofrecieron para ayudar. La comunidad de IA, en particular, se apresuró a desarrollar software que muchos creían permitiría a los hospitales diagnosticar o clasificar a los pacientes más rápido, brindando el tan necesario apoyo a las líneas del frente, en teoría.\n\nAl final, se desarrollaron muchos cientos de herramientas predictivas. Ninguna de ellas hizo una diferencia real, y algunas fueron potencialmente dañinas.\n\nEsa es la condenatoria conclusión de múltiples estudios publicados en los últimos meses. En junio, el Instituto Turing, el centro nacional de ciencia de datos e IA del Reino Unido, publicó un informe que resumía las discusiones en una serie de talleres que realizó a finales de 2020. El claro consenso fue que las herramientas de IA habían tenido poco, si es que algún, impacto en la lucha contra el covid.\n\nNo aptas para uso clínico\n\nEsto refleja los resultados de dos estudios importantes que evaluaron cientos de herramientas predictivas desarrolladas el año pasado. Wynants es la autora principal de uno de ellos, una revisión en el British Medical Journal que aún se está actualizando a medida que se lanzan nuevas herramientas y se prueban las existentes. Ella y sus colegas han examinado 232 algoritmos para diagnosticar pacientes o predecir qué tan enfermos podrían llegar a estar aquellos con la enfermedad. Encontraron que ninguno de ellos era apto para uso clínico. Solo dos han sido señalados como lo suficientemente prometedores para futuras pruebas.\n\n“Es impactante”, dice Wynants. “Entré en esto con algunas preocupaciones, pero esto superó mis temores.”\n\nEl estudio de Wynants está respaldado por otra gran revisión realizada por Derek Driggs, un investigador de aprendizaje automático en la Universidad de Cambridge, y sus colegas, y publicada en Nature Machine Intelligence. Este equipo se centró en modelos de aprendizaje profundo para diagnosticar covid y predecir el riesgo de los pacientes a partir de imágenes médicas, como radiografías de tórax y tomografías computarizadas (TC) de tórax. Examinaron 415 herramientas publicadas y, al igual que Wynants y sus colegas, concluyeron que ninguna era apta para uso clínico.\n\n“Esta pandemia fue una gran prueba para la IA y la medicina”, dice Driggs, quien está trabajando en una herramienta de aprendizaje automático para ayudar a los médicos durante la pandemia. “Habría sido un gran paso para conseguir que el público estuviera de nuestro lado”, dice. “Pero no creo que hayamos pasado esa prueba.”\n\nAmbos equipos encontraron que los investigadores repitieron los mismos errores básicos en la forma en que entrenaron o probaron sus herramientas. Suposiciones incorrectas sobre los datos a menudo significaban que los modelos entrenados no funcionaban como se afirmaba.\n\nWynants y Driggs todavía creen que la IA tiene el potencial de ayudar. Pero les preocupa que pueda ser perjudicial si se construye de la manera incorrecta porque podrían perder diagnósticos o subestimar el riesgo para los pacientes vulnerables. “Hay mucho bombo sobre los modelos de aprendizaje automático y lo que pueden hacer hoy”, dice Driggs.\n\nLas expectativas poco realistas fomentan el uso de estas herramientas antes de que estén listas. Wynants y Driggs dicen que algunos de los algoritmos que examinaron ya se han utilizado en hospitales, y algunos están siendo comercializados por desarrolladores privados. “Temo que puedan haber perjudicado a los pacientes”, dice Wynants.\n\nEntonces, ¿qué salió mal? ¿Y cómo cerramos esa brecha? Si hay un lado positivo, es que la pandemia ha dejado claro para muchos investigadores que la forma en que se construyen las herramientas de IA necesita cambiar. “La pandemia ha puesto en el centro de atención problemas que hemos estado arrastrando durante algún tiempo”, dice Wynants.\n\nQué salió mal\n\nMuchos de los problemas que se descubrieron están relacionados con la mala calidad de los datos que los investigadores utilizaron para desarrollar sus herramientas. La información sobre los pacientes con covid, incluidas las imágenes médicas, se recopiló y compartió en medio de una pandemia global, a menudo por los médicos que luchaban por tratar a esos pacientes. Los investigadores querían ayudar rápidamente, y estos eran los únicos conjuntos de datos públicos disponibles. Pero esto significaba que muchas herramientas se construyeron utilizando datos mal etiquetados o datos de fuentes desconocidas.\n\nDriggs destaca el problema de lo que él llama conjuntos de datos Frankenstein, que se ensamblan a partir de múltiples fuentes y pueden contener duplicados. Esto significa que algunas herramientas terminan siendo probadas con los mismos datos con los que fueron entrenadas, haciéndolas parecer más precisas de lo que son.\n\nTambién ensucia el origen de ciertos conjuntos de datos. Esto puede significar que los investigadores pierdan características importantes que sesgan el entrenamiento de sus modelos. Muchos usaron sin saberlo un conjunto de datos que contenía escaneos de tórax de niños que no tenían covid como sus ejemplos de cómo se veían los casos no covid. Pero como resultado, las IAs aprendieron a identificar niños, no covid.\n\nEl grupo de Driggs entrenó su propio modelo utilizando un conjunto de datos que contenía una mezcla de escaneos tomados cuando los pacientes estaban acostados y de pie. Debido a que los pacientes escaneados mientras estaban acostados tenían más probabilidades de estar gravemente enfermos, la IA aprendió erróneamente a predecir un riesgo grave de covid a partir de la posición de una persona.\n\nEn otros casos, se encontró que algunas IAs estaban detectando la fuente de texto que ciertos hospitales usaban para etiquetar los escaneos. Como resultado, las fuentes de los hospitales con casos más graves se convirtieron en predictores de riesgo de covid.\n\nErrores como estos parecen obvios en retrospectiva. También pueden corregirse ajustando los modelos, si los investigadores son conscientes de ellos. Es posible reconocer las deficiencias y lanzar un modelo menos preciso, pero menos engañoso. Pero muchas herramientas fueron desarrolladas ya sea por investigadores de IA que carecían de la experiencia médica para detectar fallas en los datos o por investigadores médicos que carecían de las habilidades matemáticas para compensar esas fallas.\n\nUn problema más sutil que destaca Driggs es el sesgo de incorporación, o sesgo introducido en el punto en que se etiqueta un conjunto de datos. Por ejemplo, muchas imágenes médicas se etiquetaron según si los radiólogos que las crearon dijeron que mostraban covid. Pero eso incrusta, o incorpora, cualquier sesgo de ese médico en particular en la verdad fundamental de un conjunto de datos. Sería mucho mejor etiquetar una imagen médica con el resultado de una prueba PCR en lugar de la opinión de un médico, dice Driggs. Pero no siempre hay tiempo para sutilezas estadísticas en hospitales ocupados.\n\nEso no ha impedido que algunas de estas herramientas se apresuren a entrar en la práctica clínica. Wynants dice que no está claro cuáles se están utilizando o cómo. Los hospitales a veces dicen que están usando una herramienta solo con fines de investigación, lo que hace difícil evaluar cuánto dependen de ellas los médicos. “Hay mucho secreto”, dice.\n\nWynants le pidió a una empresa que estaba comercializando algoritmos de aprendizaje profundo que compartiera información sobre su enfoque, pero no recibió respuesta. Más tarde encontró varios modelos publicados de investigadores vinculados a esta empresa, todos ellos con un alto riesgo de sesgo. “No sabemos realmente qué implementó la empresa”, dice.\n\nSegún Wynants, algunos hospitales incluso están firmando acuerdos de confidencialidad con proveedores de IA médica. Cuando les preguntó a los médicos qué algoritmos o software estaban usando, a veces le dijeron que no se les permitía decirlo.\n\nCómo solucionarlo\n\n¿Cuál es la solución? Mejores datos ayudarían, pero en tiempos de crisis eso es mucho pedir. Es más importante aprovechar al máximo los conjuntos de datos que tenemos. El movimiento más simple sería que los equipos de IA colaboraran más con los clínicos, dice Driggs. Los investigadores también necesitan compartir sus modelos y divulgar cómo fueron entrenados para que otros puedan probarlos y construir sobre ellos. “Esas son dos cosas que podríamos hacer hoy”, dice. “Y resolverían tal vez', + mongodb_id: '6243a9eedf8b4b62d982817e', + featured: 1, + is_incident_report: true, + is_translated: true, + namespaces: ['CSETv1_Annotator-3', 'CSETv1_Annotator-1'], + classifications: [ + 'CSETv1_Annotator-3:Harm Distribution Basis:unclear', + 'CSETv1_Annotator-3:Sector of Deployment:human health and social work activities', + 'CSETv1_Annotator-3:Physical Objects:yes', + 'CSETv1_Annotator-3:Entertainment Industry:no', + 'CSETv1_Annotator-3:Report, Test, or Study of data:no', + 'CSETv1_Annotator-3:Deployed:yes', + 'CSETv1_Annotator-3:Producer Test in Controlled Conditions:no', + 'CSETv1_Annotator-3:Producer Test in Operational Conditions:no', + 'CSETv1_Annotator-3:User Test in Controlled Conditions:no', + 'CSETv1_Annotator-3:User Test in Operational Conditions:no', + 'CSETv1_Annotator-3:Tangible Harm:unclear', + 'CSETv1_Annotator-3:AI System:yes', + 'CSETv1_Annotator-3:AI Harm Level:none', + 'CSETv1_Annotator-3:Impact on Critical Services:maybe', + 'CSETv1_Annotator-3:Rights Violation:maybe', + 'CSETv1_Annotator-3:Involving Minor:no', + 'CSETv1_Annotator-3:Detrimental Content:no', + 'CSETv1_Annotator-3:Protected Characteristic:maybe', + 'CSETv1_Annotator-3:Clear link to Technology:yes', + 'CSETv1_Annotator-3:Annotator’s AI special interest intangible harm assessment:maybe', + 'CSETv1_Annotator-3:Public Sector Deployment:no', + 'CSETv1_Annotator-3:Autonomy Level:unclear', + 'CSETv1_Annotator-3:Intentional Harm:No. Not intentionally designed to perform harm', + 'CSETv1_Annotator-3:Special Interest Intangible Harm:maybe', + 'CSETv1_Annotator-3:Date of Incident Year:2020', + 'CSETv1_Annotator-3:Multiple AI Interaction:no', + 'CSETv1_Annotator-3:Embedded:maybe', + 'CSETv1_Annotator-3:Location Region:Global', + 'CSETv1_Annotator-3:Infrastructure Sectors:healthcare and public health', + 'CSETv1_Annotator-3:Data Inputs:Unclear', + 'CSETv1_Annotator-1:Physical Objects:no', + 'CSETv1_Annotator-1:Entertainment Industry:no', + 'CSETv1_Annotator-1:Report, Test, or Study of data:maybe', + 'CSETv1_Annotator-1:Deployed:no', + 'CSETv1_Annotator-1:Producer Test in Controlled Conditions:yes', + 'CSETv1_Annotator-1:Producer Test in Operational Conditions:no', + 'CSETv1_Annotator-1:User Test in Controlled Conditions:no', + 'CSETv1_Annotator-1:User Test in Operational Conditions:no', + 'CSETv1_Annotator-1:Tangible Harm:no tangible harm, near-miss, or issue', + 'CSETv1_Annotator-1:AI System:yes', + 'CSETv1_Annotator-1:AI Harm Level:none', + 'CSETv1_Annotator-1:Impact on Critical Services:no', + 'CSETv1_Annotator-1:Rights Violation:no', + 'CSETv1_Annotator-1:Involving Minor:no', + 'CSETv1_Annotator-1:Detrimental Content:no', + 'CSETv1_Annotator-1:Protected Characteristic:no', + 'CSETv1_Annotator-1:Harm Distribution Basis:none', + 'CSETv1_Annotator-1:Special Interest Intangible Harm:no', + 'CSETv1_Annotator-1:Clear link to Technology:maybe', + 'CSETv1_Annotator-1:Annotator’s AI special interest intangible harm assessment:no', + 'CSETv1_Annotator-1:Date of Incident Year:2020', + 'CSETv1_Annotator-1:Multiple AI Interaction:no', + 'CSETv1_Annotator-1:Embedded:no', + 'CSETv1_Annotator-1:Location Region:Global', + 'CSETv1_Annotator-1:Data Inputs:patient information', + 'CSETv1_Annotator-1:Data Inputs:medical scans', + 'CSETv1_Annotator-1:Data Inputs:chest scans', + 'CSETv1_Annotator-1:Data Inputs:electronic health records', + 'CSETv1_Annotator-1:Sector of Deployment:human health and social work activities', + 'CSETv1_Annotator-1:Public Sector Deployment:no', + 'CSETv1_Annotator-1:Autonomy Level:Autonomy3', + 'CSETv1_Annotator-1:Intentional Harm:No. Not intentionally designed to perform harm', + 'CSETv1_Annotator-1:AI Task:diagnose patients', + ], + 'CSETv1_Annotator-3': { + 'Harm 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'Date of Incident Year': '2020', + 'Multiple AI Interaction': 'no', + Embedded: 'maybe', + 'Location Country (two letters)': '', + 'Location Region': 'Global', + 'Infrastructure Sectors': ['healthcare and public health'], + 'Operating Conditions': '', + 'Lives Lost': 0, + Injuries: 0, + 'Data Inputs': ['Unclear'], + 'Physical System Type': '', + 'AI Task': '', + }, + 'CSETv1_Annotator-1': { + 'Physical Objects': 'no', + 'Entertainment Industry': 'no', + 'Report, Test, or Study of data': 'maybe', + Deployed: 'no', + 'Producer Test in Controlled Conditions': 'yes', + 'Producer Test in Operational Conditions': 'no', + 'User Test in Controlled Conditions': 'no', + 'User Test in Operational Conditions': 'no', + 'Tangible Harm': 'no tangible harm, near-miss, or issue', + 'AI System': 'yes', + 'AI Harm Level': 'none', + 'Impact on Critical Services': 'no', + 'Rights Violation': 'no', + 'Involving Minor': 'no', + 'Detrimental Content': 'no', + 'Protected Characteristic': 'no', + 'Harm Distribution Basis': ['none'], + 'Special Interest Intangible Harm': 'no', + 'Clear link to Technology': 'maybe', + 'Annotator’s AI special interest intangible harm assessment': 'no', + 'Date of Incident Year': '2020', + 'Multiple AI Interaction': 'no', + Embedded: 'no', + 'Location Region': 'Global', + 'Lives Lost': 0, + Injuries: 0, + 'Data Inputs': [ + 'patient information', + 'medical scans', + 'chest scans', + 'electronic health records', + ], + 'Sector of Deployment': ['human health and social work activities'], + 'Public Sector Deployment': 'no', + 'Autonomy Level': 'Autonomy3', + 'Intentional Harm': 'No. Not intentionally designed to perform harm', + 'AI Task': ['diagnose patients'], + }, + incident_id: 3, + incident_date: '2021-07-30', + epoch_incident_date: 1627603200, + incident_title: + 'AI Tools Failed to Sufficiently Predict COVID Patients, Some Potentially Harmful', + incident_description: + 'AI tools failed to sufficiently predict COVID patients, some potentially harmful.', + objectID: '1551', + _snippetResult: { + description: { + value: + 'When covid-19 struck Europe in March 2020, hospitals were plunged into a health crisis that was still badly understood. “Doctors really didn’t have a clue how to manage these patients,” says Laure Wyn', + matchLevel: 'none', + }, + text: { + value: + 'When covid-19 struck Europe in March 2020, hospitals were plunged into a health crisis', + matchLevel: 'none', + }, + }, + _highlightResult: { + description: { + value: + 'When covid-19 struck Europe in March 2020, hospitals were plunged into a health crisis that was still badly understood. “Doctors really didn’t have a clue how to manage these patients,” says Laure Wyn', + matchLevel: 'none', + matchedWords: [], + }, + title: { + value: 'Hundreds of AI tools have been built to catch covid. None of them helped.', + matchLevel: 'none', + matchedWords: [], + }, + text: { + value: + 'When covid-19 struck Europe in March 2020, hospitals were plunged into a health crisis that was still badly understood. “Doctors really didn’t have a clue how to manage these patients,” says Laure Wynants, an epidemiologist at Maastricht University in the Netherlands, who studies predictive tools.\n\nBut there was data coming out of China, which had a four-month head start in the race to beat the pandemic. If machine-learning algorithms could be trained on that data to help doctors understand what they were seeing and make decisions, it just might save lives. “I thought, ‘If there’s any time that AI could prove its usefulness, it’s now,’” says Wynants. “I had my hopes up.”\n\nIt never happened—but not for lack of effort. Research teams around the world stepped up to help. The AI community, in particular, rushed to develop software that many believed would allow hospitals to diagnose or triage patients faster, bringing much-needed support to the front lines—in theory.\n\nIn the end, many hundreds of predictive tools were developed. None of them made a real difference, and some were potentially harmful.\n\nThat’s the damning conclusion of multiple studies published in the last few months. In June, the Turing Institute, the UK’s national center for data science and AI, put out a report summing up discussions at a series of workshops it held in late 2020. The clear consensus was that AI tools had made little, if any, impact in the fight against covid.\n\nNot fit for clinical use\n\nThis echoes the results of two major studies that assessed hundreds of predictive tools developed last year. Wynants is lead author of one of them, a review in the British Medical Journal that is still being updated as new tools are released and existing ones tested. She and her colleagues have looked at 232 algorithms for diagnosing patients or predicting how sick those with the disease might get. They found that none of them were fit for clinical use. Just two have been singled out as being promising enough for future testing.\n\n“It’s shocking,” says Wynants. “I went into it with some worries, but this exceeded my fears.”\n\nWynants’s study is backed up by another large review carried out by Derek Driggs, a machine-learning researcher at the University of Cambridge, and his colleagues, and published in Nature Machine Intelligence. This team zoomed in on deep-learning models for diagnosing covid and predicting patient risk from medical images, such as chest x-rays and chest computer tomography (CT) scans. They looked at 415 published tools and, like Wynants and her colleagues, concluded that none were fit for clinical use.\n\n“This pandemic was a big test for AI and medicine,” says Driggs, who is himself working on a machine-learning tool to help doctors during the pandemic. “It would have gone a long way to getting the public on our side,” he says. “But I don’t think we passed that test.”\n\nBoth teams found that researchers repeated the same basic errors in the way they trained or tested their tools. Incorrect assumptions about the data often meant that the trained models did not work as claimed.\n\nWynants and Driggs still believe AI has the potential to help. But they are concerned that it could be harmful if built in the wrong way because they could miss diagnoses or underestimate risk for vulnerable patients. “There is a lot of hype about machine-learning models and what they can do today,” says Driggs.\n\nUnrealistic expectations encourage the use of these tools before they are ready. Wynants and Driggs both say that a few of the algorithms they looked at have already been used in hospitals, and some are being marketed by private developers. “I fear that they may have harmed patients,” says Wynants.\n\nSo what went wrong? And how do we bridge that gap? If there’s an upside, it is that the pandemic has made it clear to many researchers that the way AI tools are built needs to change. “The pandemic has put problems in the spotlight that we’ve been dragging along for some time,” says Wynants.\n\nWhat went wrong\n\nMany of the problems that were uncovered are linked to the poor quality of the data that researchers used to develop their tools. Information about covid patients, including medical scans, was collected and shared in the middle of a global pandemic, often by the doctors struggling to treat those patients. Researchers wanted to help quickly, and these were the only public data sets available. But this meant that many tools were built using mislabeled data or data from unknown sources.\n\nDriggs highlights the problem of what he calls Frankenstein data sets, which are spliced together from multiple sources and can contain duplicates. This means that some tools end up being tested on the same data they were trained on, making them appear more accurate than they are.\n\nIt also muddies the origin of certain data sets. This can mean that researchers miss important features that skew the training of their models. Many unwittingly used a data set that contained chest scans of children who did not have covid as their examples of what non-covid cases looked like. But as a result, the AIs learned to identify kids, not covid.\n\nDriggs’s group trained its own model using a data set that contained a mix of scans taken when patients were lying down and standing up. Because patients scanned while lying down were more likely to be seriously ill, the AI learned wrongly to predict serious covid risk from a person’s position.\n\nIn yet other cases, some AIs were found to be picking up on the text font that certain hospitals used to label the scans. As a result, fonts from hospitals with more serious caseloads became predictors of covid risk.\n\nErrors like these seem obvious in hindsight. They can also be fixed by adjusting the models, if researchers are aware of them. It is possible to acknowledge the shortcomings and release a less accurate, but less misleading model. But many tools were developed either by AI researchers who lacked the medical expertise to spot flaws in the data or by medical researchers who lacked the mathematical skills to compensate for those flaws.\n\nA more subtle problem Driggs highlights is incorporation bias, or bias introduced at the point a data set is labeled. For example, many medical scans were labeled according to whether the radiologists who created them said they showed covid. But that embeds, or incorporates, any biases of that particular doctor into the ground truth of a data set. It would be much better to label a medical scan with the result of a PCR test rather than one doctor’s opinion, says Driggs. But there isn’t always time for statistical niceties in busy hospitals.\n\nThat hasn’t stopped some of these tools from being rushed into clinical practice. Wynants says it isn’t clear which ones are being used or how. Hospitals will sometimes say that they are using a tool only for research purposes, which makes it hard to assess how much doctors are relying on them. “There’s a lot of secrecy,” she says.\n\nWynants asked one company that was marketing deep-learning algorithms to share information about its approach but did not hear back. She later found several published models from researchers tied to this company, all of them with a high risk of bias. “We don’t actually know what the company implemented,” she says.\n\nAccording to Wynants, some hospitals are even signing nondisclosure agreements with medical AI vendors. When she asked doctors what algorithms or software they were using, they sometimes told her they weren’t allowed to say.\n\nHow to fix it\n\nWhat’s the fix? Better data would help, but in times of crisis that’s a big ask. It’s more important to make the most of the data sets we have. The simplest move would be for AI teams to collaborate more with clinicians, says Driggs. 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generation': 9, + 'toxicity detection': 9, + 'voice recognition': 9, + 'augmented reality (AR) game Pokémon Go': 8, + 'natural language processing': 8, + 'speech recognition': 8, + 'Design Phone Cases': 7, + 'assign risk': 7, + classification: 7, + 'deepfake audio generation': 7, + 'predict healthcare needs': 7, + 'sentiment analysis': 7, + editing: 6, + 'fraud risk prediction': 6, + 'identify hate speech': 6, + 'webpage maintenance': 6, + 'Crop Images': 5, + 'Personal Assistant': 5, + 'automatic emergency braking': 5, + 'content recommendation': 5, + 'image cropping': 5, + 'security monitor': 5, + 'speech interpretation': 5, + 'Digital Assistant': 4, + 'application screening': 4, + 'audit selection': 4, + 'candidate assessment': 4, + 'diagnose sepsis': 4, + 'emotion recognition': 4, + 'estimating house prices': 4, + 'facial expression recognition': 4, + 'home valuation': 4, + 'identify sepsis': 4, + 'object classification': 4, + 'price determination': 4, + 'product promotion': 4, + 'real estate market forecasting': 4, + 'search engine': 4, + 'search result ranking': 4, + 'sepsis prediction': 4, + transportation: 4, + 'virtual interview': 4, + 'worker management': 4, + delivery: 3, + 'ethical decision making': 3, + 'gender classification': 3, + 'move shelves': 3, + 'performance tracking': 3, + 'reply to questions': 3, + 'video recommender': 3, + 'water quality testing': 3, + 'worker monitoring': 3, + 'assess applicants': 2, + 'audio deepfake': 2, + 'automated speech recognition': 2, + 'automatic tracking': 2, + 'ball detection': 2, + 'chant scripture': 2, + cheerleading: 2, + concierge: 2, + 'driver surveillance': 2, + 'emotion detection': 2, + 'entertain guests': 2, + 'event detection': 2, + 'event prediction': 2, + 'exercise demonstration': 2, + 'eye tracking': 2, + 'facial grouping': 2, + 'household administration': 2, + 'identification or detection': 2, + 'product recognition': 2, + 'rank the reliability of workers': 2, + 'risk assessment': 2, + 'safe driving detection': 2, + 'semi-autonomous driving': 2, + 'shift assignment': 2, + 'speech-to-text': 2, + suggestion: 2, + 'weapon detection': 2, + 'welfare determination': 2, + 'ADA website compliance': 1, + 'Burmese to English translation': 1, + Matching: 1, + 'accessibility compliance': 1, + 'advertisement screening': 1, + 'autonomous food delivery': 1, + 'benefits allocation': 1, + 'brand safety detection': 1, + 'content suggestion': 1, + 'customer service': 1, + database: 1, + 'detect policy violations': 1, + 'enforcement of community guidelines': 1, + 'engagement optimization': 1, + 'exam proctoring': 1, + 'fact-check': 1, + 'human facial image transformation': 1, + 'identify misinformation': 1, + 'image editing': 1, + 'image organization': 1, + 'image recognition': 1, + 'image splicing': 1, + 'job screening': 1, + 'law enforcement': 1, + 'license plate matching': 1, + 'license plate recognition': 1, + 'marketing material generation': 1, + 'optimize movement to win a computer game': 1, + 'passport photo quality check': 1, + 'performance prediction': 1, + 'predict grades on exam': 1, + 'predict necessary amount of medical coverage': 1, + 'predict recidivism risk': 1, + 'recipe generation': 1, + 'recruitment screening': 1, + 'remote proctoring': 1, + 'risk prediction': 1, + robotics: 1, + 'spam filter': 1, + 'stoplight recognition': 1, + 'vaccine allocation': 1, + 'video classification': 1, + 'video suggestion': 1, + 'voice alteration': 1, + 'weapons detection': 1, + 'word association': 1, + }, + 'CSETv1.Deployed': { + yes: 1442, + no: 103, + maybe: 37, + }, + 'CSETv1.Embedded': { + no: 976, + yes: 592, + maybe: 14, + }, + 'CSETv1.Injuries': { + '0': 1480, + '1': 66, + '2': 5, + '10': 27, + '40': 1, + '54': 17, + '416': 3, + '1391': 12, + '55000': 3, + }, + classifications: { + 'CSETv1_Annotator-1:User Test in Controlled Conditions:no': 1841, + 'CSETv1_Annotator-1:Impact on Critical Services:no': 1799, + 'CSETv1_Annotator-1:Multiple AI Interaction:no': 1797, + 'CSETv1_Annotator-1:Producer Test in Controlled Conditions:no': 1785, + 'CSETv1_Annotator-1:Report, Test, or Study of data:no': 1755, + 'CSETv1_Annotator-1:Deployed:yes': 1715, + 'CSETv1_Annotator-1:Involving Minor:no': 1701, + 'CSETv1_Annotator-1:Rights Violation:no': 1690, + 'CSETv1_Annotator-1:Producer Test in Operational Conditions:no': 1683, + 'CSETv1_Annotator-1:User Test in Operational Conditions:no': 1567, + 'CSETv1:User Test in Controlled Conditions:no': 1555, + 'CSETv1_Annotator-1:Intentional Harm:No. Not intentionally designed to perform harm': 1542, + 'CSETv1_Annotator-1:Public Sector Deployment:no': 1526, + 'CSETv1:Multiple AI Interaction:no': 1525, + 'CSETv1:Report, Test, or Study of data:no': 1511, + 'CSETv1:Producer Test in Controlled Conditions:no': 1509, + 'CSETv1_Annotator-1:Detrimental Content:no': 1499, + 'CSETv1_Annotator-1:Entertainment Industry:no': 1498, + 'CSETv1_Annotator-1:AI System:yes': 1497, + 'CSETv1:Impact on Critical Services:no': 1474, + 'CSETv1:Involving Minor:no': 1469, + 'CSETv1:Intentional Harm:No. Not intentionally designed to perform harm': 1462, + 'CSETv1:Producer Test in Operational Conditions:no': 1453, + 'CSETv1:Deployed:yes': 1442, + 'CSETv1:Clear link to Technology:yes': 1425, + 'CSETv1_Annotator-1:Clear link to Technology:yes': 1422, + 'CSETv1:Entertainment Industry:no': 1401, + 'CSETv1:User Test in Operational Conditions:no': 1367, + 'CSETv1:Rights Violation:no': 1332, + 'CSETv1:Public Sector Deployment:no': 1324, + 'CSETv1:Detrimental Content:no': 1281, + 'CSETv1_Annotator-1:Protected Characteristic:no': 1270, + 'CSETv1_Annotator-1:Harm Distribution Basis:none': 1223, + 'CSETv1:AI System:yes': 1221, + 'CSETv1_Annotator-1:Embedded:no': 1206, + 'CSETv1_Annotator-1:Annotator’s AI special interest intangible harm assessment:no': 1192, + 'CSETv1_Annotator-1:AI Harm Level:none': 1153, + 'CSETv1_Annotator-1:Physical Objects:no': 1150, + 'CSETv1_Annotator-1:Special Interest Intangible Harm:no': 1087, + 'CSETv1:Protected Characteristic:no': 1070, + 'CSETv1:Harm Distribution Basis:none': 1059, + 'CSETv1:AI Harm Level:none': 1003, + 'CSETv1:Embedded:no': 976, + 'CSETv1:Annotator’s AI special interest intangible harm assessment:no': 949, + 'CSETv1:Physical Objects:no': 946, + 'CSETv1_Annotator-1:Location Country (two letters):US': 946, + 'CSETv0:Intent:Accident': 935, + 'CSETv1_Annotator-2:User Test in Controlled Conditions:no': 930, + 'CSETv0:Relevant AI functions:Cognition': 924, + 'CSETv1_Annotator-2:Entertainment Industry:no': 908, + 'CSETv1_Annotator-2:Multiple AI Interaction:no': 904, + 'CSETv1_Annotator-2:Producer Test in Operational Conditions:no': 904, + 'CSETv1_Annotator-2:Impact on Critical Services:no': 901, + 'CSETv1_Annotator-1:Location Region:North America': 899, + 'CSETv0:Nature of End User:Amateur': 896, + 'CSETv0:Relevant AI functions:Perception': 892, + 'CSETv1_Annotator-2:Producer Test in Controlled Conditions:no': 884, + 'CSETv1_Annotator-2:Report, Test, or Study of data:no': 870, + 'CSETv1:Special Interest Intangible Harm:no': 862, + 'CSETv1_Annotator-1:Autonomy Level:Autonomy1': 852, + 'CSETv1_Annotator-2:Intentional Harm:No. Not intentionally designed to perform harm': 850, + 'CSETv1:Autonomy Level:Autonomy1': 847, + 'CSETv1_Annotator-2:Involving Minor:no': 843, + 'CSETv1_Annotator-2:Deployed:yes': 841, + 'CSETv1_Annotator-2:Rights Violation:no': 826, + 'CSETv1_Annotator-2:Public Sector Deployment:no': 819, + 'CSETv1_Annotator-2:Detrimental Content:no': 813, + 'CSETv1_Annotator-1:Tangible Harm:no tangible harm, near-miss, or issue': 794, + 'CSETv1:Location Region:North America': 781, + 'CSETv1_Annotator-2:User Test in Operational Conditions:no': 773, + 'CSETv1:Location Country (two letters):US': 769, + 'CSETv0:Relevant AI functions:Action': 768, + 'CSETv1_Annotator-1:Special Interest Intangible Harm:yes': 732, + 'CSETv1_Annotator-1:Tangible Harm:tangible harm definitively occurred': 719, + 'CSETv0:Physical System:Software only': 704, + 'CSETv1_Annotator-2:AI Harm Level:none': 704, + 'CSETv1:Special Interest Intangible Harm:yes': 703, + 'CSETv1_Annotator-2:Protected Characteristic:no': 689, + 'CSETv1:Tangible Harm:tangible harm definitively occurred': 678, + 'CSETv1_Annotator-2:AI System:yes': 675, + 'CSETv1_Annotator-1:Physical Objects:yes': 664, + 'CSETv1_Annotator-2:Embedded:no': 649, + 'CSETv1_Annotator-1:Annotator’s AI special interest intangible harm assessment:yes': 647, + 'CSETv1_Annotator-1:Embedded:yes': 643, + 'CSETv1_Annotator-1:Sector of Deployment:information and communication': 639, + 'CSETv0:Severity:Negligible': 634, + 'CSETv1:Tangible Harm:no tangible harm, near-miss, or issue': 618, + 'CSETv1:Physical Objects:yes': 615, + 'CSETv1:Annotator’s AI special interest intangible harm assessment:yes': 609, + 'CSETv1_Annotator-2:Annotator’s AI special interest intangible harm assessment:no': 607, + 'CSETv1_Annotator-2:Harm Distribution Basis:none': 596, + 'CSETv1:Embedded:yes': 592, + 'CSETv1_Annotator-1:Protected Characteristic:yes': 565, + 'CSETv1:Sector of Deployment:information and communication': 562, + 'CSETv1_Annotator-2:Special Interest Intangible Harm:no': 551, + 'CSETv1_Annotator-2:Physical Objects:no': 550, + 'CSETv1_Annotator-1:Autonomy Level:Autonomy3': 546, + 'CSETv0:Near Miss:Harm caused': 537, + 'CSETv1_Annotator-2:Clear link to Technology:yes': 522, + 'CSETv1_Annotator-1:Location Region:Global': 519, + 'CSETv0:Problem Nature:Robustness': 507, + 'CSETv1:Protected Characteristic:yes': 505, + 'CSETv0:Problem Nature:Specification': 473, + 'CSETv0:Near Miss:Unclear/unknown': 461, + 'CSETv1_Annotator-2:Location Region:North America': 454, + 'CSETv0:Level of Autonomy:High': 452, + 'CSETv1:Data Inputs:text': 443, + 'CSETv1_Annotator-2:Location Country (two letters):US': 441, + 'CSETv1_Annotator-3:Producer Test in Controlled Conditions:no': 435, + 'CSETv1_Annotator-3:Involving Minor:no': 427, + 'CSETv1_Annotator-3:User Test in Controlled Conditions:no': 424, + 'CSETv1_Annotator-2:Tangible Harm:no tangible harm, near-miss, or issue': 422, + 'CSETv1_Annotator-3:Producer Test in Operational Conditions:no': 420, + 'CSETv1_Annotator-3:Multiple AI Interaction:no': 418, + 'CSETv1_Annotator-2:Clear link to Technology:no': 414, + 'CSETv0:Level of Autonomy:Medium': 413, + 'CSETv1_Annotator-3:Report, Test, or Study of data:no': 412, + 'CSETv1_Annotator-3:Deployed:yes': 409, + 'CSETv1_Annotator-2:Physical Objects:yes': 404, + 'CSETv1:Location Region:Global': 402, + 'CSETv1_Annotator-3:Detrimental Content:no': 394, + 'CSETv1_Annotator-3:Entertainment Industry:no': 391, + 'CSETv1_Annotator-1:Date of Incident Year:2017': 389, + 'CSETv1_Annotator-2:Special Interest Intangible Harm:yes': 389, + 'CSETv1_Annotator-3:Impact on Critical Services:no': 388, + 'CSETv1_Annotator-3:Intentional Harm:No. Not intentionally designed to perform harm': 388, + 'CSETv1_Annotator-2:Tangible Harm:tangible harm definitively occurred': 383, + 'CSETv1:Date of Incident Year:2017': 382, + 'CSETv1_Annotator-1:AI Harm Level:AI tangible harm event': 381, + 'CSETv1_Annotator-2:Autonomy Level:Autonomy1': 379, + 'CSETv1:AI Harm Level:AI tangible harm event': 373, + 'CSETv1:Autonomy Level:Autonomy3': 370, + 'CSETv1_Annotator-1:Autonomy Level:Autonomy2': 369, + 'CSETv1_Annotator-1:Sector of Deployment:transportation and storage': 368, + 'CSETv1_Annotator-1:Harm Distribution Basis:race': 364, + 'CSETv0:Location:Global': 355, + 'CSETv0:Sector of Deployment:Information and communication': 352, + 'CSETv1_Annotator-3:AI System:yes': 350, + 'CSETv1:Harm Distribution Basis:race': 346, + 'CSETv1_Annotator-3:Public Sector Deployment:no': 346, + 'CSETv1_Annotator-3:User Test in Operational Conditions:no': 342, + 'CSETv1_Annotator-1:Clear link to Technology:no': 339, + 'CSETv1_Annotator-2:Annotator’s AI special interest intangible harm assessment:yes': 333, + 'CSETv1_Annotator-3:Rights Violation:no': 322, + 'CSETv1_Annotator-3:Clear link to Technology:yes': 305, + 'CSETv1_Annotator-1:Public Sector Deployment:yes': 304, + 'CSETv1_Annotator-2:Embedded:yes': 301, + 'CSETv0:Problem Nature:Unknown/unclear': 292, + 'CSETv1:Data Inputs:images': 286, + 'CSETv1_Annotator-1:AI System:no': 286, + 'CSETv1_Annotator-1:Data Inputs:images': 283, + 'CSETv1_Annotator-1:User Test in Operational Conditions:yes': 283, + 'CSETv0:Harm Type:Harm to physical health/safety': 282, + 'CSETv1_Annotator-2:Autonomy Level:Autonomy2': 281, + 'CSETv1_Annotator-1:Detrimental Content:yes': 279, + 'CSETv1:Sector of Deployment:transportation and storage': 273, + 'CSETv1:AI System:no': 269, + 'CSETv1_Annotator-2:Sector of Deployment:information and communication': 269, + 'CSETv1:Date of Incident Year:2016': 268, + 'CSETv1_Annotator-2:Date of Incident Year:2017': 267, + 'CSETv0:Severity:Minor': 262, + 'CSETv1:Detrimental Content:yes': 262, + 'CSETv1:Autonomy Level:Autonomy2': 261, + 'CSETv1_Annotator-2:Protected Characteristic:yes': 261, + 'CSETv1_Annotator-2:Data Inputs:Text': 260, + 'CSETv1_Annotator-3:Protected Characteristic:no': 258, + 'CSETv1_Annotator-3:Physical Objects:no': 256, + 'CSETv1_Annotator-1:Sector of Deployment:Arts, entertainment and recreation': 252, + 'CSETv1_Annotator-3:Harm Distribution Basis:none': 252, + 'CSETv1:Public Sector Deployment:yes': 249, + 'CSETv1_Annotator-1:Harm Distribution Basis:sex': 243, + 'CSETv0:Named Entities:Google': 242, + 'CSETv1_Annotator-3:Special Interest Intangible Harm:no': 241, + 'CSETv0:Harm Type:Harm to social or political systems': 239, + 'CSETv1_Annotator-1:Data Inputs:sensor data': 239, + 'CSETv0:Technology Purveyor:Google': 238, + 'CSETv0:Harm Distribution Basis:Race': 235, + 'CSETv1:AI tools and methods:natural language processing': 235, + 'CSETv1:Data Inputs:video': 234, + 'CSETv1_Annotator-1:Entertainment Industry:yes': 229, + 'CSETv1_Annotator-3:Annotator’s AI special interest intangible harm assessment:no': 226, + 'CSETv0:System Developer:Google': 224, + 'CSETv1_Annotator-1:Data Inputs:text': 223, + 'CSETv1_Annotator-1:Date of Incident Year:2016': 223, + 'CSETv1_Annotator-1:Date of Incident Year:2018': 222, + 'CSETv0:AI Techniques:Machine learning': 221, + 'CSETv1_Annotator-2:Autonomy Level:Autonomy3': 221, + 'CSETv0:Harm Type:Financial harm': 220, + 'CSETv0:Near Miss:Near miss': 219, + 'CSETv1_Annotator-3:Autonomy Level:Autonomy1': 219, + 'CSETv1_Annotator-1:Sector of Deployment:law enforcement': 218, + 'CSETv1:AI tools and methods:computer vision': 217, + 'CSETv1_Annotator-1:Data Inputs:video input': 216, + 'CSETv0:Physical System:Vehicle/mobile robot': 215, + 'CSETv1_Annotator-2:AI System:no': 215, + 'CSETv1_Annotator-3:Embedded:yes': 213, + 'CSETv1:User Test in Operational Conditions:yes': 211, + 'CSETv1_Annotator-3:Embedded:no': 211, + 'CSETv0:Problem Nature:Assurance': 207, + 'CSETv1_Annotator-2:Location Region:Global': 205, + 'CSETv1_Annotator-3:AI Harm Level:none': 204, + 'CSETv0:Harm Type:Psychological harm': 202, + 'CSETv1_Annotator-1:Tangible Harm:imminent risk of tangible harm (near miss) did occur': 201, + 'CSETv1:Harm Distribution Basis:sex': 198, + 'CSETv0:Intent:Unclear': 197, + 'CSETv1_Annotator-2:AI tools and methods:Natural Language Processesing': 196, + 'CSETv0:Sector of Deployment:Transportation and storage': 191, + 'CSETv1:Sector of Deployment:Arts, entertainment and recreation': 190, + 'CSETv1_Annotator-3:Location Region:North America': 187, + 'CSETv1_Annotator-3:Tangible Harm:tangible harm definitively occurred': 187, + 'CSETv0:AI Applications:Facial recognition': 185, + 'CSETv1_Annotator-3:Location Country (two letters):US': 185, + 'CSETv1_Annotator-1:Location Region:Europe': 183, + 'CSETv1_Annotator-2:Date of Incident Year:2016': 183, + 'CSETv1_Annotator-3:Annotator’s AI special interest intangible harm assessment:yes': 183, + 'CSETv1:Date of Incident Year:2018': 181, + 'CSETv1:Tangible Harm:imminent risk of tangible harm (near miss) did occur': 180, + 'CSETv1_Annotator-1:Producer Test in Operational Conditions:yes': 180, + 'CSETv1_Annotator-2:User Test in Operational Conditions:yes': 180, + 'CSETv1_Annotator-3:Physical Objects:yes': 180, + 'CSETv1_Annotator-3:Special Interest Intangible Harm:yes': 180, + 'CSETv0:Harm Type:Harm to civil liberties': 179, + 'CSETv1:Location Region:Asia': 177, + 'CSETv0:Nature of End User:Expert': 176, + 'CSETv1_Annotator-3:Sector of Deployment:information and communication': 176, + 'CSETv1_Annotator-2:Harm Distribution Basis:race': 174, + 'CSETv1_Annotator-3:Protected Characteristic:yes': 170, + 'CSETv1:Rights Violation:yes': 168, + 'CSETv1_Annotator-1:Location Region:Asia': 163, + 'CSETv1_Annotator-1:Involving Minor:yes': 157, + 'CSETv0:Sector of Deployment:Arts, entertainment and recreation': 156, + 'CSETv1_Annotator-3:AI Harm Level:AI tangible harm event': 156, + 'CSETv1_Annotator-1:AI Harm Level:AI tangible harm near-miss': 155, + 'CSETv1_Annotator-1:Tangible Harm:non-imminent risk of tangible harm (an issue) occurred': 155, + 'CSETv0:Sector of Deployment:Public administration and defence': 154, + 'CSETv1:Sector of Deployment:wholesale and retail trade': 154, + 'CSETv1_Annotator-1:Data Inputs:camera input': 154, + 'CSETv1_Annotator-1:Sector of Deployment:wholesale and retail trade': 154, + 'CSETv1:Location Region:Europe': 152, + 'CSETv1_Annotator-2:Data Inputs:video': 150, + 'CSETv1_Annotator-3:Tangible Harm:no tangible harm, near-miss, or issue': 149, + 'CSETv0:AI Applications:autonomous driving': 147, + 'CSETv1:Data Inputs:sensor data': 147, + 'CSETv1_Annotator-2:Tangible Harm:non-imminent risk of tangible harm (an issue) occurred': 147, + 'CSETv1_Annotator-1:Date of Incident Year:2023': 145, + 'CSETv0:Harm Distribution Basis:Sex': 144, + 'CSETv1:Data Inputs:audio': 143, + 'CSETv1:Data Inputs:lidar': 143, + 'CSETv1_Annotator-1:Entertainment Industry:maybe': 142, + 'CSETv0:Public Sector Deployment:true': 141, + 'CSETv1_Annotator-3:Date of Incident Year:2017': 141, + 'CSETv1:Entertainment Industry:yes': 140, + 'CSETv0:AI Techniques:Facial recognition': 139, + 'CSETv1:Infrastructure Sectors:transportation': 139, + 'CSETv1_Annotator-3:Location Region:Global': 138, + 'CSETv0:Named Entities:Amazon': 136, + 'CSETv1_Annotator-1:AI tools and methods:natural language processing': 135, + 'GMF:Potential AI Technical Failure:Generalization Failure': 135, + 'CSETv1_Annotator-1:Data Inputs:facial images': 134, + 'CSETv0:AI Techniques:Natural language processing': 133, + 'CSETv0:Harm Type:Harm to physical property': 132, + 'CSETv1_Annotator-1:AI Harm Level:AI tangible harm issue': 132, + 'CSETv1:AI Task:facial recognition': 131, + 'CSETv1:Data Inputs:radar': 131, + 'CSETv0:Technology Purveyor:Amazon': 129, + 'CSETv1:Producer Test in Operational Conditions:yes': 129, + 'CSETv1_Annotator-1:Date of Incident Year:2020': 129, + 'CSETv1_Annotator-2:AI tools and methods:computer vision': 129, + 'CSETv1_Annotator-1:Date of Incident Year:2021': 128, + 'CSETv1_Annotator-1:Date of Incident Year:2022': 128, + 'CSETv1_Annotator-2:Public Sector Deployment:yes': 128, + 'CSETv1_Annotator-2:AI Harm Level:AI tangible harm event': 126, + 'CSETv1_Annotator-2:Location Region:Asia': 126, + 'CSETv1:Sector of Deployment:administrative and support service activities': 125, + 'CSETv1_Annotator-3:Autonomy Level:Autonomy3': 125, + 'CSETv1_Annotator-1:Rights Violation:yes': 124, + 'CSETv1_Annotator-1:Sector of Deployment:public administration': 123, + 'CSETv0:Infrastructure Sectors:Transportation': 122, + 'CSETv0:Level of Autonomy:Low': 122, + 'CSETv1:Sector of Deployment:public administration': 117, + 'CSETv1_Annotator-1:AI Task:facial recognition': 117, + 'CSETv1_Annotator-2:Harm Distribution Basis:sex': 116, + 'CSETv1_Annotator-1:Harm Distribution Basis:nation of origin, citizenship, immigrant status': 115, + 'CSETv1_Annotator-1:Sector of Deployment:professional, scientific and technical activities': 115, + 'CSETv0:Severity:Unclear/unknown': 114, + 'CSETv1:Sector of Deployment:law enforcement': 114, + 'CSETv1_Annotator-1:AI Task:navigation': 114, + 'CSETv0:AI Applications:decision support': 113, + 'CSETv0:System Developer:Amazon': 112, + 'CSETv1:AI Task:navigation': 112, + 'CSETv1_Annotator-2:Involving Minor:yes': 111, + 'CSETv1_Annotator-2:Detrimental Content:yes': 110, + 'CSETv0:Physical System:Consumer device': 108, + 'CSETv1_Annotator-1:Clear link to Technology:maybe': 108, + 'CSETv1_Annotator-1:Intentional Harm:Yes. Intentionally designed to perform harm and did create intended harm': 107, + 'CSETv1_Annotator-2:Sector of Deployment:transportation and storage': 107, + 'CSETv1_Annotator-3:AI tools and methods:unclear': 107, + 'CSETv0:AI Applications:recommendation engine': 106, + 'CSETv1:Impact on Critical Services:yes': 106, + 'CSETv1:Involving Minor:yes': 106, + 'CSETv1_Annotator-3:Data Inputs:text': 106, + 'CSETv0:Severity:Moderate': 105, + 'CSETv1:Clear link to Technology:no': 105, + 'CSETv1:AI Harm Level:AI tangible harm near-miss': 104, + 'CSETv1:Autonomy Level:unclear': 104, + 'CSETv1_Annotator-1:AI tools and methods:facial recognition': 104, + 'CSETv1_Annotator-2:Data Inputs:images': 104, + 'CSETv0:Lives Lost:true': 103, + 'CSETv0:Technology Purveyor:Tesla': 103, + 'CSETv1:Deployed:no': 103, + 'CSETv1_Annotator-1:AI Task:autonomous navigation': 103, + 'CSETv0:Severity:Severe': 102, + 'CSETv1_Annotator-1:Autonomy Level:unclear': 102, + 'CSETv1_Annotator-1:Date of Incident Year:2014': 102, + 'CSETv1_Annotator-2:Sector of Deployment:wholesale and retail trade': 102, + 'CSETv1_Annotator-2:Location Region:Europe': 101, + 'CSETv1:Data Inputs:facial images': 100, + 'CSETv1:Harm Distribution Basis:nation of origin, citizenship, immigrant status': 100, + 'CSETv1_Annotator-3:Harm Distribution Basis:race': 100, + 'CSETv1_Annotator-1:AI Task:text generation': 99, + 'CSETv1_Annotator-2:Sector of Deployment:administrative and support service activities': 99, + 'CSETv1:Tangible Harm:non-imminent risk of tangible harm (an issue) occurred': 97, + 'CSETv1_Annotator-1:AI Task:semi-autonomous navigation': 96, + 'CSETv1:Data Inputs:camera input': 95, + 'CSETv1:Date of Incident Year:2014': 95, + 'CSETv1_Annotator-2:Date of Incident Year:2015': 95, + 'CSETv1_Annotator-2:Rights Violation:yes': 94, + 'GMF:Potential AI Technical Failure:Context Misidentification': 94, + 'CSETv1:AI System:maybe': 92, + 'CSETv1:Sector of Deployment:human health and social work activities': 92, + 'CSETv1_Annotator-1:Detrimental Content:maybe': 91, + 'CSETv1_Annotator-3:User Test in Operational Conditions:yes': 91, + 'CSETv0:AI Applications:image classification': 90, + 'CSETv0:Sector of Deployment:Administrative and support service activities': 90, + 'CSETv1_Annotator-1:Harm Distribution Basis:religion': 90, + 'CSETv1_Annotator-1:Location Region:Oceania': 90, + 'CSETv1_Annotator-3:Rights Violation:yes': 90, + 'CSETv1_Annotator-2:Data Inputs:audio': 89, + 'CSETv0:Level of Autonomy:Unclear/unknown': 88, + 'CSETv1:Harm Distribution Basis:religion': 88, + 'CSETv1_Annotator-1:AI Task:content generation': 88, + 'CSETv1_Annotator-1:AI System:maybe': 86, + 'CSETv1_Annotator-1:Data Inputs:radar': 86, + 'CSETv0:AI Applications:Natural language processing': 85, + 'CSETv0:Intent:Deliberate or expected': 85, + 'CSETv1_Annotator-1:Harm Distribution Basis:financial means': 84, + 'CSETv1:Date of Incident Year:2020': 83, + 'CSETv1_Annotator-2:Report, Test, or Study of data:yes': 83, + 'CSETv1:Date of Incident Year:2021': 82, + 'CSETv1:Rights Violation:maybe': 82, + 'CSETv1_Annotator-2:AI Harm Level:AI tangible harm issue': 82, + 'CSETv1_Annotator-3:Clear link to Technology:no': 82, + 'CSETv1_Annotator-3:Sector of Deployment:law enforcement': 82, + 'CSETv1_Annotator-1:Producer Test in Controlled Conditions:yes': 80, + 'CSETv0:Named Entities:Tesla': 79, + 'CSETv1_Annotator-1:AI Task:chat bot': 79, + 'CSETv1_Annotator-1:Data Inputs:spatial data': 79, + 'CSETv1_Annotator-1:Deployed:no': 79, + 'CSETv0:AI Applications:image recognition': 78, + 'CSETv1:Date of Incident Year:2015': 78, + 'CSETv1_Annotator-1:Data Inputs:search queries': 78, + 'CSETv1_Annotator-3:Public Sector Deployment:yes': 78, + 'CSETv1:AI tools and methods:Natural Language Processesing': 77, + 'CSETv1:Date of Incident Year:2023': 77, + 'CSETv1_Annotator-1:AI tools and methods:machine learning': 77, + 'CSETv1_Annotator-1:Harm Distribution Basis:sexual orientation or gender identity': 77, + 'CSETv0:AI Techniques:Unclear': 76, + 'CSETv1:Lives Lost:1': 76, + 'CSETv1_Annotator-2:Deployed:no': 76, + 'CSETv0:AI Techniques:Tesla Autopilot': 75, + 'CSETv0:System Developer:Tesla': 75, + 'CSETv1:Infrastructure Sectors:healthcare and public health': 75, + 'CSETv1_Annotator-1:Deployed:maybe': 75, + 'CSETv1_Annotator-2:Date of Incident Year:2018': 74, + 'CSETv1_Annotator-2:Infrastructure Sectors:transportation': 74, + 'CSETv1:Harm Distribution Basis:sexual orientation or gender identity': 73, + 'CSETv1_Annotator-1:Data Inputs:radar input': 73, + 'CSETv1_Annotator-2:Autonomy Level:unclear': 73, + 'CSETv1:Sector of Deployment:professional, scientific and technical activities': 71, + 'CSETv1_Annotator-1:Date of Incident Year:2015': 71, + 'CSETv1_Annotator-1:Sector of Deployment:financial and insurance activities': 71, + 'CSETv1:Intentional Harm:Yes. Intentionally designed to perform harm and did create intended harm': 70, + 'CSETv1_Annotator-3:Sector of Deployment:transportation and storage': 70, + 'CSETv0:Relevant AI functions:Unclear': 69, + 'CSETv1:Producer Test in Controlled Conditions:yes': 69, + 'CSETv1:Sector of Deployment:financial and insurance activities': 69, + 'GMF:Known AI Technology:Face Detection': 69, + 'CSETv1:Data Inputs:search queries': 68, + 'CSETv1_Annotator-1:Data Inputs:voice input': 68, + 'CSETv1_Annotator-1:Sector of Deployment:human health and social work activities': 68, + 'CSETv1_Annotator-2:Infrastructure Sectors:healthcare and public health': 68, + 'CSETv1_Annotator-2:Sector of Deployment:human health and social work activities': 68, + 'CSETv1:Physical System Type:Manufacturing Robot': 67, + 'CSETv1:Sector of Deployment:manufacturing': 67, + 'CSETv1_Annotator-2:Sector of Deployment:manufacturing': 67, + 'CSETv1:AI Harm Level:AI tangible harm issue': 66, + 'CSETv1:Injuries:1': 66, + 'CSETv1_Annotator-1:Data Inputs:user queries': 66, + 'CSETv1_Annotator-1:Lives Lost:1': 66, + 'CSETv1_Annotator-1:Location Country (two letters):AU': 66, + 'CSETv1_Annotator-2:Producer Test in Controlled Conditions:yes': 66, + 'CSETv1_Annotator-3:Data Inputs:video': 65, + 'CSETv0:Named Entities:Microsoft': 64, + 'CSETv1:Data Inputs:speech': 64, + 'CSETv1:Location Region:Oceania': 64, + 'CSETv1_Annotator-1:AI Task:self-driving': 64, + 'CSETv1_Annotator-1:Data Inputs:geospatial data': 64, + 'CSETv1_Annotator-2:AI System:maybe': 64, + 'CSETv1_Annotator-2:Date of Incident Year:2023': 64, + 'CSETv1_Annotator-2:Sector of Deployment:financial and insurance activities': 64, + 'CSETv1:Operating Conditions:operationally representative': 63, + 'CSETv1:Report, Test, or Study of data:yes': 63, + 'CSETv0:AI Applications:self-driving vehicle': 62, + 'CSETv0:AI Techniques:Deep learning': 62, + 'CSETv0:Named Entities:Knightscope': 62, + 'CSETv0:Named Entities:Knightscope K5': 62, + 'CSETv0:System Developer:Knightscope': 62, + 'CSETv0:Technology Purveyor:Knightscope': 62, + 'CSETv1:AI Task:patrolling': 62, + 'CSETv1_Annotator-1:AI Task:virtual assistant technology': 62, + 'CSETv1_Annotator-1:Physical System Type:rocket/egg shaped, 300 pound, 5 ft. tall security robot': 62, + 'CSETv1_Annotator-2:Data Inputs:lidar': 62, + 'CSETv1_Annotator-2:Location Region:Oceania': 62, + 'CSETv1_Annotator-3:Location Region:Asia': 62, + 'CSETv1:Data Inputs:video input': 61, + 'CSETv1:Data Inputs:voice': 61, + 'CSETv1_Annotator-1:Injuries:1': 61, + 'CSETv1_Annotator-2:Data Inputs:voice': 61, + 'CSETv0:Sector of Deployment:Financial and insurance activities': 60, + 'CSETv1_Annotator-1:Data Inputs:lidar': 60, + 'CSETv1_Annotator-1:Data Inputs:user prompts': 60, + 'CSETv1_Annotator-2:Intentional Harm:Yes. Intentionally designed to perform harm and did create intended harm': 60, + 'CSETv1_Annotator-2:Sector of Deployment:law enforcement': 60, + 'CSETv0:Harm Distribution Basis:National origin or immigrant status': 59, + 'CSETv1:Location Country (two letters):RU': 59, + 'CSETv1:Physical System Type:none': 59, + 'CSETv1_Annotator-1:AI tools and methods:voice recognition': 59, + 'CSETv1_Annotator-1:Data Inputs:speech': 59, + 'CSETv1_Annotator-1:Report, Test, or Study of data:maybe': 59, + 'CSETv1_Annotator-2:Physical System Type:none': 59, + 'GMF:Known AI Technical Failure:Unsafe Exposure or Access': 59, + 'CSETv1:AI Task:virtual assistant technology': 58, + 'CSETv1:Data Inputs:photos': 58, + 'CSETv0:AI Techniques:environmental sensing': 57, + 'CSETv0:Named Entities:Stanford Shopping Center': 57, + 'CSETv1:AI Task:security': 57, + 'CSETv1:Data Inputs:road data': 57, + 'CSETv1:Physical System Type:rocket/egg shaped, 300 pound, 5 ft. tall security robot': 57, + 'CSETv1_Annotator-1:AI Task:patrolling': 57, + 'CSETv1_Annotator-1:AI Task:security': 57, + 'CSETv1_Annotator-2:AI tools and methods:Text to Speach': 57, + 'CSETv1_Annotator-2:Data Inputs:road data': 57, + 'CSETv1_Annotator-2:Operating Conditions:operationally representative': 57, + 'CSETv1_Annotator-1:Location Country (two letters):CN': 56, + 'CSETv1_Annotator-3:Date of Incident Year:2016': 56, + 'GMF:Potential AI Technical Failure:Dataset Imbalance': 56, + 'CSETv0:AI Techniques:open-source': 55, + 'CSETv0:Technology Purveyor:Microsoft': 55, + 'CSETv1:AI Task:object detection': 55, + 'CSETv1:AI tools and methods:facial recognition': 55, + 'CSETv1:AI tools and methods:voice recognition': 55, + 'CSETv1_Annotator-1:Physical Objects:maybe': 55, + 'CSETv1_Annotator-1:Report, Test, or Study of data:yes': 55, + 'CSETv1_Annotator-1:Rights Violation:maybe': 55, + 'CSETv1_Annotator-1:Sector of Deployment:manufacturing': 55, + 'CSETv1_Annotator-2:AI tools and methods:Audio Transcription': 55, + 'CSETv1_Annotator-2:Physical System Type:Manufacturing Robot': 55, + 'GMF:Known AI Technical Failure:Distributional Bias': 55, + 'GMF:Known AI Technology:Recurrent Neural Network': 55, + 'CSETv1:AI Task:autonomous driving': 54, + 'CSETv1:AI tools and methods:neural networks': 54, + 'CSETv1_Annotator-1:Data Inputs:audio inputs': 54, + 'GMF:Potential AI Technology:Regression': 54, + 'CSETv0:Harm Distribution Basis:Financial means': 53, + 'CSETv1:AI Task:self-driving': 53, + 'CSETv1:Date of Incident Year:2013': 53, + 'CSETv1:Location Country (two letters):': 53, + 'CSETv1_Annotator-1:AI Task:image identification': 53, + 'CSETv1_Annotator-2:Infrastructure Sectors:communications': 53, + 'GMF:Known AI Technology:Distributional Learning': 53, + 'CSETv0:AI Techniques:image classification': 52, + 'CSETv1:Clear link to Technology:maybe': 52, + 'CSETv1:Data Inputs:spatial data': 52, + 'CSETv1_Annotator-1:Date of Incident Year:2019': 52, + 'CSETv1_Annotator-1:Sector of Deployment:Education': 52, + 'GMF:Potential AI Technical Failure:Underspecification': 52, + 'CSETv0:Location:Palo Alto, CA': 51, + 'CSETv0:Named Entities:Apple': 51, + 'CSETv0:Named Entities:Joshua Brown': 51, + 'CSETv0:Technology Purveyor:Apple': 51, + 'CSETv1:AI Task:object recognition': 51, + 'CSETv1:AI tools and methods:audio transcription': 51, + 'CSETv1:AI tools and methods:machine learning': 51, + 'CSETv1:Data Inputs:infrared camera': 51, + 'CSETv1:Data Inputs:personal data': 51, + 'CSETv1_Annotator-1:Data Inputs:infrared camera': 51, + 'CSETv1_Annotator-2:Impact on Critical Services:yes': 51, + 'CSETv1_Annotator-2:Physical System Type:Amazon Alexa': 51, + 'CSETv1_Annotator-1:Special Interest Intangible Harm:maybe': 50, + 'GMF:Known AI Technology:Convolutional Neural Network': 50, + 'CSETv0:AI Applications:chatbot': 49, + 'CSETv0:Harm Distribution Basis:Religion': 49, + 'CSETv1:AI Task:chat bot': 49, + 'CSETv1_Annotator-1:AI Task:smart suggestions': 49, + 'CSETv1:Multiple AI Interaction:yes': 48, + 'CSETv1_Annotator-1:AI Harm Level:unclear': 48, + 'CSETv1_Annotator-1:Date of Incident Year:2013': 48, + 'CSETv1_Annotator-1:Infrastructure Sectors:transportation': 48, + 'CSETv1_Annotator-1:Multiple AI Interaction:yes': 48, + 'CSETv1_Annotator-3:AI Harm Level:unclear': 48, + 'CSETv1_Annotator-3:Data Inputs:audio': 48, + 'CSETv1_Annotator-3:Location Region:Europe': 48, + 'CSETv1:Data Inputs:image alt tags': 47, + 'CSETv1_Annotator-1:Data Inputs:image alt tags': 47, + 'CSETv1_Annotator-2:AI Task:autonomous driving': 47, + 'CSETv1_Annotator-3:Impact on Critical Services:yes': 47, + 'CSETv0:AI Techniques:autonomous vehicle': 46, + 'CSETv1:AI Task:text generation': 46, + 'CSETv1_Annotator-1:Impact on Critical Services:yes': 46, + 'CSETv1_Annotator-2:Location Country (two letters):': 46, + 'CSETv0:System Developer:Apple': 45, + 'CSETv1:AI tools and methods:image mapping': 45, + 'CSETv1:AI tools and methods:point mapping': 45, + 'CSETv1:Data Inputs:dot projector': 45, + 'CSETv1:Location Country (two letters):CN': 45, + 'CSETv1:Physical System Type:Apple iPhone X': 45, + 'CSETv1_Annotator-1:AI Task:image generation': 45, + 'CSETv1_Annotator-1:AI tools and methods:facial reconstruction': 45, + 'CSETv1_Annotator-1:AI tools and methods:image mapping': 45, + 'CSETv1_Annotator-1:AI tools and methods:image reconstruction': 45, + 'CSETv1_Annotator-1:AI tools and methods:point mapping': 45, + 'CSETv1_Annotator-1:Data Inputs:dot projector': 45, + 'CSETv1_Annotator-1:Physical System Type:Apple iPhone X': 45, + 'CSETv1_Annotator-2:Date of Incident Year:2020': 45, + 'CSETv1_Annotator-2:Producer Test in Operational Conditions:yes': 45, + 'CSETv1_Annotator-3:AI System:no': 45, + 'CSETv1_Annotator-3:Data Inputs:radar': 45, + 'CSETv1_Annotator-3:Harm Distribution Basis:sex': 45, + 'CSETv1_Annotator-3:Harm Distribution Basis:sexual orientation or gender identity': 45, + 'GMF:Known AI Goal:Autonomous Driving': 45, + 'GMF:Potential AI Technical Failure:Unauthorized Data': 45, + 'GMF:Potential AI Technology:Image Segmentation': 45, + 'CSETv0:AI Applications:voice recognition': 44, + 'CSETv0:System Developer:Microsoft': 44, + 'CSETv1:AI Task:search optimization': 44, + 'CSETv1:Data Inputs:GPS': 44, + 'CSETv1:Harm Distribution Basis:financial means': 44, + 'CSETv1:Intentional Harm:unclear': 44, + 'CSETv1_Annotator-2:Entertainment Industry:yes': 44, + 'CSETv1_Annotator-3:Harm Distribution Basis:religion': 44, + 'GMF:Potential AI Technology:Classification': 44, + 'GMF:Potential AI Technology:Convolutional Neural Network': 44, + 'CSETv0:AI Techniques:language recognition': 43, + 'CSETv1:AI Task:autonomous navigation': 43, + 'CSETv1:Date of Incident Year:2011': 43, + 'CSETv1:Date of Incident Year:2019': 43, + 'CSETv1_Annotator-1:AI Task:content moderation': 43, + 'CSETv1_Annotator-1:AI Task:search optimization': 43, + 'CSETv1_Annotator-1:AI Task:search suggestion': 43, + 'CSETv1_Annotator-1:Location Country (two letters):DE': 43, + 'CSETv1_Annotator-2:AI Task:self driving': 43, + 'CSETv1_Annotator-2:Multiple AI Interaction:yes': 43, + 'GMF:Known AI Technology:Language Modeling': 43, + 'CSETv0:Location:Los Angeles, CA': 42, + 'CSETv1:AI tools and methods:unclear': 42, + 'CSETv1_Annotator-1:Date of Incident Year:2012': 42, + 'CSETv1_Annotator-1:Sector of Deployment:administrative and support service activities': 42, + 'CSETv1_Annotator-3:Detrimental Content:yes': 42, + 'CSETv0:Named Entities:China': 41, + 'CSETv1:AI Task:search suggestion': 41, + 'CSETv1:Date of Incident Year:2012': 41, + 'CSETv1:Entertainment Industry:maybe': 41, + 'CSETv1:Harm Distribution Basis:disability': 41, + 'CSETv1:Location Country (two letters):IN': 41, + 'CSETv1:Sector of Deployment:Education': 41, + 'CSETv1_Annotator-1:Data Inputs:age': 41, + 'CSETv1_Annotator-1:Harm Distribution Basis:disability': 41, + 'CSETv1_Annotator-1:Lives Lost:2': 41, + 'CSETv1_Annotator-2:Date of Incident Year:2014': 41, + 'CSETv1_Annotator-2:Location Country (two letters):IN': 41, + 'CSETv1_Annotator-3:AI System:maybe': 41, + 'CSETv1_Annotator-3:Autonomy Level:Autonomy2': 41, + 'GMF:Potential AI Technical Failure:Underfitting': 41, + 'CSETv0:AI Applications:environmental sensing': 40, + 'CSETv0:AI Techniques:virtual assistant': 40, + 'CSETv0:Harm Distribution Basis:Sexual orientation or gender identity': 40, + 'CSETv1:AI Task:Image search': 40, + 'CSETv1:Location Country (two letters):AU': 40, + 'CSETv1_Annotator-1:Harm Distribution Basis:ideology': 40, + 'CSETv1_Annotator-2:AI Task:Personal Assistant': 40, + 'CSETv1_Annotator-2:Date of Incident Year:2021': 40, + 'CSETv1_Annotator-3:Tangible Harm:imminent risk of tangible harm (near miss) did occur': 40, + 'CSETv0:AI Techniques:Amazon Alexa': 39, + 'CSETv0:Location:Australia': 39, + 'CSETv0:Named Entities:Alan Tudge': 39, + 'CSETv0:Named Entities:Centrelink': 39, + 'CSETv0:Named Entities:Centrelink Master Program': 39, + 'CSETv0:Named Entities:Department of Human Services': 39, + 'CSETv0:Named Entities:Richard Glenn': 39, + 'CSETv0:System Developer:Centrelink Master Program': 39, + 'CSETv0:Technology Purveyor:Centrelink Master Program': 39, + 'CSETv0:Technology Purveyor:Department of Human Services': 39, + 'CSETv1:Data Inputs:income data': 39, + 'CSETv1:Data Inputs:tabular': 39, + 'CSETv1:Data Inputs:tax reports': 39, + 'CSETv1:Detrimental Content:maybe': 39, + 'CSETv1_Annotator-1:Data Inputs:income data': 39, + 'CSETv1_Annotator-1:Data Inputs:laser input': 39, + 'CSETv1_Annotator-1:Data Inputs:tax reports': 39, + 'CSETv1_Annotator-1:Data Inputs:words': 39, + 'CSETv1_Annotator-1:Infrastructure Sectors:government facilities': 39, + 'CSETv1_Annotator-2:Data Inputs:amazon account': 39, + 'CSETv1_Annotator-2:Infrastructure Sectors:government facilities': 39, + 'CSETv1_Annotator-2:Lives Lost:1': 39, + 'CSETv1_Annotator-2:Location Country (two letters):AU': 39, + 'CSETv1_Annotator-3:Autonomy Level:unclear': 39, + 'CSETv1_Annotator-3:Data Inputs:lidar': 39, + 'CSETv1_Annotator-3:Sector of Deployment:wholesale and retail trade': 39, + 'GMF:Potential AI Technical Failure:Hardware Failure': 39, + 'GMF:Potential AI Technical Failure:Lack of Capability Control': 39, + 'GMF:Potential AI Technology:Ensemble Aggregation': 39, + 'CSETv0:AI Techniques:natural language processing model': 38, + 'CSETv1:AI Task:predict recidivism': 38, + 'CSETv1:AI tools and methods:object detection': 38, + 'CSETv1_Annotator-1:AI Task:predict recidivism': 38, + 'CSETv1_Annotator-1:AI tools and methods:computer vision': 38, + 'CSETv1_Annotator-2:AI tools and methods:Machine Learning': 38, + 'CSETv1_Annotator-2:Sector of Deployment:other': 38, + 'GMF:Potential AI Technical Failure:Data or Labelling Noise': 38, + 'CSETv0:AI Techniques:law enforcement algorithm': 37, + 'CSETv0:Infrastructure Sectors:Government facilities': 37, + 'CSETv1:AI Task:Rank Applicants': 37, + 'CSETv1:AI Task:translation': 37, + 'CSETv1:Data Inputs:Amazon account information': 37, + 'CSETv1:Deployed:maybe': 37, + 'CSETv1_Annotator-1:AI Task:search result ranking': 37, + 'CSETv1_Annotator-1:Data Inputs:137 questionnaire responses': 37, + 'CSETv1_Annotator-1:Data Inputs:Amazon account information': 37, + 'CSETv1_Annotator-2:AI Task:Rank Applicants': 37, + 'CSETv1_Annotator-3:Date of Incident Year:2020': 37, + 'GMF:Known AI Technical Failure:Misuse': 37, + 'GMF:Known AI Technology:Automatic Speech Recognition': 37, + 'CSETv0:AI Applications:language translation': 36, + 'CSETv1:AI Harm Level:unclear': 36, + 'CSETv1:Data Inputs:Facebook posts': 36, + 'CSETv1:Data Inputs:air quality': 36, + 'CSETv1:Date of Incident Year:2022': 36, + 'CSETv1:Infrastructure Sectors:financial services': 36, + 'CSETv1_Annotator-1:AI Task:transportation': 36, + 'CSETv1_Annotator-1:Location Country (two letters):IN': 36, + 'CSETv1_Annotator-1:Location Country (two letters):RU': 36, + 'CSETv1_Annotator-1:Physical System Type:Tesla Model S': 36, + 'CSETv1_Annotator-3:Entertainment Industry:yes': 36, + 'GMF:Known AI Goal:Face Recognition': 36, + 'CSETv0:AI Applications:Interpreting traffic patterns': 35, + 'CSETv0:AI Applications:image processing': 35, + 'CSETv0:Harm Distribution Basis:Geography': 35, + 'CSETv0:Infrastructure Sectors:Information technology': 35, + 'CSETv0:Named Entities:San Diego TV': 35, + 'CSETv1:AI Task:prediction': 35, + 'CSETv1:AI Task:self driving': 35, + 'CSETv1:AI Task:semi-autonomous navigation': 35, + 'CSETv1:AI Task:surveillance': 35, + 'CSETv1:Data Inputs:Crime Records': 35, + 'CSETv1:Harm Distribution Basis:geography': 35, + 'CSETv1:Location Country (two letters):IE': 35, + 'CSETv1:Physical System Type:Amazon Alexa Echo Dot speaker': 35, + 'CSETv1:Physical System Type:Volvo XC90 SUV': 35, + 'CSETv1_Annotator-1:AI Task:image modification': 35, + 'CSETv1_Annotator-1:AI tools and methods:audio transcription': 35, + 'CSETv1_Annotator-1:Location Country (two letters):IE': 35, + 'CSETv1_Annotator-1:Physical System Type:Amazon Alexa Echo Dot speaker': 35, + 'CSETv1_Annotator-1:Sector of Deployment:defense': 35, + 'CSETv1_Annotator-2:AI Harm Level:AI tangible harm near-miss': 35, + 'CSETv1_Annotator-2:Deployed:maybe': 35, + 'CSETv1_Annotator-3:Data Inputs:face image': 35, + 'CSETv1_Annotator-3:Date of Incident Year:2021': 35, + 'GMF:Known AI Goal:AI Voice Assistant': 35, + 'GMF:Known AI Technology:Acoustic Fingerprint': 35, + 'GMF:Known AI Technology:Neural Network': 35, + 'GMF:Potential AI Technical Failure:Inadequate Anonymization': 35, + 'GMF:Potential AI Technology:Distributional Learning': 35, + 'CSETv0:AI Techniques:radar': 34, + 'CSETv0:Harm Type:Harm to intangible property': 34, + 'CSETv0:Sector of Deployment:Professional, scientific and technical activities': 34, + 'CSETv1:AI Task:Driving': 34, + 'CSETv1_Annotator-1:Data Inputs:photos': 34, + 'CSETv1_Annotator-1:Protected Characteristic:maybe': 34, + 'CSETv1_Annotator-1:Public Sector Deployment:maybe': 34, + 'CSETv1_Annotator-2:AI Task:recommender': 34, + 'CSETv1_Annotator-2:Sector of Deployment:Arts, entertainment and recreation': 34, + 'GMF:Potential AI Technical Failure:Overfitting': 34, + 'CSETv0:Location:Edinburgh, Scotland': 33, + 'CSETv0:Named Entities:Edinburgh': 33, + 'CSETv1:AI Task:resume screening': 33, + 'CSETv1:Data Inputs:Resume': 33, + 'CSETv1:Data Inputs:traffic': 33, + 'CSETv1:Harm Distribution Basis:ideology': 33, + 'CSETv1_Annotator-1:AI Task:obstacle avoidance': 33, + 'CSETv1_Annotator-1:AI Task:recruitment': 33, + 'CSETv1_Annotator-1:AI Task:resume screening': 33, + 'CSETv1_Annotator-1:Data Inputs:resumes': 33, + 'CSETv1_Annotator-2:AI tools and methods:Natural language processing': 33, + 'CSETv1_Annotator-2:Data Inputs:Resume': 33, + 'CSETv1_Annotator-2:Data Inputs:personal data': 33, + 'CSETv1_Annotator-2:Data Inputs:photos': 33, + 'CSETv1_Annotator-2:Data Inputs:traffic': 33, + 'CSETv1_Annotator-2:Date of Incident Year:2013': 33, + 'CSETv1_Annotator-2:Infrastructure Sectors:unclear': 33, + 'CSETv1_Annotator-2:Location Country (two letters):IE': 33, + 'CSETv1_Annotator-2:Rights Violation:maybe': 33, + 'CSETv1_Annotator-3:Tangible Harm:unclear': 33, + 'GMF:Known AI Technology:Generative Adversarial Network': 33, + 'GMF:Potential AI Technical Failure:Misinformation Generation Hazard': 33, + 'CSETv0:AI Applications:content filtering': 32, + 'CSETv0:AI Applications:environment sensing': 32, + 'CSETv0:AI Applications:self-driving': 32, + 'CSETv0:Named Entities:Facebook': 32, + 'CSETv0:System Developer:Facebook': 32, + 'CSETv0:Technology Purveyor:Facebook': 32, + 'CSETv1:AI tools and methods:human language technology': 32, + 'CSETv1:Location Country (two letters):DE': 32, + 'CSETv1_Annotator-1:AI Task:deepfake video generation': 32, + 'CSETv1:AI Task:content generation': 31, + 'CSETv1:AI Task:deepfake video generation': 31, + 'CSETv1:AI tools and methods:search engine optimization': 31, + 'CSETv1:Data Inputs:age': 31, + 'CSETv1:Data Inputs:personal information': 31, + 'CSETv1:Infrastructure Sectors:defense-industrial base': 31, + 'CSETv1:Sector of Deployment:defense': 31, + 'CSETv1_Annotator-1:AI Task:image search': 31, + 'CSETv1_Annotator-1:AI tools and methods:neural networks': 31, + 'CSETv1_Annotator-1:AI tools and methods:search engine optimization': 31, + 'CSETv1_Annotator-1:Sector of Deployment:other': 31, + 'CSETv1_Annotator-2:Location Country (two letters):DE': 31, + 'GMF:Known AI Goal:Autonomous Drones': 31, + 'CSETv0:AI Applications:forecasting': 30, + 'CSETv0:AI Applications:stock trading': 30, + 'CSETv0:AI Techniques:stock market algorithm': 30, + 'CSETv0:Financial Cost:Short term: $1 trillion unclear of long-term impact': 30, + 'CSETv0:Infrastructure Sectors:Financial services': 30, + 'CSETv0:Location:UK/USA': 30, + 'CSETv0:Location:Washington, D.C.': 30, + 'CSETv0:Named Entities:Chicago Merchant Exchange': 30, + 'CSETv0:Named Entities:Dow Jones Industrial Index': 30, + 'CSETv0:Named Entities:Electronic Privacy Information Center (EPIC)': 30, + 'CSETv0:Named Entities:Mountain View': 30, + 'CSETv0:Named Entities:Navinder Singh Saroa': 30, + 'CSETv0:Technology Purveyor:Navinder Singh Sarao': 30, + 'CSETv1:AI Task:autocomplete': 30, + 'CSETv1:AI Task:content moderation': 30, + 'CSETv1:AI Task:recommender': 30, + 'CSETv1:Data Inputs:radar input': 30, + 'CSETv1:Data Inputs:stock data': 30, + 'CSETv1:Data Inputs:stock orders': 30, + 'CSETv1:Data Inputs:stock prices': 30, + 'CSETv1:Data Inputs:thermal imaging': 30, + 'CSETv1:Data Inputs:words': 30, + 'CSETv1:Date of Incident Year:2010': 30, + 'CSETv1:Harm Distribution Basis:age': 30, + 'CSETv1_Annotator-1:AI Task:surveillance': 30, + 'CSETv1_Annotator-1:Data Inputs:Youtube videos': 30, + 'CSETv1_Annotator-1:Data Inputs:air quality data': 30, + 'CSETv1_Annotator-1:Data Inputs:personal information': 30, + 'CSETv1_Annotator-1:Data Inputs:stock orders': 30, + 'CSETv1_Annotator-1:Data Inputs:stock prices': 30, + 'CSETv1_Annotator-1:Data Inputs:thermal imaging': 30, + 'CSETv1_Annotator-1:Date of Incident Year:2010': 30, + 'CSETv1_Annotator-1:Infrastructure Sectors:financial services': 30, + 'CSETv1_Annotator-2:Data Inputs:stock data': 30, + 'CSETv1_Annotator-2:Date of Incident Year:2010': 30, + 'CSETv1_Annotator-3:AI Task:Patrolling': 30, + 'CSETv1_Annotator-3:Data Inputs:Facebook posts': 30, + 'CSETv1_Annotator-3:Data Inputs:Unclear': 30, + 'CSETv1_Annotator-3:Data Inputs:air quality': 30, + 'CSETv1_Annotator-3:Data Inputs:thermal imaging': 30, + 'CSETv1_Annotator-3:Physical System Type:autonomous security robot': 30, + 'GMF:Known AI Goal:Automatic Stock Trading': 30, + 'GMF:Potential AI Technical Failure:Gaming Vulnerability': 30, + 'CSETv0:AI Applications:Content curation': 29, + 'CSETv0:AI Techniques:GANs': 29, + 'CSETv0:AI Techniques:RNNs': 29, + 'CSETv0:Harm Type:Other:Reputational harm/social harm (libel and defamation)': 29, + 'CSETv0:Location:Delhi, India': 29, + 'CSETv0:Location:Unknown': 29, + 'CSETv0:Location:Williston, FL': 29, + 'CSETv0:Named Entities:Barack Obama': 29, + 'CSETv0:Named Entities:Delhi Metro Rail Corporation': 29, + 'CSETv0:Named Entities:Hyuandai Rotem': 29, + 'CSETv0:Named Entities:Jordan Peele': 29, + 'CSETv0:Named Entities:Tesla Autopilot': 29, + 'CSETv0:Named Entities:Tesla Model S': 29, + 'CSETv0:Named Entities:University of Washington': 29, + 'CSETv0:System Developer:FakeApp': 29, + 'CSETv0:System Developer:University of Washington': 29, + 'CSETv0:Technology Purveyor:Delhi Metro Rail Corporation': 29, + 'CSETv0:Technology Purveyor:Hyuandai Rotem': 29, + 'CSETv0:Technology Purveyor:Jordan Peele': 29, + 'CSETv0:Technology Purveyor:University of Washington': 29, + 'CSETv1:AI Task:content ranking': 29, + 'CSETv1:AI tools and methods:Sensor Data Processing': 29, + 'CSETv1:Data Inputs:Train position/status': 29, + 'CSETv1:Data Inputs:geospatial data': 29, + 'CSETv1:Data Inputs:track sensors': 29, + 'CSETv1:Infrastructure Sectors:communications': 29, + 'CSETv1:Operating Conditions:Testing': 29, + 'CSETv1:Physical System Type:Camera': 29, + 'CSETv1:Physical System Type:Metro train': 29, + 'CSETv1_Annotator-1:AI Task:autocomplete': 29, + 'CSETv1_Annotator-1:AI Task:object detection': 29, + 'CSETv1_Annotator-1:Annotator’s AI special interest intangible harm assessment:maybe': 29, + 'CSETv1_Annotator-1:Date of Incident Year:2011': 29, + 'CSETv1_Annotator-1:Location Region:--': 29, + 'CSETv1_Annotator-1:Physical System Type:Automobile': 29, + 'CSETv1_Annotator-1:Physical System Type:Metro train': 29, + 'CSETv1_Annotator-2:AI Task:Deepfake': 29, + 'CSETv1_Annotator-2:Data Inputs:Train position/status': 29, + 'CSETv1_Annotator-2:Data Inputs:track sensors': 29, + 'CSETv1_Annotator-2:Detrimental Content:maybe': 29, + 'CSETv1_Annotator-2:Operating Conditions:Testing': 29, + 'CSETv1_Annotator-2:Physical System Type:Metro train': 29, + 'CSETv1_Annotator-3:AI Task:autocomplete': 29, + 'CSETv1_Annotator-3:AI Task:content ranking': 29, + 'CSETv1_Annotator-3:Date of Incident Year:2011': 29, + 'GMF:Known AI Goal:Deepfake Video Generation': 29, + 'GMF:Known AI Goal:Hate Speech Detection': 29, + 'GMF:Known AI Technical Failure:Adversarial Data': 29, + 'GMF:Potential AI Technical Failure:Distributional Bias': 29, + 'GMF:Potential AI Technical Failure:Misuse': 29, + 'GMF:Potential AI Technology:3D reconstruction': 29, + 'GMF:Potential AI Technology:Other domain-specific approaches': 29, + 'CSETv0:AI Applications:Text generation': 28, + 'CSETv0:AI Applications:comprehension': 28, + 'CSETv0:AI Applications:computer vision': 28, + 'CSETv0:AI Applications:language output': 28, + 'CSETv0:AI Techniques:content creation': 28, + 'CSETv0:AI Techniques:driving lane detection': 28, + 'CSETv0:AI Techniques:image recognition': 28, + 'CSETv0:AI Techniques:language recognition natural language processing': 28, + 'CSETv0:AI Techniques:object detection': 28, + 'CSETv0:AI Techniques:scene segmentation': 28, + 'CSETv0:Location:Cambridge, MA': 28, + 'CSETv0:Location:Mountain View, CA': 28, + 'CSETv0:Location:Storey County, Nevada': 28, + 'CSETv0:Named Entities:Lexus': 28, + 'CSETv0:Named Entities:MIT Media Lab': 28, + 'CSETv0:Named Entities:Massachusetts Intstitute of Technology': 28, + 'CSETv0:Named Entities:Norman': 28, + 'CSETv0:Named Entities:Panasonic': 28, + 'CSETv0:Named Entities:Reddit': 28, + 'CSETv0:Named Entities:Santa Clara Valley Transportation Authority': 28, + 'CSETv0:Named Entities:Tay': 28, + 'CSETv0:Named Entities:Twitter': 28, + 'CSETv0:Named Entities:Xiaoice': 28, + 'CSETv0:System Developer:MIT Media Lab': 28, + 'CSETv0:Technology Purveyor:MIT Media Lab': 28, + 'CSETv0:Technology Purveyor:Twitter': 28, + 'CSETv1:AI Task:Generate Captions': 28, + 'CSETv1:AI Task:assembly': 28, + 'CSETv1:AI Task:image identification': 28, + 'CSETv1:AI Task:image interpretation': 28, + 'CSETv1:AI Task:production': 28, + 'CSETv1:AI tools and methods:prediction': 28, + 'CSETv1:Data Inputs:Captions': 28, + 'CSETv1:Data Inputs:image captions': 28, + 'CSETv1:Data Inputs:laser input': 28, + 'CSETv1:Data Inputs:video captions': 28, + 'CSETv1:Date of Incident Year:2009': 28, + 'CSETv1:Operating Conditions:Driverless car': 28, + 'CSETv1:Physical System Type:Vehicles (Lexus, Audi, Chrysler Pacifica)': 28, + 'CSETv1_Annotator-1:AI Task:assembly': 28, + 'CSETv1_Annotator-1:AI Task:image classification': 28, + 'CSETv1_Annotator-1:AI Task:image interpretation': 28, + 'CSETv1_Annotator-1:AI Task:production': 28, + 'CSETv1_Annotator-1:AI tools and methods:human language technology': 28, + 'CSETv1_Annotator-1:AI tools and methods:large language models': 28, + 'CSETv1_Annotator-1:Data Inputs:image captions': 28, + 'CSETv1_Annotator-1:Data Inputs:video captions': 28, + 'CSETv1_Annotator-1:Date of Incident Year:2024': 28, + 'CSETv1_Annotator-1:Physical System Type:Vehicles (Lexus, Audi, Chrysler Pacifica)': 28, + 'CSETv1_Annotator-2:AI Task:Generate Captions': 28, + 'CSETv1_Annotator-2:AI Task:Manufacturing Car': 28, + 'CSETv1_Annotator-2:Data Inputs:Captions': 28, + 'CSETv1_Annotator-2:Harm Distribution Basis:nation of origin, citizenship, immigrant status': 28, + 'CSETv1_Annotator-2:Harm Distribution Basis:religion': 28, + 'GMF:Known AI Technical Failure:Generalization Failure': 28, + 'GMF:Known AI Technical Failure:Limited Dataset': 28, + 'CSETv0:AI Applications:Early warning system': 27, + 'CSETv0:AI Applications:targeted advertising': 27, + 'CSETv0:AI Techniques:Google Adsense': 27, + 'CSETv0:AI Techniques:Oko satellites': 27, + 'CSETv0:Harm Type:Other:Reputational harm': 27, + 'CSETv0:Infrastructure Sectors:Critical manufacturing': 27, + 'CSETv0:Infrastructure Sectors:Nuclear': 27, + 'CSETv0:Location:Baunatal, Germany': 27, + 'CSETv0:Location:Soviet Union': 27, + 'CSETv0:Named Entities:Carnegie Mellon University': 27, + 'CSETv0:Named Entities:Harvard University': 27, + 'CSETv0:Named Entities:Harwin Cheng': 27, + 'CSETv0:Named Entities:Oko': 27, + 'CSETv0:Named Entities:Soviet Union': 27, + 'CSETv0:Named Entities:Tiffany Teng': 27, + 'CSETv0:Named Entities:United States': 27, + 'CSETv0:Named Entities:Volkswagen': 27, + 'CSETv0:Named Entities:William Santana Li': 27, + 'CSETv0:Named Entities:www.instantcheckmate.com': 27, + 'CSETv0:Physical System:Weapons system': 27, + 'CSETv0:System Developer:Soviet Union': 27, + 'CSETv0:Technology Purveyor:Instant Checkmate': 27, + 'CSETv0:Technology Purveyor:Soviet Union': 27, + 'CSETv0:Technology Purveyor:United States': 27, + 'CSETv0:Technology Purveyor:Volkswagen': 27, + 'CSETv1:AI Task:personalized online advertising': 27, + 'CSETv1:Data Inputs:satellite data': 27, + 'CSETv1:Data Inputs:sonar': 27, + 'CSETv1:Data Inputs:vibration detectors': 27, + 'CSETv1:Date of Incident Year:1983': 27, + 'CSETv1:Injuries:10': 27, + 'CSETv1_Annotator-1:AI Task:personalized online advertising': 27, + 'CSETv1_Annotator-1:Data Inputs:satellite data': 27, + 'CSETv1_Annotator-1:Date of Incident Year:1983': 27, + 'CSETv1_Annotator-1:Injuries:10': 27, + 'CSETv1_Annotator-1:Sector of Deployment:accommodation and food service activities': 27, + 'CSETv1_Annotator-2:AI Task:facial recognition': 27, + 'CSETv1_Annotator-2:AI tools and methods:Sensor Data Processing': 27, + 'CSETv1_Annotator-2:Data Inputs:sonar': 27, + 'CSETv1_Annotator-2:Data Inputs:vibration detectors': 27, + 'CSETv1_Annotator-2:Injuries:1': 27, + 'CSETv1_Annotator-2:Location Country (two letters):CN': 27, + "CSETv1_Annotator-2:Physical System Type:5' tall, 300 pound, egg shaped security robot": 27, + 'CSETv1_Annotator-3:Annotator’s AI special interest intangible harm assessment:maybe': 27, + 'CSETv1_Annotator-3:Clear link to Technology:maybe': 27, + 'CSETv1_Annotator-3:Date of Incident Year:1983': 27, + 'CSETv1_Annotator-3:Infrastructure Sectors:defense-industrial base': 27, + 'CSETv1_Annotator-3:Intentional Harm:Yes. Intentionally designed to perform harm but created an unintended harm (a different harm may have occurred)': 27, + 'CSETv1_Annotator-3:Location Country (two letters):RU': 27, + 'CSETv1_Annotator-3:Sector of Deployment:defense': 27, + 'CSETv1_Annotator-3:Tangible Harm:non-imminent risk of tangible harm (an issue) occurred': 27, + 'GMF:Known AI Goal:Threat Detection': 27, + 'GMF:Known AI Technical Failure:Black Swan Event': 27, + 'GMF:Known AI Technical Failure:Data or Labelling Noise': 27, + 'GMF:Known AI Technical Failure:Dataset Imbalance': 27, + 'GMF:Potential AI Technology:Face Detection': 27, + 'GMF:Potential AI Technology:Satellite Imaging': 27, + 'CSETv0:Location:Beitar Illit, Israel': 26, + 'CSETv0:Named Entities:Beitar Illit': 26, + 'CSETv0:Named Entities:Israel': 26, + 'CSETv0:Named Entities:Israeli Police': 26, + 'CSETv0:Sector of Deployment:Human health and social work activities': 26, + 'CSETv1:Data Inputs:Youtube videos': 26, + 'CSETv1:Data Inputs:traffic patterns': 26, + 'CSETv1:Location Country (two letters):PS': 26, + 'CSETv1:Location Country (two letters):VN': 26, + 'CSETv1_Annotator-1:AI Task:route optimization': 26, + 'CSETv1_Annotator-1:AI tools and methods:Dijkstra Algorithm': 26, + 'CSETv1_Annotator-1:AI tools and methods:shortest-path algorithm': 26, + 'CSETv1_Annotator-1:Data Inputs:GPS input': 26, + 'CSETv1_Annotator-1:Data Inputs:traffic patterns': 26, + 'CSETv1_Annotator-1:Data Inputs:user traffic reports': 26, + 'CSETv1_Annotator-1:Location Country (two letters):GB': 26, + 'CSETv1_Annotator-1:User Test in Controlled Conditions:yes': 26, + 'CSETv1_Annotator-2:AI Task:Translation': 26, + 'CSETv1_Annotator-2:Location Country (two letters):PS': 26, + 'CSETv1_Annotator-3:AI Task:Translation': 26, + 'CSETv1_Annotator-3:Location Country (two letters):PS': 26, + 'GMF:Known AI Goal:Translation': 26, + 'GMF:Potential AI Technical Failure:Misconfigured Aggregation': 26, + 'GMF:Potential AI Technology:Image Classification': 26, + 'GMF:Potential AI Technology:Intermediate modeling': 26, + 'GMF:Potential AI Technology:Multimodal Learning': 26, + 'CSETv0:Harm Type:Other': 25, + 'CSETv0:Location:Ningbo, China': 25, + 'CSETv0:Location:United States': 25, + 'CSETv0:Named Entities:Dong Mingzhu': 25, + 'CSETv0:Named Entities:Google Photos': 25, + 'CSETv0:Named Entities:Gree Electric Appliances': 25, + 'CSETv0:Named Entities:Ningbo': 25, + 'CSETv0:Physical System:Other:CCTV cameras, displays': 25, + 'CSETv0:System Developer:Ningbo traffic police': 25, + 'CSETv0:Technology Purveyor:Ningbo traffic police': 25, + 'CSETv1:AI Task:image classification': 25, + 'CSETv1:AI Task:jaywalking detection': 25, + 'CSETv1:Data Inputs:sex': 25, + 'CSETv1:Infrastructure Sectors:information technology': 25, + 'CSETv1:Lives Lost:2': 25, + 'CSETv1:Operating Conditions:night': 25, + 'CSETv1:Physical System Type:Tesla vehicle (Model 3, Model S, Model X)': 25, + 'CSETv1:User Test in Controlled Conditions:yes': 25, + 'CSETv1_Annotator-1:AI Task:jaywalking detection': 25, + 'CSETv1_Annotator-1:Data Inputs:camera footage': 25, + 'CSETv1_Annotator-1:Data Inputs:sex': 25, + 'CSETv1_Annotator-1:Date of Incident Year:2009': 25, + 'CSETv1_Annotator-1:Harm Distribution Basis:age': 25, + 'CSETv1_Annotator-1:Physical System Type:Tesla vehicle (Model 3, Model S, Model X)': 25, + 'CSETv1_Annotator-1:Physical System Type:autonomous car': 25, + 'CSETv1_Annotator-2:AI Task:Detect Jaywalkers': 25, + 'CSETv1_Annotator-2:Physical System Type:Camera': 25, + 'CSETv1_Annotator-3:AI Harm Level:AI tangible harm issue': 25, + 'GMF:Potential AI Technical Failure:Concept Drift': 25, + }, + 'CSETv1.AI System': { + yes: 1221, + no: 269, + maybe: 92, + }, + 'CSETv1.Lives Lost': { + '0': 1476, + '1': 76, + '2': 25, + '18': 2, + '51': 1, + '130': 3, + '144': 12, + '189': 19, + }, + 'GMF.Known AI Goal': { + 'Autonomous Driving': 45, + 'Face Recognition': 36, + 'AI Voice Assistant': 35, + 'Autonomous Drones': 31, + 'Automatic Stock Trading': 30, + 'Deepfake Video Generation': 29, + 'Hate Speech Detection': 29, + 'Threat Detection': 27, + Translation: 26, + 'Image Tagging': 24, + 'Content Recommendation': 14, + 'Content Search': 14, + 'Market Forecasting': 14, + 'NSFW Content Detection': 14, + Chatbot: 13, + 'Audio Localization': 11, + 'Gunshot Detection': 11, + 'Automatic Skill Assessment': 10, + Scheduling: 10, + 'Robotic Manipulation': 8, + 'Visual Art Generation': 7, + 'Smart Devices': 6, + 'Substance Detection': 6, + 'Code Generation': 5, + 'Image Cropping': 5, + 'Activity Tracking': 3, + 'Question Answering': 3, + 'Copyrighted Content Detection': 2, + 'Data Grouping': 2, + 'Social Media Content Generation': 2, + 'Automated Content Curation': 1, + 'Behavioral Modeling': 1, + 'Financial Processing': 1, + 'License Plate Recognition': 1, + 'Recidivism Prediction': 1, + }, + 'CSETv1.Data Inputs': { + text: 443, + images: 286, + video: 234, + 'sensor data': 147, + audio: 143, + lidar: 143, + radar: 131, + 'facial images': 100, + 'camera input': 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'1712448000': 2, + '1712534400': 3, + '1712707200': 2, + '1713139200': 2, + '1713830400': 2, + '1713916800': 2, + '1714003200': 8, + '1714089600': 7, + '1714176000': 3, + '1714348800': 2, + '1715644800': 3, + '1715731200': 4, + '1715817600': 2, + '1715904000': 4, + '1716163200': 12, + '1716249600': 6, + '1716336000': 4, + '1716422400': 7, + '1716508800': 3, + '1716595200': 2, + '1716681600': 2, + '1716768000': 2, + '1717113600': 2, + '1717200000': 2, + '1717977600': 3, + '1718064000': 3, + '1718150400': 5, + '1718236800': 4, + '1718668800': 2, + '1718841600': 3, + '1718928000': 2, + '1719187200': 4, + '1719273600': 2, + '1719532800': 2, + '1719619200': 3, + '1719792000': 2, + '1719878400': 3, + '1719964800': 2, + '1720569600': 2, + '1721692800': 2, + '1721952000': 2, + '1722124800': 2, + '1722384000': 2, + '1723507200': 2, + '1723593600': 6, + '1724025600': 3, + '1724112000': 2, + '1724198400': 2, + '1724716800': 2, + '1724976000': 2, + '1725235200': 5, + '1725321600': 5, + '1725408000': 4, + '1726012800': 2, + '1726185600': 2, + '1726704000': 2, + '1727136000': 2, + '1727222400': 3, + '1727308800': 10, + '1727740800': 3, + '1727827200': 6, + '1727913600': 2, + '1728000000': 2, + '1728086400': 3, + '1728172800': 2, + '1728259200': 4, + '1728432000': 2, + '1728604800': 10, + '1728691200': 6, + '1728777600': 3, + '1728864000': 18, + '1728950400': 7, + '1729036800': 3, + '1729123200': 5, + '1729209600': 7, + '1729468800': 4, + '1729555200': 8, + '1729641600': 12, + '1729728000': 15, + '1729814400': 5, + '1729900800': 3, + '1732060800': 3, + }, + 'CSETv1.Autonomy Level': { + Autonomy1: 847, + Autonomy3: 370, + Autonomy2: 261, + unclear: 104, + }, + 'CSETv1.Involving Minor': { + no: 1469, + yes: 106, + maybe: 7, + }, + 'CSETv1.Location Region': { + 'North America': 781, + Global: 402, + Asia: 177, + Europe: 152, + Oceania: 64, + Africa: 5, + 'South America': 1, + }, + 'CSETv1.Intentional Harm': { + 'No. Not intentionally designed to perform harm': 1462, + 'Yes. Intentionally designed to perform harm and did create intended harm': 70, + unclear: 44, + 'Yes. Intentionally designed to perform harm but created an unintended harm (a different harm may have occurred)': 2, + }, + 'CSETv1.Physical Objects': { + no: 946, + yes: 615, + maybe: 21, + }, + 'CSETv1.Rights Violation': { + no: 1332, + yes: 168, + maybe: 82, + }, + 'GMF.Known AI Technology': { + 'Face Detection': 69, + 'Recurrent Neural Network': 55, + 'Distributional Learning': 53, + 'Convolutional Neural Network': 50, + 'Language Modeling': 43, + 'Automatic Speech Recognition': 37, + 'Acoustic Fingerprint': 35, + 'Neural Network': 35, + 'Generative Adversarial Network': 33, + 'Keyword Filtering': 24, + 'Geolocation Data': 15, + 'Collaborative Filtering': 14, + 'Content-based Filtering': 14, + Transformer: 14, + Autoencoder: 13, + 'Acoustic Triangulation': 11, + 'Character NGrams': 9, + Classification: 6, + 'Gesture Recognition': 6, + 'Diverse Data': 4, + 'Multimodal Learning': 4, + Regression: 4, + 'Image Segmentation': 2, + 'Optical Character Recognition': 1, + 'Visual Object Detection': 1, + }, + 'CSETv1.Detrimental Content': { + no: 1281, + yes: 262, + maybe: 39, + }, + 'CSETv1.AI tools and methods': { + 'natural language processing': 235, + 'computer vision': 217, + 'Natural Language Processesing': 77, + 'facial recognition': 55, + 'voice recognition': 55, + 'neural networks': 54, + 'audio transcription': 51, + 'machine learning': 51, + 'image mapping': 45, + 'point mapping': 45, + unclear: 42, + 'object detection': 38, + 'human language technology': 32, + 'search engine optimization': 31, + 'Sensor Data Processing': 29, + prediction: 28, + 'Dijkstra Algorithm': 24, + 'face detection': 24, + 'logistic regression': 24, + 'shortest-path algorithm': 24, + 'non-linear SVM': 22, + 'signal detection theory': 22, + 'facial reconstruction': 21, + 'image reconstruction': 21, + 'large language models': 17, + 'image match': 16, + 'text to speech': 16, + 'deep learning': 15, + 'mesh autoencoders': 13, + classification: 12, + 'vector embedding': 11, + 'machine vision': 10, + 'large language model': 8, + 'regular expressions': 6, + robotics: 5, + 'speech recognition': 5, + estimation: 4, + 'neural network': 3, + transformer: 3, + 'Text to Speach': 2, + 'emotion detection': 2, + 'large-language models': 2, + 'logistic regression model': 2, + ranking: 2, + 'C4.5 algorithm': 1, + 'GPT-3': 1, + 'Markov decision process': 1, + 'SSD network algorithm': 1, + 'VCG network': 1, + 'YOLO network algorithm': 1, + classiification: 1, + 'convolutional neural networks': 1, + 'decision tree': 1, + 'linear algebra': 1, + 'natural language': 1, + 'natural language response': 1, + 'pixel embeddings': 1, + recommendation: 1, + 'reinforcement learning': 1, + 'response model': 1, + 'text scraping': 1, + 'unsupervised learning': 1, + 'website code': 1, + }, + 'CSETv1.Operating Conditions': { + 'operationally representative': 63, + Testing: 29, + 'Driverless car': 28, + night: 25, + 'natural disaster - wildfires': 22, + 'Twin faces': 21, + 'unclear enunciation': 16, + 'inclement weather and snow': 6, + 'COVID-19 pandemic': 4, + 'inclement weather - snow': 2, + 'seasonal road closures': 2, + 'Non-operational': 1, + }, + 'CSETv1.Physical System Type': { + 'Manufacturing Robot': 67, + none: 59, + 'rocket/egg shaped, 300 pound, 5 ft. tall security robot': 57, + 'Apple iPhone X': 45, + 'Amazon Alexa Echo Dot speaker': 35, + 'Volvo XC90 SUV': 35, + Camera: 29, + 'Metro train': 29, + 'Vehicles (Lexus, Audi, Chrysler Pacifica)': 28, + 'Tesla vehicle (Model 3, Model S, Model X)': 25, + 'Tesla Model S': 24, + 'oval, eight-seater, autonomous, electric shuttle bus': 24, + 'Boeing 737 Max airplane': 19, + 'Amazon Echo Dot Smart Speaker': 16, + 'medical robot': 12, + 'Vehicles (Audi Q5 Crossover, Lexus RX400h Crossover)': 11, + microphones: 11, + 'Electric vehicle': 10, + 'Tesla Model 3': 6, + 'smoke detector': 6, + 'A cone-shaped, 400-pound security robot on wheels': 5, + 'Nissan vehicles (Rogue, Rogue Hybrid, Rogue Sport, Sentra)': 5, + 'Amazon Echo': 4, + 'Tesla sedan': 4, + 'Vehicles (Volvo, Lexus, Ford, Google, etc.)': 4, + drone: 4, + 'fulfillment robot': 4, + '2016 Tesla Model S': 3, + 'Handheld barcode scanner; flat, round mobile robots;': 3, + '4ft tall vaguely humanoid droid on wheels': 2, + 'Amazon Echo Dot': 2, + 'Humanoid robot': 2, + 'body scanner': 2, + car: 2, + 'store security camera': 2, + 'Palm VII': 1, + 'Starship delivery robot': 1, + 'in-van cameras and sensors': 1, + 'robotic dog': 1, + 'vaguely humanoid robot': 1, + }, + 'CSETv1.Sector of Deployment': { + 'information and communication': 562, + 'transportation and storage': 273, + 'Arts, entertainment and recreation': 190, + 'wholesale and retail trade': 154, + 'administrative and support service activities': 125, + 'public administration': 117, + 'law enforcement': 114, + 'human health and social work activities': 92, + 'professional, scientific and technical activities': 71, + 'financial and insurance activities': 69, + manufacturing: 67, + Education: 41, + defense: 31, + 'accommodation and food service activities': 18, + other: 10, + 'real estate activities': 5, + 'other service activities': 2, + unclear: 2, + }, + 'GMF.Potential AI Technology': { + Regression: 54, + 'Image Segmentation': 45, + Classification: 44, + 'Convolutional Neural Network': 44, + 'Ensemble Aggregation': 39, + 'Distributional Learning': 35, + '3D reconstruction': 29, + 'Other domain-specific approaches': 29, + 'Face Detection': 27, + 'Satellite Imaging': 27, + 'Image Classification': 26, + 'Intermediate modeling': 26, + 'Multimodal Learning': 26, + 'Diverse Data': 13, + 'Visual Object Detection': 11, + Clustering: 6, + 'Geolocation Data': 4, + 'Optical Character Recognition': 4, + Transformer: 3, + 'Acoustic Fingerprint': 2, + Spectrogram: 2, + 'Siamese Network': 1, + }, + 'CSETv1.Date of Incident Year': { + '1979': 4, + '1983': 27, + '2000': 13, + '2001': 6, + '2008': 12, + '2009': 28, + '2010': 30, + '2011': 43, + '2012': 41, + '2013': 53, + '2014': 95, + '2015': 78, + '2016': 268, + '2017': 382, + '2018': 181, + '2019': 43, + '2020': 83, + '2021': 82, + '2022': 36, + '2023': 77, + }, + 'CSETv1.Entertainment Industry': { + no: 1401, + yes: 140, + maybe: 41, + }, + 'CSETv1.Infrastructure Sectors': { + transportation: 139, + 'healthcare and public health': 75, + 'financial services': 36, + 'defense-industrial base': 31, + communications: 29, + 'information technology': 25, + 'emergency services': 23, + 'food and agriculture': 8, + Other: 3, + 'commercial facilities': 2, + 'government facilities': 2, + unclear: 2, + }, + 'CSETv1.Harm Distribution Basis': { + none: 1059, + race: 346, + sex: 198, + 'nation of origin, citizenship, immigrant status': 100, + religion: 88, + 'sexual orientation or gender identity': 73, + 'financial means': 44, + disability: 41, + geography: 35, + ideology: 33, + age: 30, + 'familial status (e.g., having or not having children) or pregnancy': 4, + unclear: 4, + other: 3, + }, + 'CSETv1.Multiple AI Interaction': { + no: 1525, + yes: 48, + maybe: 9, + }, + 'GMF.Known AI Technical Failure': { + 'Unsafe Exposure or Access': 59, + 'Distributional Bias': 55, + Misuse: 37, + 'Adversarial Data': 29, + 'Generalization Failure': 28, + 'Limited Dataset': 28, + 'Black Swan Event': 27, + 'Data or Labelling Noise': 27, + 'Dataset Imbalance': 27, + Underfitting: 24, + 'Lack of Adversarial Robustness': 23, + 'Tuning Issues': 17, + 'Inappropriate Training Content': 16, + 'Inadequate Anonymization': 13, + 'Unauthorized Data': 13, + Underspecification: 10, + 'Context Misidentification': 9, + 'Gaming Vulnerability': 5, + 'Lack of Capability Control': 5, + 'Concept Drift': 4, + 'Misinformation Generation Hazard': 3, + 'Incomplete Data Attribute Capture': 2, + 'Lack of Explainability': 2, + 'Lack of Transparency': 2, + 'Misconfigured Threshold': 1, + }, + 'CSETv1.Clear link to Technology': { + yes: 1425, + no: 105, + maybe: 52, + }, + 'CSETv1.Protected Characteristic': { + no: 1070, + yes: 505, + maybe: 7, + }, + 'CSETv1.Public Sector Deployment': { + no: 1324, + yes: 249, + maybe: 9, + }, + 'CSETv1.Impact on Critical Services': { + no: 1474, + yes: 106, + maybe: 2, + }, + 'GMF.Potential AI Technical Failure': { + 'Generalization Failure': 135, + 'Context Misidentification': 94, + 'Dataset Imbalance': 56, + Underspecification: 52, + 'Unauthorized Data': 45, + Underfitting: 41, + 'Hardware Failure': 39, + 'Lack of Capability Control': 39, + 'Data or Labelling Noise': 38, + 'Inadequate Anonymization': 35, + Overfitting: 34, + 'Misinformation Generation Hazard': 33, + 'Gaming Vulnerability': 30, + 'Distributional Bias': 29, + Misuse: 29, + 'Misconfigured Aggregation': 26, + 'Concept Drift': 25, + 'Misaligned Objective': 23, + 'Misconfigured Threshold': 18, + 'Incomplete Data Attribute Capture': 17, + 'Limited Dataset': 17, + 'Covariate Shift': 16, + 'Inadequate Data Sampling': 13, + 'Tuning Issues': 10, + 'Limited User Access': 6, + 'Data Memorization': 5, + 'Lack of Explainability': 5, + 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"short_name": "AI Task", - "value_json": "[\"scheduling\",\"productivity optimization\",\"predict store traffic\"]" - }, - { - "__typename": "ClassificationAttribute", - "short_name": "Notes (AI Functionality and Techniques)", - "value_json": "\"\"" - } - ], - "incidents": [ - { - "__typename": "Incident", - "incident_id": 50 - } - ], - "namespace": "CSETv1", - "notes": "", - "publish": true, - "reports": [] - } - } -} diff --git a/site/gatsby-site/playwright/fixtures/history/incidentHistory.json b/site/gatsby-site/playwright/fixtures/history/incidentHistory.json deleted file mode 100644 index b8c13be934..0000000000 --- a/site/gatsby-site/playwright/fixtures/history/incidentHistory.json +++ /dev/null @@ -1,99 +0,0 @@ -{ - "data": { - "history_incidents": [ - { - "AllegedDeployerOfAISystem": ["youtube", "google"], - "AllegedDeveloperOfAISystem": ["google"], - "AllegedHarmedOrNearlyHarmedParties": ["google"], - "_id": "64cc42271a57ad8fd9b338d0", - "date": "2023-08-02", - "description": "this is the description v2", - 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"_id": "64cc1d0c1c1f73dbc022f0b6", - "date": "2023-08-03", - "description": "this is the description", - "editor_dissimilar_incidents": [], - "editor_notes": null, - "editor_similar_incidents": [], - "editors": ["2"], - "embedding": null, - "epoch_date_modified": 1691098359, - "flagged_dissimilar_incidents": null, - "incident_id": 10, - "modifiedBy": "1", - "nlp_similar_incidents": [], - "reports": [3205], - "title": "Google Bungles AI Reveal With Incorrect Webb Telescope Facts", - "tsne": null - } - ] - } -} diff --git a/site/gatsby-site/playwright/fixtures/history/reportHistory.json b/site/gatsby-site/playwright/fixtures/history/reportHistory.json deleted file mode 100644 index 2e05ddc6e2..0000000000 --- a/site/gatsby-site/playwright/fixtures/history/reportHistory.json +++ /dev/null @@ -1,291 +0,0 @@ -{ - "data": { - "history_reports": [ - { - "_id": "64d18f7a1a4fcdeaf137442b", - "authors": ["Pablo Costa v2", "Pablo Costa v3", "Pablo Costa v4"], - "cloudinary_id": "reports/image.cnbcfm.com/api/v1/image/100266733-38cbdbd7a4bcc3cf98120f6574fe94a3afe0fca6.jpg?v=1529461359&w=1920&h=1080", - "date_downloaded": "2023-08-04", - "date_modified": "2023-08-07", - "date_published": "2023-08-04", - "date_submitted": "2023-08-03", - "description": null, - "editor_notes": "editor notes v2", - "embedding": null, - "epoch_date_downloaded": 1691107200, - "epoch_date_modified": 1691455351, - "epoch_date_published": 1691107200, - "epoch_date_submitted": 1691020800, - "flag": null, - "image_url": "https://image.cnbcfm.com/api/v1/image/100266733-38cbdbd7a4bcc3cf98120f6574fe94a3afe0fca6.jpg?v=1529461359&w=1920&h=1080", - "inputs_outputs": ["input 1"], - "is_incident_report": true, - "language": "es", - "modifiedBy": "1", - "plain_text": "\n\nBy Aimee Picchi\n\nSeptember 24, 2015 / 11:41 AM / MoneyWatch\n\nFor Starbucks (SBUX) barista Kylei Weisse, working at the coffee chain helps him secure health insurance and some extra money while he studies at Georgia Perimeter College. What it doesn't provide is the kind of stable schedule that the company promised its workers last year.\n\n\"It's the wild inconsistency\" of the hours that's a problem, Weisse, 32, said. \"We're supposed to get them 10 days in advance, which often happens, but there's no guarantee. If our manager doesn't get it to us on time, we just have to deal with it.\"\n\nThat became a problem recently when Weisse's manager gave him only a few days notice on his work hours, which ended up conflicting with an anatomy and physiology exam at his college. Weisse ended up paying another worker $20 to take his shift so he could take the exam.\n\nThe short notice is especially frustrating because of Starbucks' vow last year to post employees' schedules at least 10 days in advance, as well as the company's insistence that workers provide at least one-month notice when they need to take a day off.\n\nWhat's behind Starbucks price increases? 03:04\n\nWeisse isn't alone in complaining that Starbucks isn't living up to its promises to overhaul its labor practices for its roughly 130,000 baristas. That vow followed an article last year by The New York Times that detailed how workers were struggling to manage childcare and other obligations when the company provided only a few days notice about their schedules.\n\nAbout half of roughly 200 Starbucks baristas in a recent survey said they are still receiving their schedule with less than one week's notice. Others also reported being asked to handle \"clopens,\" split shifts in which employees work a closing shift late into the evening and then an early opening shift the following morning. The company last year promised to end the practice.\n\nOf course, Starbucks isn't alone in using \"just-in-time\" scheduling, with the retail and restaurant industry increasingly turning to software that allows them to change work schedules at the last minute, depending on whether business picks up or slows down. But it is Starbucks that has become a lightning rod on the issue given its vows to improve how it treats employees and its own emphatic claims to valuing workers, whom it labels \"partners.\"\n\n\"Starbucks has the values and wants to do right by their employees,\" said Carrie Gleason, director of the Fair Workweek Initiative at the Center for Popular Democracy, an advocacy group focused on workers'rights, and a co-author of the group's new report on the company's labor practices. \"However, since last year when the company recognized there was a serious problem with the way it scheduled workers and pledged to reform, still so many of the same issues persist.\"\n\nStarbucks didn't respond to requests for comment on the study or on baristas' reports of labor practices that are failing to meet the company's stated goals.\n\nIn an internal memo this week published by Time, Starbucks executive Cliff Burrows wrote that the company couldn't validate the survey, but added that \"the findings suggest, contrary to the expectations we have in place, that some partners are receiving their schedules less than one week in advance and that there is a continuing issue with some partners working a close and then an opening shift the following morning.\" He asks store managers \"to go the extra mile to ensure partners have a consistent schedule.\"\n\nStarbucks ends \"race together\" campaign amid public backlash 00:26\n\nTo be sure, some Starbucks workers are receiving at least 10 days notice on their work hours, with the survey finding that about one-third receive two weeks notice and another 18 percent get their schedules three weeks in advance. But that leaves almost half of workers who only receive one week's notice, making it more difficult from them manage other obligations, such as school, family commitments or other jobs.\n\nClopens remain a problem, as well. About 60 percent of workers who have to handle a clopen receive seven or fewer hours of rest between a closing and an opening shift, the study found.\n\nThat's prompted one former Starbucks employee to start a petition to end the practice of scheduling clopens. Ciara Moran noted in her petition that she sometimes was only able to get four or five hours of sleep on the days she was scheduled for clopens. She said she quit her job because she doubted whether it was possible to get ahead given the demands on workers.\n\nEven if Starbucks stuck with its policy of providing eight hours between shifts, that's not enough time, especially given that many workers in the service sector have long commutes, the study said.\n\nAnother issue singled out by the report is Starbucks' practices on sick time. Since paid time off is only available to workers with at least a year on the job, about 40 percent of employees in the survey said they had dealt with barriers in taking sick days.\n\nIn a perfect world, Weisse said he'd like to receive his schedule either a month or a quarter in advance. Asked if he's upset about the last-minute schedules, Weisse said he had become resigned to it.\n\n\"I don't know if you've ever seen experiments with rats,\" he said. \"First they get upset if you treat them badly. Then they lay down and try not to move too much. I'm like that now. Things are terrible.\"\n\nV2\n", - "report_number": 3206, - "submitters": ["Test User v2"], - "tags": [], - "text": "[](/moneywatch)\n\nBy Aimee Picchi\n\nSeptember 24, 2015 / 11:41 AM / MoneyWatch\n\nFor Starbucks ([SBUX](http://markets.cbsnews.com/SBUX/quote/)) barista Kylei Weisse, working at the coffee chain helps him secure health insurance and some extra money while he studies at Georgia Perimeter College. What it doesn't provide is the kind of stable schedule that the [company promised its workers](https://www.cbsnews.com/news/for-some-starbucks-workers-job-leaves-bitter-taste/) last year.\n\n\"It's the wild inconsistency\" of the hours that's a problem, Weisse, 32, said. \"We're supposed to get them 10 days in advance, which often happens, but there's no guarantee. If our manager doesn't get it to us on time, we just have to deal with it.\"\n\nThat became a problem recently when Weisse's manager gave him only a few days notice on his work hours, which ended up conflicting with an anatomy and physiology exam at his college. Weisse ended up paying another worker $20 to take his shift so he could take the exam.\n\nThe short notice is especially frustrating because of Starbucks' vow last year to post employees' schedules at least 10 days in advance, as well as the company's insistence that workers provide at least one-month notice when they need to take a day off.\n\n[What's behind Starbucks price increases? 03:04](https://www.cbsnews.com/video/whats-behind-starbucks-price-increases/)\n\nWeisse isn't alone in complaining that Starbucks isn't living up to its promises to overhaul its labor practices for its roughly 130,000 baristas. That vow followed an [article last year by The New York Times](http://www.nytimes.com/interactive/2014/08/13/us/starbucks-workers-scheduling-hours.html?_r=1) that detailed how workers were struggling to manage childcare and other obligations when the company provided only a few days notice about their schedules.\n\nAbout half of roughly 200 Starbucks baristas [in a recent survey](http://static1.squarespace.com/static/556496efe4b02c9d26fdf26a/t/56027485e4b02ead4f16bc2a/1443001477305/The+Grind.pdf) said they are still receiving their schedule with less than one week's notice. Others also reported being asked to handle \"clopens,\" split shifts in which employees work a closing shift late into the evening and then an early opening shift the following morning. The company last year promised to end the practice.\n\nOf course, Starbucks isn't alone in using [\"just-in-time\" scheduling](https://www.cbsnews.com/news/in-retail-life-on-the-job-often-leads-to-a-dead-end/), with the retail and restaurant industry increasingly turning to software that allows them to change work schedules at the last minute, depending on whether business picks up or slows down. But it is Starbucks that has become a lightning rod on the issue given its vows to improve how it treats employees and its own [emphatic claims](http://www.starbucks.com/careers/working-at-starbucks) to valuing workers, whom it labels \"partners.\"\n\n\"Starbucks has the values and wants to do right by their employees,\" said Carrie Gleason, director of the Fair Workweek Initiative at the Center for Popular Democracy, an advocacy group focused on workers'rights, and a co-author of the group's new [report on the company's labor practices](http://populardemocracy.org/sites/default/files/The%2BGrind.pdf). \"However, since last year when the company recognized there was a serious problem with the way it scheduled workers and pledged to reform, still so many of the same issues persist.\"\n\nStarbucks didn't respond to requests for comment on the study or on baristas' reports of labor practices that are failing to meet the company's stated goals.\n\nIn an internal memo this week [published by Time](http://time.com/4047359/starbucks-scheduling-labor-practices-memo/), Starbucks executive Cliff Burrows wrote that the company couldn't validate the survey, but added that \"the findings suggest, contrary to the expectations we have in place, that some partners are receiving their schedules less than one week in advance and that there is a continuing issue with some partners working a close and then an opening shift the following morning.\" He asks store managers \"to go the extra mile to ensure partners have a consistent schedule.\"\n\n[Starbucks ends \"race together\" campaign amid public backlash 00:26](https://www.cbsnews.com/video/starbucks-ends-race-together-campaign-amid-public-backlash/)\n\nTo be sure, some Starbucks workers are receiving at least 10 days notice on their work hours, with the survey finding that about one-third receive two weeks notice and another 18 percent get their schedules three weeks in advance. But that leaves almost half of workers who only receive one week's notice, making it more difficult from them manage other obligations, such as school, family commitments or other jobs.\n\nClopens remain a problem, as well. About 60 percent of workers who have to handle a clopen receive seven or fewer hours of rest between a closing and an opening shift, the study found.\n\nThat's prompted one former Starbucks employee to start a petition to end the practice of scheduling clopens. Ciara Moran noted in her petition that she sometimes was only able to get [four or five hours of sleep](https://www.coworker.org/petitions/starbucks-end-clopens-now) on the days she was scheduled for clopens. She said she quit her job because she doubted whether it was possible to get ahead given the demands on workers.\n\nEven if Starbucks stuck with its policy of providing eight hours between shifts, that's not enough time, especially given that many workers in the service sector have long commutes, the study said.\n\nAnother issue singled out by the report is Starbucks' practices on sick time. Since paid time off is only available to workers with at least a year on the job, about 40 percent of employees in the survey said they had dealt with barriers in taking sick days.\n\nIn a perfect world, Weisse said he'd like to receive his schedule either a month or a quarter in advance. Asked if he's upset about the last-minute schedules, Weisse said he had become resigned to it.\n\n\"I don't know if you've ever seen experiments with rats,\" he said. \"First they get upset if you treat them badly. Then they lay down and try not to move too much. I'm like that now. Things are terrible.\"\n\nV2", - "title": "​Is Starbucks shortchanging its baristas? v2", - "url": "https://www.cbsnews.com/news/is-starbucks-shortchanging-its-baristas", - "source_domain": "cbsnews.com", - "user": "1" - }, - { - "_id": "64d18f66f549de65c4e7841e", - "authors": ["Pablo Costa v2", "Pablo Costa v3", "Pablo Costa v4"], - "cloudinary_id": "reports/image.cnbcfm.com/api/v1/image/100266733-38cbdbd7a4bcc3cf98120f6574fe94a3afe0fca6.jpg?v=1529461359&w=1920&h=1080", - "date_downloaded": "2023-08-04", - "date_modified": "2023-08-07", - "date_published": "2023-08-04", - "date_submitted": "2023-08-03", - "description": null, - "editor_notes": "editor notes v2", - "embedding": null, - "epoch_date_downloaded": 1691107200, - "epoch_date_modified": 1691455331, - "epoch_date_published": 1691107200, - "epoch_date_submitted": 1691020800, - "flag": null, - "image_url": "https://image.cnbcfm.com/api/v1/image/100266733-38cbdbd7a4bcc3cf98120f6574fe94a3afe0fca6.jpg?v=1529461359&w=1920&h=1080", - "inputs_outputs": ["input 1"], - "is_incident_report": true, - "language": "es", - "modifiedBy": "1", - "plain_text": "\n\nBy Aimee Picchi\n\nSeptember 24, 2015 / 11:41 AM / MoneyWatch\n\nFor Starbucks (SBUX) barista Kylei Weisse, working at the coffee chain helps him secure health insurance and some extra money while he studies at Georgia Perimeter College. What it doesn't provide is the kind of stable schedule that the company promised its workers last year.\n\n\"It's the wild inconsistency\" of the hours that's a problem, Weisse, 32, said. \"We're supposed to get them 10 days in advance, which often happens, but there's no guarantee. If our manager doesn't get it to us on time, we just have to deal with it.\"\n\nThat became a problem recently when Weisse's manager gave him only a few days notice on his work hours, which ended up conflicting with an anatomy and physiology exam at his college. Weisse ended up paying another worker $20 to take his shift so he could take the exam.\n\nThe short notice is especially frustrating because of Starbucks' vow last year to post employees' schedules at least 10 days in advance, as well as the company's insistence that workers provide at least one-month notice when they need to take a day off.\n\nWhat's behind Starbucks price increases? 03:04\n\nWeisse isn't alone in complaining that Starbucks isn't living up to its promises to overhaul its labor practices for its roughly 130,000 baristas. That vow followed an article last year by The New York Times that detailed how workers were struggling to manage childcare and other obligations when the company provided only a few days notice about their schedules.\n\nAbout half of roughly 200 Starbucks baristas in a recent survey said they are still receiving their schedule with less than one week's notice. Others also reported being asked to handle \"clopens,\" split shifts in which employees work a closing shift late into the evening and then an early opening shift the following morning. The company last year promised to end the practice.\n\nOf course, Starbucks isn't alone in using \"just-in-time\" scheduling, with the retail and restaurant industry increasingly turning to software that allows them to change work schedules at the last minute, depending on whether business picks up or slows down. But it is Starbucks that has become a lightning rod on the issue given its vows to improve how it treats employees and its own emphatic claims to valuing workers, whom it labels \"partners.\"\n\n\"Starbucks has the values and wants to do right by their employees,\" said Carrie Gleason, director of the Fair Workweek Initiative at the Center for Popular Democracy, an advocacy group focused on workers'rights, and a co-author of the group's new report on the company's labor practices. \"However, since last year when the company recognized there was a serious problem with the way it scheduled workers and pledged to reform, still so many of the same issues persist.\"\n\nStarbucks didn't respond to requests for comment on the study or on baristas' reports of labor practices that are failing to meet the company's stated goals.\n\nIn an internal memo this week published by Time, Starbucks executive Cliff Burrows wrote that the company couldn't validate the survey, but added that \"the findings suggest, contrary to the expectations we have in place, that some partners are receiving their schedules less than one week in advance and that there is a continuing issue with some partners working a close and then an opening shift the following morning.\" He asks store managers \"to go the extra mile to ensure partners have a consistent schedule.\"\n\nStarbucks ends \"race together\" campaign amid public backlash 00:26\n\nTo be sure, some Starbucks workers are receiving at least 10 days notice on their work hours, with the survey finding that about one-third receive two weeks notice and another 18 percent get their schedules three weeks in advance. But that leaves almost half of workers who only receive one week's notice, making it more difficult from them manage other obligations, such as school, family commitments or other jobs.\n\nClopens remain a problem, as well. About 60 percent of workers who have to handle a clopen receive seven or fewer hours of rest between a closing and an opening shift, the study found.\n\nThat's prompted one former Starbucks employee to start a petition to end the practice of scheduling clopens. Ciara Moran noted in her petition that she sometimes was only able to get four or five hours of sleep on the days she was scheduled for clopens. She said she quit her job because she doubted whether it was possible to get ahead given the demands on workers.\n\nEven if Starbucks stuck with its policy of providing eight hours between shifts, that's not enough time, especially given that many workers in the service sector have long commutes, the study said.\n\nAnother issue singled out by the report is Starbucks' practices on sick time. Since paid time off is only available to workers with at least a year on the job, about 40 percent of employees in the survey said they had dealt with barriers in taking sick days.\n\nIn a perfect world, Weisse said he'd like to receive his schedule either a month or a quarter in advance. Asked if he's upset about the last-minute schedules, Weisse said he had become resigned to it.\n\n\"I don't know if you've ever seen experiments with rats,\" he said. \"First they get upset if you treat them badly. Then they lay down and try not to move too much. I'm like that now. Things are terrible.\"\n\nV2\n", - "report_number": 3206, - "submitters": ["Test User v2"], - "tags": ["response"], - "text": "[](/moneywatch)\n\nBy Aimee Picchi\n\nSeptember 24, 2015 / 11:41 AM / MoneyWatch\n\nFor Starbucks ([SBUX](http://markets.cbsnews.com/SBUX/quote/)) barista Kylei Weisse, working at the coffee chain helps him secure health insurance and some extra money while he studies at Georgia Perimeter College. What it doesn't provide is the kind of stable schedule that the [company promised its workers](https://www.cbsnews.com/news/for-some-starbucks-workers-job-leaves-bitter-taste/) last year.\n\n\"It's the wild inconsistency\" of the hours that's a problem, Weisse, 32, said. \"We're supposed to get them 10 days in advance, which often happens, but there's no guarantee. If our manager doesn't get it to us on time, we just have to deal with it.\"\n\nThat became a problem recently when Weisse's manager gave him only a few days notice on his work hours, which ended up conflicting with an anatomy and physiology exam at his college. Weisse ended up paying another worker $20 to take his shift so he could take the exam.\n\nThe short notice is especially frustrating because of Starbucks' vow last year to post employees' schedules at least 10 days in advance, as well as the company's insistence that workers provide at least one-month notice when they need to take a day off.\n\n[What's behind Starbucks price increases? 03:04](https://www.cbsnews.com/video/whats-behind-starbucks-price-increases/)\n\nWeisse isn't alone in complaining that Starbucks isn't living up to its promises to overhaul its labor practices for its roughly 130,000 baristas. That vow followed an [article last year by The New York Times](http://www.nytimes.com/interactive/2014/08/13/us/starbucks-workers-scheduling-hours.html?_r=1) that detailed how workers were struggling to manage childcare and other obligations when the company provided only a few days notice about their schedules.\n\nAbout half of roughly 200 Starbucks baristas [in a recent survey](http://static1.squarespace.com/static/556496efe4b02c9d26fdf26a/t/56027485e4b02ead4f16bc2a/1443001477305/The+Grind.pdf) said they are still receiving their schedule with less than one week's notice. Others also reported being asked to handle \"clopens,\" split shifts in which employees work a closing shift late into the evening and then an early opening shift the following morning. The company last year promised to end the practice.\n\nOf course, Starbucks isn't alone in using [\"just-in-time\" scheduling](https://www.cbsnews.com/news/in-retail-life-on-the-job-often-leads-to-a-dead-end/), with the retail and restaurant industry increasingly turning to software that allows them to change work schedules at the last minute, depending on whether business picks up or slows down. But it is Starbucks that has become a lightning rod on the issue given its vows to improve how it treats employees and its own [emphatic claims](http://www.starbucks.com/careers/working-at-starbucks) to valuing workers, whom it labels \"partners.\"\n\n\"Starbucks has the values and wants to do right by their employees,\" said Carrie Gleason, director of the Fair Workweek Initiative at the Center for Popular Democracy, an advocacy group focused on workers'rights, and a co-author of the group's new [report on the company's labor practices](http://populardemocracy.org/sites/default/files/The%2BGrind.pdf). \"However, since last year when the company recognized there was a serious problem with the way it scheduled workers and pledged to reform, still so many of the same issues persist.\"\n\nStarbucks didn't respond to requests for comment on the study or on baristas' reports of labor practices that are failing to meet the company's stated goals.\n\nIn an internal memo this week [published by Time](http://time.com/4047359/starbucks-scheduling-labor-practices-memo/), Starbucks executive Cliff Burrows wrote that the company couldn't validate the survey, but added that \"the findings suggest, contrary to the expectations we have in place, that some partners are receiving their schedules less than one week in advance and that there is a continuing issue with some partners working a close and then an opening shift the following morning.\" He asks store managers \"to go the extra mile to ensure partners have a consistent schedule.\"\n\n[Starbucks ends \"race together\" campaign amid public backlash 00:26](https://www.cbsnews.com/video/starbucks-ends-race-together-campaign-amid-public-backlash/)\n\nTo be sure, some Starbucks workers are receiving at least 10 days notice on their work hours, with the survey finding that about one-third receive two weeks notice and another 18 percent get their schedules three weeks in advance. But that leaves almost half of workers who only receive one week's notice, making it more difficult from them manage other obligations, such as school, family commitments or other jobs.\n\nClopens remain a problem, as well. About 60 percent of workers who have to handle a clopen receive seven or fewer hours of rest between a closing and an opening shift, the study found.\n\nThat's prompted one former Starbucks employee to start a petition to end the practice of scheduling clopens. Ciara Moran noted in her petition that she sometimes was only able to get [four or five hours of sleep](https://www.coworker.org/petitions/starbucks-end-clopens-now) on the days she was scheduled for clopens. She said she quit her job because she doubted whether it was possible to get ahead given the demands on workers.\n\nEven if Starbucks stuck with its policy of providing eight hours between shifts, that's not enough time, especially given that many workers in the service sector have long commutes, the study said.\n\nAnother issue singled out by the report is Starbucks' practices on sick time. Since paid time off is only available to workers with at least a year on the job, about 40 percent of employees in the survey said they had dealt with barriers in taking sick days.\n\nIn a perfect world, Weisse said he'd like to receive his schedule either a month or a quarter in advance. Asked if he's upset about the last-minute schedules, Weisse said he had become resigned to it.\n\n\"I don't know if you've ever seen experiments with rats,\" he said. \"First they get upset if you treat them badly. Then they lay down and try not to move too much. I'm like that now. Things are terrible.\"\n\nV2", - "title": "​Is Starbucks shortchanging its baristas? v2", - "url": "https://www.cbsnews.com/news/is-starbucks-shortchanging-its-baristas", - "source_domain": "cbsnews.com", - "user": "1" - }, - { - "_id": "64d18f471bae3610af2ab1bc", - "authors": ["Pablo Costa v2", "Pablo Costa v3", "Pablo Costa v4"], - "cloudinary_id": "reports/image.cnbcfm.com/api/v1/image/100266733-38cbdbd7a4bcc3cf98120f6574fe94a3afe0fca6.jpg?v=1529461359&w=1920&h=1080", - "date_downloaded": "2023-08-04", - "date_modified": "2023-08-07", - "date_published": "2023-08-04", - "date_submitted": "2023-08-03", - "description": null, - "editor_notes": "editor notes v2", - "embedding": null, - "epoch_date_downloaded": 1691107200, - "epoch_date_modified": 1691455300, - "epoch_date_published": 1691107200, - "epoch_date_submitted": 1691020800, - "flag": null, - "image_url": "https://image.cnbcfm.com/api/v1/image/100266733-38cbdbd7a4bcc3cf98120f6574fe94a3afe0fca6.jpg?v=1529461359&w=1920&h=1080", - "inputs_outputs": ["input 1"], - "is_incident_report": true, - "language": "es", - "modifiedBy": "1", - "plain_text": "\n\nBy Aimee Picchi\n\nSeptember 24, 2015 / 11:41 AM / MoneyWatch\n\nFor Starbucks (SBUX) barista Kylei Weisse, working at the coffee chain helps him secure health insurance and some extra money while he studies at Georgia Perimeter College. What it doesn't provide is the kind of stable schedule that the company promised its workers last year.\n\n\"It's the wild inconsistency\" of the hours that's a problem, Weisse, 32, said. \"We're supposed to get them 10 days in advance, which often happens, but there's no guarantee. If our manager doesn't get it to us on time, we just have to deal with it.\"\n\nThat became a problem recently when Weisse's manager gave him only a few days notice on his work hours, which ended up conflicting with an anatomy and physiology exam at his college. Weisse ended up paying another worker $20 to take his shift so he could take the exam.\n\nThe short notice is especially frustrating because of Starbucks' vow last year to post employees' schedules at least 10 days in advance, as well as the company's insistence that workers provide at least one-month notice when they need to take a day off.\n\nWhat's behind Starbucks price increases? 03:04\n\nWeisse isn't alone in complaining that Starbucks isn't living up to its promises to overhaul its labor practices for its roughly 130,000 baristas. That vow followed an article last year by The New York Times that detailed how workers were struggling to manage childcare and other obligations when the company provided only a few days notice about their schedules.\n\nAbout half of roughly 200 Starbucks baristas in a recent survey said they are still receiving their schedule with less than one week's notice. Others also reported being asked to handle \"clopens,\" split shifts in which employees work a closing shift late into the evening and then an early opening shift the following morning. The company last year promised to end the practice.\n\nOf course, Starbucks isn't alone in using \"just-in-time\" scheduling, with the retail and restaurant industry increasingly turning to software that allows them to change work schedules at the last minute, depending on whether business picks up or slows down. But it is Starbucks that has become a lightning rod on the issue given its vows to improve how it treats employees and its own emphatic claims to valuing workers, whom it labels \"partners.\"\n\n\"Starbucks has the values and wants to do right by their employees,\" said Carrie Gleason, director of the Fair Workweek Initiative at the Center for Popular Democracy, an advocacy group focused on workers'rights, and a co-author of the group's new report on the company's labor practices. \"However, since last year when the company recognized there was a serious problem with the way it scheduled workers and pledged to reform, still so many of the same issues persist.\"\n\nStarbucks didn't respond to requests for comment on the study or on baristas' reports of labor practices that are failing to meet the company's stated goals.\n\nIn an internal memo this week published by Time, Starbucks executive Cliff Burrows wrote that the company couldn't validate the survey, but added that \"the findings suggest, contrary to the expectations we have in place, that some partners are receiving their schedules less than one week in advance and that there is a continuing issue with some partners working a close and then an opening shift the following morning.\" He asks store managers \"to go the extra mile to ensure partners have a consistent schedule.\"\n\nStarbucks ends \"race together\" campaign amid public backlash 00:26\n\nTo be sure, some Starbucks workers are receiving at least 10 days notice on their work hours, with the survey finding that about one-third receive two weeks notice and another 18 percent get their schedules three weeks in advance. But that leaves almost half of workers who only receive one week's notice, making it more difficult from them manage other obligations, such as school, family commitments or other jobs.\n\nClopens remain a problem, as well. About 60 percent of workers who have to handle a clopen receive seven or fewer hours of rest between a closing and an opening shift, the study found.\n\nThat's prompted one former Starbucks employee to start a petition to end the practice of scheduling clopens. Ciara Moran noted in her petition that she sometimes was only able to get four or five hours of sleep on the days she was scheduled for clopens. She said she quit her job because she doubted whether it was possible to get ahead given the demands on workers.\n\nEven if Starbucks stuck with its policy of providing eight hours between shifts, that's not enough time, especially given that many workers in the service sector have long commutes, the study said.\n\nAnother issue singled out by the report is Starbucks' practices on sick time. Since paid time off is only available to workers with at least a year on the job, about 40 percent of employees in the survey said they had dealt with barriers in taking sick days.\n\nIn a perfect world, Weisse said he'd like to receive his schedule either a month or a quarter in advance. Asked if he's upset about the last-minute schedules, Weisse said he had become resigned to it.\n\n\"I don't know if you've ever seen experiments with rats,\" he said. \"First they get upset if you treat them badly. Then they lay down and try not to move too much. I'm like that now. Things are terrible.\"\n\nV2\n", - "report_number": 3206, - "submitters": ["Test User v2"], - "tags": [], - "text": "[](/moneywatch)\n\nBy Aimee Picchi\n\nSeptember 24, 2015 / 11:41 AM / MoneyWatch\n\nFor Starbucks ([SBUX](http://markets.cbsnews.com/SBUX/quote/)) barista Kylei Weisse, working at the coffee chain helps him secure health insurance and some extra money while he studies at Georgia Perimeter College. What it doesn't provide is the kind of stable schedule that the [company promised its workers](https://www.cbsnews.com/news/for-some-starbucks-workers-job-leaves-bitter-taste/) last year.\n\n\"It's the wild inconsistency\" of the hours that's a problem, Weisse, 32, said. \"We're supposed to get them 10 days in advance, which often happens, but there's no guarantee. If our manager doesn't get it to us on time, we just have to deal with it.\"\n\nThat became a problem recently when Weisse's manager gave him only a few days notice on his work hours, which ended up conflicting with an anatomy and physiology exam at his college. Weisse ended up paying another worker $20 to take his shift so he could take the exam.\n\nThe short notice is especially frustrating because of Starbucks' vow last year to post employees' schedules at least 10 days in advance, as well as the company's insistence that workers provide at least one-month notice when they need to take a day off.\n\n[What's behind Starbucks price increases? 03:04](https://www.cbsnews.com/video/whats-behind-starbucks-price-increases/)\n\nWeisse isn't alone in complaining that Starbucks isn't living up to its promises to overhaul its labor practices for its roughly 130,000 baristas. That vow followed an [article last year by The New York Times](http://www.nytimes.com/interactive/2014/08/13/us/starbucks-workers-scheduling-hours.html?_r=1) that detailed how workers were struggling to manage childcare and other obligations when the company provided only a few days notice about their schedules.\n\nAbout half of roughly 200 Starbucks baristas [in a recent survey](http://static1.squarespace.com/static/556496efe4b02c9d26fdf26a/t/56027485e4b02ead4f16bc2a/1443001477305/The+Grind.pdf) said they are still receiving their schedule with less than one week's notice. Others also reported being asked to handle \"clopens,\" split shifts in which employees work a closing shift late into the evening and then an early opening shift the following morning. The company last year promised to end the practice.\n\nOf course, Starbucks isn't alone in using [\"just-in-time\" scheduling](https://www.cbsnews.com/news/in-retail-life-on-the-job-often-leads-to-a-dead-end/), with the retail and restaurant industry increasingly turning to software that allows them to change work schedules at the last minute, depending on whether business picks up or slows down. But it is Starbucks that has become a lightning rod on the issue given its vows to improve how it treats employees and its own [emphatic claims](http://www.starbucks.com/careers/working-at-starbucks) to valuing workers, whom it labels \"partners.\"\n\n\"Starbucks has the values and wants to do right by their employees,\" said Carrie Gleason, director of the Fair Workweek Initiative at the Center for Popular Democracy, an advocacy group focused on workers'rights, and a co-author of the group's new [report on the company's labor practices](http://populardemocracy.org/sites/default/files/The%2BGrind.pdf). \"However, since last year when the company recognized there was a serious problem with the way it scheduled workers and pledged to reform, still so many of the same issues persist.\"\n\nStarbucks didn't respond to requests for comment on the study or on baristas' reports of labor practices that are failing to meet the company's stated goals.\n\nIn an internal memo this week [published by Time](http://time.com/4047359/starbucks-scheduling-labor-practices-memo/), Starbucks executive Cliff Burrows wrote that the company couldn't validate the survey, but added that \"the findings suggest, contrary to the expectations we have in place, that some partners are receiving their schedules less than one week in advance and that there is a continuing issue with some partners working a close and then an opening shift the following morning.\" He asks store managers \"to go the extra mile to ensure partners have a consistent schedule.\"\n\n[Starbucks ends \"race together\" campaign amid public backlash 00:26](https://www.cbsnews.com/video/starbucks-ends-race-together-campaign-amid-public-backlash/)\n\nTo be sure, some Starbucks workers are receiving at least 10 days notice on their work hours, with the survey finding that about one-third receive two weeks notice and another 18 percent get their schedules three weeks in advance. But that leaves almost half of workers who only receive one week's notice, making it more difficult from them manage other obligations, such as school, family commitments or other jobs.\n\nClopens remain a problem, as well. About 60 percent of workers who have to handle a clopen receive seven or fewer hours of rest between a closing and an opening shift, the study found.\n\nThat's prompted one former Starbucks employee to start a petition to end the practice of scheduling clopens. Ciara Moran noted in her petition that she sometimes was only able to get [four or five hours of sleep](https://www.coworker.org/petitions/starbucks-end-clopens-now) on the days she was scheduled for clopens. She said she quit her job because she doubted whether it was possible to get ahead given the demands on workers.\n\nEven if Starbucks stuck with its policy of providing eight hours between shifts, that's not enough time, especially given that many workers in the service sector have long commutes, the study said.\n\nAnother issue singled out by the report is Starbucks' practices on sick time. Since paid time off is only available to workers with at least a year on the job, about 40 percent of employees in the survey said they had dealt with barriers in taking sick days.\n\nIn a perfect world, Weisse said he'd like to receive his schedule either a month or a quarter in advance. Asked if he's upset about the last-minute schedules, Weisse said he had become resigned to it.\n\n\"I don't know if you've ever seen experiments with rats,\" he said. \"First they get upset if you treat them badly. Then they lay down and try not to move too much. I'm like that now. Things are terrible.\"\n\nV2", - "title": "​Is Starbucks shortchanging its baristas? v2", - "url": "https://www.cbsnews.com/news/is-starbucks-shortchanging-its-baristas", - "source_domain": "cbsnews.com", - "user": "1" - }, - { - "_id": "64d18f2f1a4fcdeaf1372bf4", - "authors": ["Pablo Costa v2", "Pablo Costa v3", "Pablo Costa v4"], - "cloudinary_id": "reports/image.cnbcfm.com/api/v1/image/100266733-38cbdbd7a4bcc3cf98120f6574fe94a3afe0fca6.jpg?v=1529461359&w=1920&h=1080", - "date_downloaded": "2023-08-04", - "date_modified": "2023-08-07", - "date_published": "2023-08-04", - "date_submitted": "2023-08-03", - "description": null, - "editor_notes": "editor notes v2", - "embedding": null, - "epoch_date_downloaded": 1691107200, - "epoch_date_modified": 1691455276, - "epoch_date_published": 1691107200, - "epoch_date_submitted": 1691020800, - "flag": null, - "image_url": "https://image.cnbcfm.com/api/v1/image/100266733-38cbdbd7a4bcc3cf98120f6574fe94a3afe0fca6.jpg?v=1529461359&w=1920&h=1080", - "inputs_outputs": ["input 1", "output 1", "input 2", "output 2"], - "is_incident_report": true, - "language": "es", - "modifiedBy": "1", - "plain_text": "\n\nBy Aimee Picchi\n\nSeptember 24, 2015 / 11:41 AM / MoneyWatch\n\nFor Starbucks (SBUX) barista Kylei Weisse, working at the coffee chain helps him secure health insurance and some extra money while he studies at Georgia Perimeter College. What it doesn't provide is the kind of stable schedule that the company promised its workers last year.\n\n\"It's the wild inconsistency\" of the hours that's a problem, Weisse, 32, said. \"We're supposed to get them 10 days in advance, which often happens, but there's no guarantee. If our manager doesn't get it to us on time, we just have to deal with it.\"\n\nThat became a problem recently when Weisse's manager gave him only a few days notice on his work hours, which ended up conflicting with an anatomy and physiology exam at his college. Weisse ended up paying another worker $20 to take his shift so he could take the exam.\n\nThe short notice is especially frustrating because of Starbucks' vow last year to post employees' schedules at least 10 days in advance, as well as the company's insistence that workers provide at least one-month notice when they need to take a day off.\n\nWhat's behind Starbucks price increases? 03:04\n\nWeisse isn't alone in complaining that Starbucks isn't living up to its promises to overhaul its labor practices for its roughly 130,000 baristas. That vow followed an article last year by The New York Times that detailed how workers were struggling to manage childcare and other obligations when the company provided only a few days notice about their schedules.\n\nAbout half of roughly 200 Starbucks baristas in a recent survey said they are still receiving their schedule with less than one week's notice. Others also reported being asked to handle \"clopens,\" split shifts in which employees work a closing shift late into the evening and then an early opening shift the following morning. The company last year promised to end the practice.\n\nOf course, Starbucks isn't alone in using \"just-in-time\" scheduling, with the retail and restaurant industry increasingly turning to software that allows them to change work schedules at the last minute, depending on whether business picks up or slows down. But it is Starbucks that has become a lightning rod on the issue given its vows to improve how it treats employees and its own emphatic claims to valuing workers, whom it labels \"partners.\"\n\n\"Starbucks has the values and wants to do right by their employees,\" said Carrie Gleason, director of the Fair Workweek Initiative at the Center for Popular Democracy, an advocacy group focused on workers'rights, and a co-author of the group's new report on the company's labor practices. \"However, since last year when the company recognized there was a serious problem with the way it scheduled workers and pledged to reform, still so many of the same issues persist.\"\n\nStarbucks didn't respond to requests for comment on the study or on baristas' reports of labor practices that are failing to meet the company's stated goals.\n\nIn an internal memo this week published by Time, Starbucks executive Cliff Burrows wrote that the company couldn't validate the survey, but added that \"the findings suggest, contrary to the expectations we have in place, that some partners are receiving their schedules less than one week in advance and that there is a continuing issue with some partners working a close and then an opening shift the following morning.\" He asks store managers \"to go the extra mile to ensure partners have a consistent schedule.\"\n\nStarbucks ends \"race together\" campaign amid public backlash 00:26\n\nTo be sure, some Starbucks workers are receiving at least 10 days notice on their work hours, with the survey finding that about one-third receive two weeks notice and another 18 percent get their schedules three weeks in advance. But that leaves almost half of workers who only receive one week's notice, making it more difficult from them manage other obligations, such as school, family commitments or other jobs.\n\nClopens remain a problem, as well. About 60 percent of workers who have to handle a clopen receive seven or fewer hours of rest between a closing and an opening shift, the study found.\n\nThat's prompted one former Starbucks employee to start a petition to end the practice of scheduling clopens. Ciara Moran noted in her petition that she sometimes was only able to get four or five hours of sleep on the days she was scheduled for clopens. She said she quit her job because she doubted whether it was possible to get ahead given the demands on workers.\n\nEven if Starbucks stuck with its policy of providing eight hours between shifts, that's not enough time, especially given that many workers in the service sector have long commutes, the study said.\n\nAnother issue singled out by the report is Starbucks' practices on sick time. Since paid time off is only available to workers with at least a year on the job, about 40 percent of employees in the survey said they had dealt with barriers in taking sick days.\n\nIn a perfect world, Weisse said he'd like to receive his schedule either a month or a quarter in advance. Asked if he's upset about the last-minute schedules, Weisse said he had become resigned to it.\n\n\"I don't know if you've ever seen experiments with rats,\" he said. \"First they get upset if you treat them badly. Then they lay down and try not to move too much. I'm like that now. Things are terrible.\"\n\nV2\n", - "report_number": 3206, - "submitters": ["Test User v2"], - "tags": [], - "text": "[](/moneywatch)\n\nBy Aimee Picchi\n\nSeptember 24, 2015 / 11:41 AM / MoneyWatch\n\nFor Starbucks ([SBUX](http://markets.cbsnews.com/SBUX/quote/)) barista Kylei Weisse, working at the coffee chain helps him secure health insurance and some extra money while he studies at Georgia Perimeter College. What it doesn't provide is the kind of stable schedule that the [company promised its workers](https://www.cbsnews.com/news/for-some-starbucks-workers-job-leaves-bitter-taste/) last year.\n\n\"It's the wild inconsistency\" of the hours that's a problem, Weisse, 32, said. \"We're supposed to get them 10 days in advance, which often happens, but there's no guarantee. If our manager doesn't get it to us on time, we just have to deal with it.\"\n\nThat became a problem recently when Weisse's manager gave him only a few days notice on his work hours, which ended up conflicting with an anatomy and physiology exam at his college. Weisse ended up paying another worker $20 to take his shift so he could take the exam.\n\nThe short notice is especially frustrating because of Starbucks' vow last year to post employees' schedules at least 10 days in advance, as well as the company's insistence that workers provide at least one-month notice when they need to take a day off.\n\n[What's behind Starbucks price increases? 03:04](https://www.cbsnews.com/video/whats-behind-starbucks-price-increases/)\n\nWeisse isn't alone in complaining that Starbucks isn't living up to its promises to overhaul its labor practices for its roughly 130,000 baristas. That vow followed an [article last year by The New York Times](http://www.nytimes.com/interactive/2014/08/13/us/starbucks-workers-scheduling-hours.html?_r=1) that detailed how workers were struggling to manage childcare and other obligations when the company provided only a few days notice about their schedules.\n\nAbout half of roughly 200 Starbucks baristas [in a recent survey](http://static1.squarespace.com/static/556496efe4b02c9d26fdf26a/t/56027485e4b02ead4f16bc2a/1443001477305/The+Grind.pdf) said they are still receiving their schedule with less than one week's notice. Others also reported being asked to handle \"clopens,\" split shifts in which employees work a closing shift late into the evening and then an early opening shift the following morning. The company last year promised to end the practice.\n\nOf course, Starbucks isn't alone in using [\"just-in-time\" scheduling](https://www.cbsnews.com/news/in-retail-life-on-the-job-often-leads-to-a-dead-end/), with the retail and restaurant industry increasingly turning to software that allows them to change work schedules at the last minute, depending on whether business picks up or slows down. But it is Starbucks that has become a lightning rod on the issue given its vows to improve how it treats employees and its own [emphatic claims](http://www.starbucks.com/careers/working-at-starbucks) to valuing workers, whom it labels \"partners.\"\n\n\"Starbucks has the values and wants to do right by their employees,\" said Carrie Gleason, director of the Fair Workweek Initiative at the Center for Popular Democracy, an advocacy group focused on workers'rights, and a co-author of the group's new [report on the company's labor practices](http://populardemocracy.org/sites/default/files/The%2BGrind.pdf). \"However, since last year when the company recognized there was a serious problem with the way it scheduled workers and pledged to reform, still so many of the same issues persist.\"\n\nStarbucks didn't respond to requests for comment on the study or on baristas' reports of labor practices that are failing to meet the company's stated goals.\n\nIn an internal memo this week [published by Time](http://time.com/4047359/starbucks-scheduling-labor-practices-memo/), Starbucks executive Cliff Burrows wrote that the company couldn't validate the survey, but added that \"the findings suggest, contrary to the expectations we have in place, that some partners are receiving their schedules less than one week in advance and that there is a continuing issue with some partners working a close and then an opening shift the following morning.\" He asks store managers \"to go the extra mile to ensure partners have a consistent schedule.\"\n\n[Starbucks ends \"race together\" campaign amid public backlash 00:26](https://www.cbsnews.com/video/starbucks-ends-race-together-campaign-amid-public-backlash/)\n\nTo be sure, some Starbucks workers are receiving at least 10 days notice on their work hours, with the survey finding that about one-third receive two weeks notice and another 18 percent get their schedules three weeks in advance. But that leaves almost half of workers who only receive one week's notice, making it more difficult from them manage other obligations, such as school, family commitments or other jobs.\n\nClopens remain a problem, as well. About 60 percent of workers who have to handle a clopen receive seven or fewer hours of rest between a closing and an opening shift, the study found.\n\nThat's prompted one former Starbucks employee to start a petition to end the practice of scheduling clopens. Ciara Moran noted in her petition that she sometimes was only able to get [four or five hours of sleep](https://www.coworker.org/petitions/starbucks-end-clopens-now) on the days she was scheduled for clopens. She said she quit her job because she doubted whether it was possible to get ahead given the demands on workers.\n\nEven if Starbucks stuck with its policy of providing eight hours between shifts, that's not enough time, especially given that many workers in the service sector have long commutes, the study said.\n\nAnother issue singled out by the report is Starbucks' practices on sick time. Since paid time off is only available to workers with at least a year on the job, about 40 percent of employees in the survey said they had dealt with barriers in taking sick days.\n\nIn a perfect world, Weisse said he'd like to receive his schedule either a month or a quarter in advance. Asked if he's upset about the last-minute schedules, Weisse said he had become resigned to it.\n\n\"I don't know if you've ever seen experiments with rats,\" he said. \"First they get upset if you treat them badly. Then they lay down and try not to move too much. I'm like that now. Things are terrible.\"\n\nV2", - "title": "​Is Starbucks shortchanging its baristas? v2", - "url": "https://www.cbsnews.com/news/is-starbucks-shortchanging-its-baristas", - "source_domain": "cbsnews.com", - "user": "1" - }, - { - "_id": "64d18e9e1bae3610af2a8fee", - "authors": ["Pablo Costa v2", "Pablo Costa v3", "Pablo Costa v4"], - "cloudinary_id": "reports/image.cnbcfm.com/api/v1/image/100266733-38cbdbd7a4bcc3cf98120f6574fe94a3afe0fca6.jpg?v=1529461359&w=1920&h=1080", - "date_downloaded": "2023-08-04", - "date_modified": "2023-08-07", - "date_published": "2023-08-04", - "date_submitted": "2023-08-03", - "description": null, - "editor_notes": "editor notes v2", - "embedding": null, - "epoch_date_downloaded": 1691107200, - "epoch_date_modified": 1691455131, - "epoch_date_published": 1691107200, - "epoch_date_submitted": 1691020800, - "flag": null, - "image_url": "https://image.cnbcfm.com/api/v1/image/100266733-38cbdbd7a4bcc3cf98120f6574fe94a3afe0fca6.jpg?v=1529461359&w=1920&h=1080", - "inputs_outputs": ["input 1", "output 1", "input 2", "output 2", "input 3", "output 3"], - "is_incident_report": true, - "language": "es", - "modifiedBy": "1", - "plain_text": "\n\nBy Aimee Picchi\n\nSeptember 24, 2015 / 11:41 AM / MoneyWatch\n\nFor Starbucks (SBUX) barista Kylei Weisse, working at the coffee chain helps him secure health insurance and some extra money while he studies at Georgia Perimeter College. What it doesn't provide is the kind of stable schedule that the company promised its workers last year.\n\n\"It's the wild inconsistency\" of the hours that's a problem, Weisse, 32, said. \"We're supposed to get them 10 days in advance, which often happens, but there's no guarantee. If our manager doesn't get it to us on time, we just have to deal with it.\"\n\nThat became a problem recently when Weisse's manager gave him only a few days notice on his work hours, which ended up conflicting with an anatomy and physiology exam at his college. Weisse ended up paying another worker $20 to take his shift so he could take the exam.\n\nThe short notice is especially frustrating because of Starbucks' vow last year to post employees' schedules at least 10 days in advance, as well as the company's insistence that workers provide at least one-month notice when they need to take a day off.\n\nWhat's behind Starbucks price increases? 03:04\n\nWeisse isn't alone in complaining that Starbucks isn't living up to its promises to overhaul its labor practices for its roughly 130,000 baristas. That vow followed an article last year by The New York Times that detailed how workers were struggling to manage childcare and other obligations when the company provided only a few days notice about their schedules.\n\nAbout half of roughly 200 Starbucks baristas in a recent survey said they are still receiving their schedule with less than one week's notice. Others also reported being asked to handle \"clopens,\" split shifts in which employees work a closing shift late into the evening and then an early opening shift the following morning. The company last year promised to end the practice.\n\nOf course, Starbucks isn't alone in using \"just-in-time\" scheduling, with the retail and restaurant industry increasingly turning to software that allows them to change work schedules at the last minute, depending on whether business picks up or slows down. But it is Starbucks that has become a lightning rod on the issue given its vows to improve how it treats employees and its own emphatic claims to valuing workers, whom it labels \"partners.\"\n\n\"Starbucks has the values and wants to do right by their employees,\" said Carrie Gleason, director of the Fair Workweek Initiative at the Center for Popular Democracy, an advocacy group focused on workers'rights, and a co-author of the group's new report on the company's labor practices. \"However, since last year when the company recognized there was a serious problem with the way it scheduled workers and pledged to reform, still so many of the same issues persist.\"\n\nStarbucks didn't respond to requests for comment on the study or on baristas' reports of labor practices that are failing to meet the company's stated goals.\n\nIn an internal memo this week published by Time, Starbucks executive Cliff Burrows wrote that the company couldn't validate the survey, but added that \"the findings suggest, contrary to the expectations we have in place, that some partners are receiving their schedules less than one week in advance and that there is a continuing issue with some partners working a close and then an opening shift the following morning.\" He asks store managers \"to go the extra mile to ensure partners have a consistent schedule.\"\n\nStarbucks ends \"race together\" campaign amid public backlash 00:26\n\nTo be sure, some Starbucks workers are receiving at least 10 days notice on their work hours, with the survey finding that about one-third receive two weeks notice and another 18 percent get their schedules three weeks in advance. But that leaves almost half of workers who only receive one week's notice, making it more difficult from them manage other obligations, such as school, family commitments or other jobs.\n\nClopens remain a problem, as well. About 60 percent of workers who have to handle a clopen receive seven or fewer hours of rest between a closing and an opening shift, the study found.\n\nThat's prompted one former Starbucks employee to start a petition to end the practice of scheduling clopens. Ciara Moran noted in her petition that she sometimes was only able to get four or five hours of sleep on the days she was scheduled for clopens. She said she quit her job because she doubted whether it was possible to get ahead given the demands on workers.\n\nEven if Starbucks stuck with its policy of providing eight hours between shifts, that's not enough time, especially given that many workers in the service sector have long commutes, the study said.\n\nAnother issue singled out by the report is Starbucks' practices on sick time. Since paid time off is only available to workers with at least a year on the job, about 40 percent of employees in the survey said they had dealt with barriers in taking sick days.\n\nIn a perfect world, Weisse said he'd like to receive his schedule either a month or a quarter in advance. Asked if he's upset about the last-minute schedules, Weisse said he had become resigned to it.\n\n\"I don't know if you've ever seen experiments with rats,\" he said. \"First they get upset if you treat them badly. Then they lay down and try not to move too much. I'm like that now. Things are terrible.\"\n\nV2\n", - "report_number": 3206, - "submitters": ["Test User v2"], - "tags": [], - "text": "[](/moneywatch)\n\nBy Aimee Picchi\n\nSeptember 24, 2015 / 11:41 AM / MoneyWatch\n\nFor Starbucks ([SBUX](http://markets.cbsnews.com/SBUX/quote/)) barista Kylei Weisse, working at the coffee chain helps him secure health insurance and some extra money while he studies at Georgia Perimeter College. What it doesn't provide is the kind of stable schedule that the [company promised its workers](https://www.cbsnews.com/news/for-some-starbucks-workers-job-leaves-bitter-taste/) last year.\n\n\"It's the wild inconsistency\" of the hours that's a problem, Weisse, 32, said. \"We're supposed to get them 10 days in advance, which often happens, but there's no guarantee. If our manager doesn't get it to us on time, we just have to deal with it.\"\n\nThat became a problem recently when Weisse's manager gave him only a few days notice on his work hours, which ended up conflicting with an anatomy and physiology exam at his college. Weisse ended up paying another worker $20 to take his shift so he could take the exam.\n\nThe short notice is especially frustrating because of Starbucks' vow last year to post employees' schedules at least 10 days in advance, as well as the company's insistence that workers provide at least one-month notice when they need to take a day off.\n\n[What's behind Starbucks price increases? 03:04](https://www.cbsnews.com/video/whats-behind-starbucks-price-increases/)\n\nWeisse isn't alone in complaining that Starbucks isn't living up to its promises to overhaul its labor practices for its roughly 130,000 baristas. That vow followed an [article last year by The New York Times](http://www.nytimes.com/interactive/2014/08/13/us/starbucks-workers-scheduling-hours.html?_r=1) that detailed how workers were struggling to manage childcare and other obligations when the company provided only a few days notice about their schedules.\n\nAbout half of roughly 200 Starbucks baristas [in a recent survey](http://static1.squarespace.com/static/556496efe4b02c9d26fdf26a/t/56027485e4b02ead4f16bc2a/1443001477305/The+Grind.pdf) said they are still receiving their schedule with less than one week's notice. Others also reported being asked to handle \"clopens,\" split shifts in which employees work a closing shift late into the evening and then an early opening shift the following morning. The company last year promised to end the practice.\n\nOf course, Starbucks isn't alone in using [\"just-in-time\" scheduling](https://www.cbsnews.com/news/in-retail-life-on-the-job-often-leads-to-a-dead-end/), with the retail and restaurant industry increasingly turning to software that allows them to change work schedules at the last minute, depending on whether business picks up or slows down. But it is Starbucks that has become a lightning rod on the issue given its vows to improve how it treats employees and its own [emphatic claims](http://www.starbucks.com/careers/working-at-starbucks) to valuing workers, whom it labels \"partners.\"\n\n\"Starbucks has the values and wants to do right by their employees,\" said Carrie Gleason, director of the Fair Workweek Initiative at the Center for Popular Democracy, an advocacy group focused on workers'rights, and a co-author of the group's new [report on the company's labor practices](http://populardemocracy.org/sites/default/files/The%2BGrind.pdf). \"However, since last year when the company recognized there was a serious problem with the way it scheduled workers and pledged to reform, still so many of the same issues persist.\"\n\nStarbucks didn't respond to requests for comment on the study or on baristas' reports of labor practices that are failing to meet the company's stated goals.\n\nIn an internal memo this week [published by Time](http://time.com/4047359/starbucks-scheduling-labor-practices-memo/), Starbucks executive Cliff Burrows wrote that the company couldn't validate the survey, but added that \"the findings suggest, contrary to the expectations we have in place, that some partners are receiving their schedules less than one week in advance and that there is a continuing issue with some partners working a close and then an opening shift the following morning.\" He asks store managers \"to go the extra mile to ensure partners have a consistent schedule.\"\n\n[Starbucks ends \"race together\" campaign amid public backlash 00:26](https://www.cbsnews.com/video/starbucks-ends-race-together-campaign-amid-public-backlash/)\n\nTo be sure, some Starbucks workers are receiving at least 10 days notice on their work hours, with the survey finding that about one-third receive two weeks notice and another 18 percent get their schedules three weeks in advance. But that leaves almost half of workers who only receive one week's notice, making it more difficult from them manage other obligations, such as school, family commitments or other jobs.\n\nClopens remain a problem, as well. About 60 percent of workers who have to handle a clopen receive seven or fewer hours of rest between a closing and an opening shift, the study found.\n\nThat's prompted one former Starbucks employee to start a petition to end the practice of scheduling clopens. Ciara Moran noted in her petition that she sometimes was only able to get [four or five hours of sleep](https://www.coworker.org/petitions/starbucks-end-clopens-now) on the days she was scheduled for clopens. She said she quit her job because she doubted whether it was possible to get ahead given the demands on workers.\n\nEven if Starbucks stuck with its policy of providing eight hours between shifts, that's not enough time, especially given that many workers in the service sector have long commutes, the study said.\n\nAnother issue singled out by the report is Starbucks' practices on sick time. Since paid time off is only available to workers with at least a year on the job, about 40 percent of employees in the survey said they had dealt with barriers in taking sick days.\n\nIn a perfect world, Weisse said he'd like to receive his schedule either a month or a quarter in advance. Asked if he's upset about the last-minute schedules, Weisse said he had become resigned to it.\n\n\"I don't know if you've ever seen experiments with rats,\" he said. \"First they get upset if you treat them badly. Then they lay down and try not to move too much. I'm like that now. Things are terrible.\"\n\nV2", - "title": "​Is Starbucks shortchanging its baristas? v2", - "url": "https://www.cbsnews.com/news/is-starbucks-shortchanging-its-baristas", - "source_domain": "cbsnews.com", - "user": "1" - }, - { - "_id": "64d18dccf549de65c4e6f6d9", - "authors": ["Pablo Costa v2", "Pablo Costa v3", "Pablo Costa v4"], - "cloudinary_id": "reports/image.cnbcfm.com/api/v1/image/100266733-38cbdbd7a4bcc3cf98120f6574fe94a3afe0fca6.jpg?v=1529461359&w=1920&h=1080", - "date_downloaded": "2023-08-04", - "date_modified": "2023-08-07", - "date_published": "2023-08-04", - "date_submitted": "2023-08-03", - "description": null, - "editor_notes": "editor notes v2", - "embedding": null, - "epoch_date_downloaded": 1691107200, - "epoch_date_modified": 1691454921, - "epoch_date_published": 1691107200, - "epoch_date_submitted": 1691020800, - "flag": null, - "image_url": "https://image.cnbcfm.com/api/v1/image/100266733-38cbdbd7a4bcc3cf98120f6574fe94a3afe0fca6.jpg?v=1529461359&w=1920&h=1080", - "inputs_outputs": ["input 1", "output 1", "input 2", "output 2"], - "is_incident_report": true, - "language": "es", - "modifiedBy": "1", - "plain_text": "\n\nBy Aimee Picchi\n\nSeptember 24, 2015 / 11:41 AM / MoneyWatch\n\nFor Starbucks (SBUX) barista Kylei Weisse, working at the coffee chain helps him secure health insurance and some extra money while he studies at Georgia Perimeter College. What it doesn't provide is the kind of stable schedule that the company promised its workers last year.\n\n\"It's the wild inconsistency\" of the hours that's a problem, Weisse, 32, said. \"We're supposed to get them 10 days in advance, which often happens, but there's no guarantee. If our manager doesn't get it to us on time, we just have to deal with it.\"\n\nThat became a problem recently when Weisse's manager gave him only a few days notice on his work hours, which ended up conflicting with an anatomy and physiology exam at his college. Weisse ended up paying another worker $20 to take his shift so he could take the exam.\n\nThe short notice is especially frustrating because of Starbucks' vow last year to post employees' schedules at least 10 days in advance, as well as the company's insistence that workers provide at least one-month notice when they need to take a day off.\n\nWhat's behind Starbucks price increases? 03:04\n\nWeisse isn't alone in complaining that Starbucks isn't living up to its promises to overhaul its labor practices for its roughly 130,000 baristas. That vow followed an article last year by The New York Times that detailed how workers were struggling to manage childcare and other obligations when the company provided only a few days notice about their schedules.\n\nAbout half of roughly 200 Starbucks baristas in a recent survey said they are still receiving their schedule with less than one week's notice. Others also reported being asked to handle \"clopens,\" split shifts in which employees work a closing shift late into the evening and then an early opening shift the following morning. The company last year promised to end the practice.\n\nOf course, Starbucks isn't alone in using \"just-in-time\" scheduling, with the retail and restaurant industry increasingly turning to software that allows them to change work schedules at the last minute, depending on whether business picks up or slows down. But it is Starbucks that has become a lightning rod on the issue given its vows to improve how it treats employees and its own emphatic claims to valuing workers, whom it labels \"partners.\"\n\n\"Starbucks has the values and wants to do right by their employees,\" said Carrie Gleason, director of the Fair Workweek Initiative at the Center for Popular Democracy, an advocacy group focused on workers'rights, and a co-author of the group's new report on the company's labor practices. \"However, since last year when the company recognized there was a serious problem with the way it scheduled workers and pledged to reform, still so many of the same issues persist.\"\n\nStarbucks didn't respond to requests for comment on the study or on baristas' reports of labor practices that are failing to meet the company's stated goals.\n\nIn an internal memo this week published by Time, Starbucks executive Cliff Burrows wrote that the company couldn't validate the survey, but added that \"the findings suggest, contrary to the expectations we have in place, that some partners are receiving their schedules less than one week in advance and that there is a continuing issue with some partners working a close and then an opening shift the following morning.\" He asks store managers \"to go the extra mile to ensure partners have a consistent schedule.\"\n\nStarbucks ends \"race together\" campaign amid public backlash 00:26\n\nTo be sure, some Starbucks workers are receiving at least 10 days notice on their work hours, with the survey finding that about one-third receive two weeks notice and another 18 percent get their schedules three weeks in advance. But that leaves almost half of workers who only receive one week's notice, making it more difficult from them manage other obligations, such as school, family commitments or other jobs.\n\nClopens remain a problem, as well. About 60 percent of workers who have to handle a clopen receive seven or fewer hours of rest between a closing and an opening shift, the study found.\n\nThat's prompted one former Starbucks employee to start a petition to end the practice of scheduling clopens. Ciara Moran noted in her petition that she sometimes was only able to get four or five hours of sleep on the days she was scheduled for clopens. She said she quit her job because she doubted whether it was possible to get ahead given the demands on workers.\n\nEven if Starbucks stuck with its policy of providing eight hours between shifts, that's not enough time, especially given that many workers in the service sector have long commutes, the study said.\n\nAnother issue singled out by the report is Starbucks' practices on sick time. Since paid time off is only available to workers with at least a year on the job, about 40 percent of employees in the survey said they had dealt with barriers in taking sick days.\n\nIn a perfect world, Weisse said he'd like to receive his schedule either a month or a quarter in advance. Asked if he's upset about the last-minute schedules, Weisse said he had become resigned to it.\n\n\"I don't know if you've ever seen experiments with rats,\" he said. \"First they get upset if you treat them badly. Then they lay down and try not to move too much. I'm like that now. Things are terrible.\"\n\nV2\n", - "report_number": 3206, - "submitters": ["Test User v2"], - "tags": [], - "text": "[](/moneywatch)\n\nBy Aimee Picchi\n\nSeptember 24, 2015 / 11:41 AM / MoneyWatch\n\nFor Starbucks ([SBUX](http://markets.cbsnews.com/SBUX/quote/)) barista Kylei Weisse, working at the coffee chain helps him secure health insurance and some extra money while he studies at Georgia Perimeter College. What it doesn't provide is the kind of stable schedule that the [company promised its workers](https://www.cbsnews.com/news/for-some-starbucks-workers-job-leaves-bitter-taste/) last year.\n\n\"It's the wild inconsistency\" of the hours that's a problem, Weisse, 32, said. \"We're supposed to get them 10 days in advance, which often happens, but there's no guarantee. If our manager doesn't get it to us on time, we just have to deal with it.\"\n\nThat became a problem recently when Weisse's manager gave him only a few days notice on his work hours, which ended up conflicting with an anatomy and physiology exam at his college. Weisse ended up paying another worker $20 to take his shift so he could take the exam.\n\nThe short notice is especially frustrating because of Starbucks' vow last year to post employees' schedules at least 10 days in advance, as well as the company's insistence that workers provide at least one-month notice when they need to take a day off.\n\n[What's behind Starbucks price increases? 03:04](https://www.cbsnews.com/video/whats-behind-starbucks-price-increases/)\n\nWeisse isn't alone in complaining that Starbucks isn't living up to its promises to overhaul its labor practices for its roughly 130,000 baristas. That vow followed an [article last year by The New York Times](http://www.nytimes.com/interactive/2014/08/13/us/starbucks-workers-scheduling-hours.html?_r=1) that detailed how workers were struggling to manage childcare and other obligations when the company provided only a few days notice about their schedules.\n\nAbout half of roughly 200 Starbucks baristas [in a recent survey](http://static1.squarespace.com/static/556496efe4b02c9d26fdf26a/t/56027485e4b02ead4f16bc2a/1443001477305/The+Grind.pdf) said they are still receiving their schedule with less than one week's notice. Others also reported being asked to handle \"clopens,\" split shifts in which employees work a closing shift late into the evening and then an early opening shift the following morning. The company last year promised to end the practice.\n\nOf course, Starbucks isn't alone in using [\"just-in-time\" scheduling](https://www.cbsnews.com/news/in-retail-life-on-the-job-often-leads-to-a-dead-end/), with the retail and restaurant industry increasingly turning to software that allows them to change work schedules at the last minute, depending on whether business picks up or slows down. But it is Starbucks that has become a lightning rod on the issue given its vows to improve how it treats employees and its own [emphatic claims](http://www.starbucks.com/careers/working-at-starbucks) to valuing workers, whom it labels \"partners.\"\n\n\"Starbucks has the values and wants to do right by their employees,\" said Carrie Gleason, director of the Fair Workweek Initiative at the Center for Popular Democracy, an advocacy group focused on workers'rights, and a co-author of the group's new [report on the company's labor practices](http://populardemocracy.org/sites/default/files/The%2BGrind.pdf). \"However, since last year when the company recognized there was a serious problem with the way it scheduled workers and pledged to reform, still so many of the same issues persist.\"\n\nStarbucks didn't respond to requests for comment on the study or on baristas' reports of labor practices that are failing to meet the company's stated goals.\n\nIn an internal memo this week [published by Time](http://time.com/4047359/starbucks-scheduling-labor-practices-memo/), Starbucks executive Cliff Burrows wrote that the company couldn't validate the survey, but added that \"the findings suggest, contrary to the expectations we have in place, that some partners are receiving their schedules less than one week in advance and that there is a continuing issue with some partners working a close and then an opening shift the following morning.\" He asks store managers \"to go the extra mile to ensure partners have a consistent schedule.\"\n\n[Starbucks ends \"race together\" campaign amid public backlash 00:26](https://www.cbsnews.com/video/starbucks-ends-race-together-campaign-amid-public-backlash/)\n\nTo be sure, some Starbucks workers are receiving at least 10 days notice on their work hours, with the survey finding that about one-third receive two weeks notice and another 18 percent get their schedules three weeks in advance. But that leaves almost half of workers who only receive one week's notice, making it more difficult from them manage other obligations, such as school, family commitments or other jobs.\n\nClopens remain a problem, as well. About 60 percent of workers who have to handle a clopen receive seven or fewer hours of rest between a closing and an opening shift, the study found.\n\nThat's prompted one former Starbucks employee to start a petition to end the practice of scheduling clopens. Ciara Moran noted in her petition that she sometimes was only able to get [four or five hours of sleep](https://www.coworker.org/petitions/starbucks-end-clopens-now) on the days she was scheduled for clopens. She said she quit her job because she doubted whether it was possible to get ahead given the demands on workers.\n\nEven if Starbucks stuck with its policy of providing eight hours between shifts, that's not enough time, especially given that many workers in the service sector have long commutes, the study said.\n\nAnother issue singled out by the report is Starbucks' practices on sick time. Since paid time off is only available to workers with at least a year on the job, about 40 percent of employees in the survey said they had dealt with barriers in taking sick days.\n\nIn a perfect world, Weisse said he'd like to receive his schedule either a month or a quarter in advance. Asked if he's upset about the last-minute schedules, Weisse said he had become resigned to it.\n\n\"I don't know if you've ever seen experiments with rats,\" he said. \"First they get upset if you treat them badly. Then they lay down and try not to move too much. I'm like that now. Things are terrible.\"\n\nV2", - "title": "​Is Starbucks shortchanging its baristas? v2", - "url": "https://www.cbsnews.com/news/is-starbucks-shortchanging-its-baristas", - "source_domain": "cbsnews.com", - "user": "1" - }, - { - "_id": "64d18b101a4fcdeaf136613a", - "authors": ["Pablo Costa v2"], - "cloudinary_id": "reports/image.cnbcfm.com/api/v1/image/100266733-38cbdbd7a4bcc3cf98120f6574fe94a3afe0fca6.jpg?v=1529461359&w=1920&h=1080", - "date_downloaded": "2023-08-04", - "date_modified": "2023-08-07", - "date_published": "2023-08-04", - "date_submitted": "2023-08-03", - "description": null, - "editor_notes": "editor notes v2", - "embedding": null, - "epoch_date_downloaded": 1691107200, - "epoch_date_modified": 1691454221, - "epoch_date_published": 1691107200, - "epoch_date_submitted": 1691020800, - "flag": null, - "image_url": "https://image.cnbcfm.com/api/v1/image/100266733-38cbdbd7a4bcc3cf98120f6574fe94a3afe0fca6.jpg?v=1529461359&w=1920&h=1080", - "inputs_outputs": ["input 1", "output 1", "input 2", "output 2"], - "is_incident_report": true, - "language": "es", - "modifiedBy": "1", - "plain_text": "\n\nBy Aimee Picchi\n\nSeptember 24, 2015 / 11:41 AM / MoneyWatch\n\nFor Starbucks (SBUX) barista Kylei Weisse, working at the coffee chain helps him secure health insurance and some extra money while he studies at Georgia Perimeter College. What it doesn't provide is the kind of stable schedule that the company promised its workers last year.\n\n\"It's the wild inconsistency\" of the hours that's a problem, Weisse, 32, said. \"We're supposed to get them 10 days in advance, which often happens, but there's no guarantee. If our manager doesn't get it to us on time, we just have to deal with it.\"\n\nThat became a problem recently when Weisse's manager gave him only a few days notice on his work hours, which ended up conflicting with an anatomy and physiology exam at his college. Weisse ended up paying another worker $20 to take his shift so he could take the exam.\n\nThe short notice is especially frustrating because of Starbucks' vow last year to post employees' schedules at least 10 days in advance, as well as the company's insistence that workers provide at least one-month notice when they need to take a day off.\n\nWhat's behind Starbucks price increases? 03:04\n\nWeisse isn't alone in complaining that Starbucks isn't living up to its promises to overhaul its labor practices for its roughly 130,000 baristas. That vow followed an article last year by The New York Times that detailed how workers were struggling to manage childcare and other obligations when the company provided only a few days notice about their schedules.\n\nAbout half of roughly 200 Starbucks baristas in a recent survey said they are still receiving their schedule with less than one week's notice. Others also reported being asked to handle \"clopens,\" split shifts in which employees work a closing shift late into the evening and then an early opening shift the following morning. The company last year promised to end the practice.\n\nOf course, Starbucks isn't alone in using \"just-in-time\" scheduling, with the retail and restaurant industry increasingly turning to software that allows them to change work schedules at the last minute, depending on whether business picks up or slows down. But it is Starbucks that has become a lightning rod on the issue given its vows to improve how it treats employees and its own emphatic claims to valuing workers, whom it labels \"partners.\"\n\n\"Starbucks has the values and wants to do right by their employees,\" said Carrie Gleason, director of the Fair Workweek Initiative at the Center for Popular Democracy, an advocacy group focused on workers'rights, and a co-author of the group's new report on the company's labor practices. \"However, since last year when the company recognized there was a serious problem with the way it scheduled workers and pledged to reform, still so many of the same issues persist.\"\n\nStarbucks didn't respond to requests for comment on the study or on baristas' reports of labor practices that are failing to meet the company's stated goals.\n\nIn an internal memo this week published by Time, Starbucks executive Cliff Burrows wrote that the company couldn't validate the survey, but added that \"the findings suggest, contrary to the expectations we have in place, that some partners are receiving their schedules less than one week in advance and that there is a continuing issue with some partners working a close and then an opening shift the following morning.\" He asks store managers \"to go the extra mile to ensure partners have a consistent schedule.\"\n\nStarbucks ends \"race together\" campaign amid public backlash 00:26\n\nTo be sure, some Starbucks workers are receiving at least 10 days notice on their work hours, with the survey finding that about one-third receive two weeks notice and another 18 percent get their schedules three weeks in advance. But that leaves almost half of workers who only receive one week's notice, making it more difficult from them manage other obligations, such as school, family commitments or other jobs.\n\nClopens remain a problem, as well. About 60 percent of workers who have to handle a clopen receive seven or fewer hours of rest between a closing and an opening shift, the study found.\n\nThat's prompted one former Starbucks employee to start a petition to end the practice of scheduling clopens. Ciara Moran noted in her petition that she sometimes was only able to get four or five hours of sleep on the days she was scheduled for clopens. She said she quit her job because she doubted whether it was possible to get ahead given the demands on workers.\n\nEven if Starbucks stuck with its policy of providing eight hours between shifts, that's not enough time, especially given that many workers in the service sector have long commutes, the study said.\n\nAnother issue singled out by the report is Starbucks' practices on sick time. Since paid time off is only available to workers with at least a year on the job, about 40 percent of employees in the survey said they had dealt with barriers in taking sick days.\n\nIn a perfect world, Weisse said he'd like to receive his schedule either a month or a quarter in advance. Asked if he's upset about the last-minute schedules, Weisse said he had become resigned to it.\n\n\"I don't know if you've ever seen experiments with rats,\" he said. \"First they get upset if you treat them badly. Then they lay down and try not to move too much. I'm like that now. Things are terrible.\"\n\nV2\n", - "report_number": 3206, - "submitters": ["Test User v2"], - "tags": [], - "text": "[](/moneywatch)\n\nBy Aimee Picchi\n\nSeptember 24, 2015 / 11:41 AM / MoneyWatch\n\nFor Starbucks ([SBUX](http://markets.cbsnews.com/SBUX/quote/)) barista Kylei Weisse, working at the coffee chain helps him secure health insurance and some extra money while he studies at Georgia Perimeter College. What it doesn't provide is the kind of stable schedule that the [company promised its workers](https://www.cbsnews.com/news/for-some-starbucks-workers-job-leaves-bitter-taste/) last year.\n\n\"It's the wild inconsistency\" of the hours that's a problem, Weisse, 32, said. \"We're supposed to get them 10 days in advance, which often happens, but there's no guarantee. If our manager doesn't get it to us on time, we just have to deal with it.\"\n\nThat became a problem recently when Weisse's manager gave him only a few days notice on his work hours, which ended up conflicting with an anatomy and physiology exam at his college. Weisse ended up paying another worker $20 to take his shift so he could take the exam.\n\nThe short notice is especially frustrating because of Starbucks' vow last year to post employees' schedules at least 10 days in advance, as well as the company's insistence that workers provide at least one-month notice when they need to take a day off.\n\n[What's behind Starbucks price increases? 03:04](https://www.cbsnews.com/video/whats-behind-starbucks-price-increases/)\n\nWeisse isn't alone in complaining that Starbucks isn't living up to its promises to overhaul its labor practices for its roughly 130,000 baristas. That vow followed an [article last year by The New York Times](http://www.nytimes.com/interactive/2014/08/13/us/starbucks-workers-scheduling-hours.html?_r=1) that detailed how workers were struggling to manage childcare and other obligations when the company provided only a few days notice about their schedules.\n\nAbout half of roughly 200 Starbucks baristas [in a recent survey](http://static1.squarespace.com/static/556496efe4b02c9d26fdf26a/t/56027485e4b02ead4f16bc2a/1443001477305/The+Grind.pdf) said they are still receiving their schedule with less than one week's notice. Others also reported being asked to handle \"clopens,\" split shifts in which employees work a closing shift late into the evening and then an early opening shift the following morning. The company last year promised to end the practice.\n\nOf course, Starbucks isn't alone in using [\"just-in-time\" scheduling](https://www.cbsnews.com/news/in-retail-life-on-the-job-often-leads-to-a-dead-end/), with the retail and restaurant industry increasingly turning to software that allows them to change work schedules at the last minute, depending on whether business picks up or slows down. But it is Starbucks that has become a lightning rod on the issue given its vows to improve how it treats employees and its own [emphatic claims](http://www.starbucks.com/careers/working-at-starbucks) to valuing workers, whom it labels \"partners.\"\n\n\"Starbucks has the values and wants to do right by their employees,\" said Carrie Gleason, director of the Fair Workweek Initiative at the Center for Popular Democracy, an advocacy group focused on workers'rights, and a co-author of the group's new [report on the company's labor practices](http://populardemocracy.org/sites/default/files/The%2BGrind.pdf). \"However, since last year when the company recognized there was a serious problem with the way it scheduled workers and pledged to reform, still so many of the same issues persist.\"\n\nStarbucks didn't respond to requests for comment on the study or on baristas' reports of labor practices that are failing to meet the company's stated goals.\n\nIn an internal memo this week [published by Time](http://time.com/4047359/starbucks-scheduling-labor-practices-memo/), Starbucks executive Cliff Burrows wrote that the company couldn't validate the survey, but added that \"the findings suggest, contrary to the expectations we have in place, that some partners are receiving their schedules less than one week in advance and that there is a continuing issue with some partners working a close and then an opening shift the following morning.\" He asks store managers \"to go the extra mile to ensure partners have a consistent schedule.\"\n\n[Starbucks ends \"race together\" campaign amid public backlash 00:26](https://www.cbsnews.com/video/starbucks-ends-race-together-campaign-amid-public-backlash/)\n\nTo be sure, some Starbucks workers are receiving at least 10 days notice on their work hours, with the survey finding that about one-third receive two weeks notice and another 18 percent get their schedules three weeks in advance. But that leaves almost half of workers who only receive one week's notice, making it more difficult from them manage other obligations, such as school, family commitments or other jobs.\n\nClopens remain a problem, as well. About 60 percent of workers who have to handle a clopen receive seven or fewer hours of rest between a closing and an opening shift, the study found.\n\nThat's prompted one former Starbucks employee to start a petition to end the practice of scheduling clopens. Ciara Moran noted in her petition that she sometimes was only able to get [four or five hours of sleep](https://www.coworker.org/petitions/starbucks-end-clopens-now) on the days she was scheduled for clopens. She said she quit her job because she doubted whether it was possible to get ahead given the demands on workers.\n\nEven if Starbucks stuck with its policy of providing eight hours between shifts, that's not enough time, especially given that many workers in the service sector have long commutes, the study said.\n\nAnother issue singled out by the report is Starbucks' practices on sick time. Since paid time off is only available to workers with at least a year on the job, about 40 percent of employees in the survey said they had dealt with barriers in taking sick days.\n\nIn a perfect world, Weisse said he'd like to receive his schedule either a month or a quarter in advance. Asked if he's upset about the last-minute schedules, Weisse said he had become resigned to it.\n\n\"I don't know if you've ever seen experiments with rats,\" he said. \"First they get upset if you treat them badly. Then they lay down and try not to move too much. I'm like that now. Things are terrible.\"\n\nV2", - "title": "​Is Starbucks shortchanging its baristas? v2", - "url": "https://www.cbsnews.com/news/is-starbucks-shortchanging-its-baristas", - "source_domain": "cbsnews.com", - "user": "1" - }, - { - "_id": "64d17f86f549de65c4e31a29", - "authors": ["Pablo Costa v2"], - "cloudinary_id": "reports/image.cnbcfm.com/api/v1/image/100266733-38cbdbd7a4bcc3cf98120f6574fe94a3afe0fca6.jpg?v=1529461359&w=1920&h=1080", - "date_downloaded": "2023-08-04", - "date_modified": "2023-08-07", - "date_published": "2023-08-04", - "date_submitted": "2023-08-03", - "description": null, - "editor_notes": "editor notes v2", - "embedding": null, - "epoch_date_downloaded": 1691107200, - "epoch_date_modified": 1691451266, - "epoch_date_published": 1691107200, - "epoch_date_submitted": 1691020800, - "flag": null, - "image_url": "https://image.cnbcfm.com/api/v1/image/100266733-38cbdbd7a4bcc3cf98120f6574fe94a3afe0fca6.jpg?v=1529461359&w=1920&h=1080", - "inputs_outputs": null, - "is_incident_report": true, - "language": "es", - "modifiedBy": "1", - "plain_text": "\n\nBy Aimee Picchi\n\nSeptember 24, 2015 / 11:41 AM / MoneyWatch\n\nFor Starbucks (SBUX) barista Kylei Weisse, working at the coffee chain helps him secure health insurance and some extra money while he studies at Georgia Perimeter College. What it doesn't provide is the kind of stable schedule that the company promised its workers last year.\n\n\"It's the wild inconsistency\" of the hours that's a problem, Weisse, 32, said. \"We're supposed to get them 10 days in advance, which often happens, but there's no guarantee. If our manager doesn't get it to us on time, we just have to deal with it.\"\n\nThat became a problem recently when Weisse's manager gave him only a few days notice on his work hours, which ended up conflicting with an anatomy and physiology exam at his college. Weisse ended up paying another worker $20 to take his shift so he could take the exam.\n\nThe short notice is especially frustrating because of Starbucks' vow last year to post employees' schedules at least 10 days in advance, as well as the company's insistence that workers provide at least one-month notice when they need to take a day off.\n\nWhat's behind Starbucks price increases? 03:04\n\nWeisse isn't alone in complaining that Starbucks isn't living up to its promises to overhaul its labor practices for its roughly 130,000 baristas. That vow followed an article last year by The New York Times that detailed how workers were struggling to manage childcare and other obligations when the company provided only a few days notice about their schedules.\n\nAbout half of roughly 200 Starbucks baristas in a recent survey said they are still receiving their schedule with less than one week's notice. Others also reported being asked to handle \"clopens,\" split shifts in which employees work a closing shift late into the evening and then an early opening shift the following morning. The company last year promised to end the practice.\n\nOf course, Starbucks isn't alone in using \"just-in-time\" scheduling, with the retail and restaurant industry increasingly turning to software that allows them to change work schedules at the last minute, depending on whether business picks up or slows down. But it is Starbucks that has become a lightning rod on the issue given its vows to improve how it treats employees and its own emphatic claims to valuing workers, whom it labels \"partners.\"\n\n\"Starbucks has the values and wants to do right by their employees,\" said Carrie Gleason, director of the Fair Workweek Initiative at the Center for Popular Democracy, an advocacy group focused on workers'rights, and a co-author of the group's new report on the company's labor practices. \"However, since last year when the company recognized there was a serious problem with the way it scheduled workers and pledged to reform, still so many of the same issues persist.\"\n\nStarbucks didn't respond to requests for comment on the study or on baristas' reports of labor practices that are failing to meet the company's stated goals.\n\nIn an internal memo this week published by Time, Starbucks executive Cliff Burrows wrote that the company couldn't validate the survey, but added that \"the findings suggest, contrary to the expectations we have in place, that some partners are receiving their schedules less than one week in advance and that there is a continuing issue with some partners working a close and then an opening shift the following morning.\" He asks store managers \"to go the extra mile to ensure partners have a consistent schedule.\"\n\nStarbucks ends \"race together\" campaign amid public backlash 00:26\n\nTo be sure, some Starbucks workers are receiving at least 10 days notice on their work hours, with the survey finding that about one-third receive two weeks notice and another 18 percent get their schedules three weeks in advance. But that leaves almost half of workers who only receive one week's notice, making it more difficult from them manage other obligations, such as school, family commitments or other jobs.\n\nClopens remain a problem, as well. About 60 percent of workers who have to handle a clopen receive seven or fewer hours of rest between a closing and an opening shift, the study found.\n\nThat's prompted one former Starbucks employee to start a petition to end the practice of scheduling clopens. Ciara Moran noted in her petition that she sometimes was only able to get four or five hours of sleep on the days she was scheduled for clopens. She said she quit her job because she doubted whether it was possible to get ahead given the demands on workers.\n\nEven if Starbucks stuck with its policy of providing eight hours between shifts, that's not enough time, especially given that many workers in the service sector have long commutes, the study said.\n\nAnother issue singled out by the report is Starbucks' practices on sick time. Since paid time off is only available to workers with at least a year on the job, about 40 percent of employees in the survey said they had dealt with barriers in taking sick days.\n\nIn a perfect world, Weisse said he'd like to receive his schedule either a month or a quarter in advance. Asked if he's upset about the last-minute schedules, Weisse said he had become resigned to it.\n\n\"I don't know if you've ever seen experiments with rats,\" he said. \"First they get upset if you treat them badly. Then they lay down and try not to move too much. I'm like that now. Things are terrible.\"\n\nV2\n", - "report_number": 3206, - "submitters": ["Test User v2"], - "tags": [], - "text": "[](/moneywatch)\n\nBy Aimee Picchi\n\nSeptember 24, 2015 / 11:41 AM / MoneyWatch\n\nFor Starbucks ([SBUX](http://markets.cbsnews.com/SBUX/quote/)) barista Kylei Weisse, working at the coffee chain helps him secure health insurance and some extra money while he studies at Georgia Perimeter College. What it doesn't provide is the kind of stable schedule that the [company promised its workers](https://www.cbsnews.com/news/for-some-starbucks-workers-job-leaves-bitter-taste/) last year.\n\n\"It's the wild inconsistency\" of the hours that's a problem, Weisse, 32, said. \"We're supposed to get them 10 days in advance, which often happens, but there's no guarantee. If our manager doesn't get it to us on time, we just have to deal with it.\"\n\nThat became a problem recently when Weisse's manager gave him only a few days notice on his work hours, which ended up conflicting with an anatomy and physiology exam at his college. Weisse ended up paying another worker $20 to take his shift so he could take the exam.\n\nThe short notice is especially frustrating because of Starbucks' vow last year to post employees' schedules at least 10 days in advance, as well as the company's insistence that workers provide at least one-month notice when they need to take a day off.\n\n[What's behind Starbucks price increases? 03:04](https://www.cbsnews.com/video/whats-behind-starbucks-price-increases/)\n\nWeisse isn't alone in complaining that Starbucks isn't living up to its promises to overhaul its labor practices for its roughly 130,000 baristas. That vow followed an [article last year by The New York Times](http://www.nytimes.com/interactive/2014/08/13/us/starbucks-workers-scheduling-hours.html?_r=1) that detailed how workers were struggling to manage childcare and other obligations when the company provided only a few days notice about their schedules.\n\nAbout half of roughly 200 Starbucks baristas [in a recent survey](http://static1.squarespace.com/static/556496efe4b02c9d26fdf26a/t/56027485e4b02ead4f16bc2a/1443001477305/The+Grind.pdf) said they are still receiving their schedule with less than one week's notice. Others also reported being asked to handle \"clopens,\" split shifts in which employees work a closing shift late into the evening and then an early opening shift the following morning. The company last year promised to end the practice.\n\nOf course, Starbucks isn't alone in using [\"just-in-time\" scheduling](https://www.cbsnews.com/news/in-retail-life-on-the-job-often-leads-to-a-dead-end/), with the retail and restaurant industry increasingly turning to software that allows them to change work schedules at the last minute, depending on whether business picks up or slows down. But it is Starbucks that has become a lightning rod on the issue given its vows to improve how it treats employees and its own [emphatic claims](http://www.starbucks.com/careers/working-at-starbucks) to valuing workers, whom it labels \"partners.\"\n\n\"Starbucks has the values and wants to do right by their employees,\" said Carrie Gleason, director of the Fair Workweek Initiative at the Center for Popular Democracy, an advocacy group focused on workers'rights, and a co-author of the group's new [report on the company's labor practices](http://populardemocracy.org/sites/default/files/The%2BGrind.pdf). \"However, since last year when the company recognized there was a serious problem with the way it scheduled workers and pledged to reform, still so many of the same issues persist.\"\n\nStarbucks didn't respond to requests for comment on the study or on baristas' reports of labor practices that are failing to meet the company's stated goals.\n\nIn an internal memo this week [published by Time](http://time.com/4047359/starbucks-scheduling-labor-practices-memo/), Starbucks executive Cliff Burrows wrote that the company couldn't validate the survey, but added that \"the findings suggest, contrary to the expectations we have in place, that some partners are receiving their schedules less than one week in advance and that there is a continuing issue with some partners working a close and then an opening shift the following morning.\" He asks store managers \"to go the extra mile to ensure partners have a consistent schedule.\"\n\n[Starbucks ends \"race together\" campaign amid public backlash 00:26](https://www.cbsnews.com/video/starbucks-ends-race-together-campaign-amid-public-backlash/)\n\nTo be sure, some Starbucks workers are receiving at least 10 days notice on their work hours, with the survey finding that about one-third receive two weeks notice and another 18 percent get their schedules three weeks in advance. But that leaves almost half of workers who only receive one week's notice, making it more difficult from them manage other obligations, such as school, family commitments or other jobs.\n\nClopens remain a problem, as well. About 60 percent of workers who have to handle a clopen receive seven or fewer hours of rest between a closing and an opening shift, the study found.\n\nThat's prompted one former Starbucks employee to start a petition to end the practice of scheduling clopens. Ciara Moran noted in her petition that she sometimes was only able to get [four or five hours of sleep](https://www.coworker.org/petitions/starbucks-end-clopens-now) on the days she was scheduled for clopens. She said she quit her job because she doubted whether it was possible to get ahead given the demands on workers.\n\nEven if Starbucks stuck with its policy of providing eight hours between shifts, that's not enough time, especially given that many workers in the service sector have long commutes, the study said.\n\nAnother issue singled out by the report is Starbucks' practices on sick time. Since paid time off is only available to workers with at least a year on the job, about 40 percent of employees in the survey said they had dealt with barriers in taking sick days.\n\nIn a perfect world, Weisse said he'd like to receive his schedule either a month or a quarter in advance. Asked if he's upset about the last-minute schedules, Weisse said he had become resigned to it.\n\n\"I don't know if you've ever seen experiments with rats,\" he said. \"First they get upset if you treat them badly. Then they lay down and try not to move too much. I'm like that now. Things are terrible.\"\n\nV2", - "title": "​Is Starbucks shortchanging its baristas? v2", - "url": "https://www.cbsnews.com/news/is-starbucks-shortchanging-its-baristas", - "source_domain": "cbsnews.com", - "user": "1" - }, - { - "_id": "64cc41211c1f73dbc02ab00c", - "authors": ["Pablo Costa"], - "cloudinary_id": "reports/assets3.cbsnewsstatic.com/hub/i/r/2015/03/17/01a38576-5108-40f7-8df8-5416164ed878/thumbnail/1200x630/ca8d35fe6bc065b5c9a747d92bc6d94c/154211248.jpg?v=523df724471d66f84598615fede8d33f", - "date_downloaded": "2023-08-03", - "date_modified": "2023-08-03", - "date_published": "2023-08-03", - "date_submitted": "2023-08-03", - "description": null, - "editor_notes": null, - "embedding": { - "__typename": "History_reportEmbedding", - "from_text_hash": "0cf0b5d5d39e3e9040ce8da9367364511c3e9532", - "vector": [ - -0.09017669409513474, 0.049546271562576294, 0.038945719599723816, -0.1436443030834198 - ] - }, - "epoch_date_downloaded": 1691020800, - "epoch_date_modified": 1691107596, - "epoch_date_published": 1691020800, - "epoch_date_submitted": 1691020800, - "flag": null, - "image_url": "https://assets3.cbsnewsstatic.com/hub/i/r/2015/03/17/01a38576-5108-40f7-8df8-5416164ed878/thumbnail/1200x630/ca8d35fe6bc065b5c9a747d92bc6d94c/154211248.jpg?v=523df724471d66f84598615fede8d33f", - "inputs_outputs": null, - "is_incident_report": true, - "language": "en", - "modifiedBy": "1", - "plain_text": "\n\nBy Aimee Picchi\n\nSeptember 24, 2015 / 11:41 AM / MoneyWatch\n\nFor Starbucks (SBUX) barista Kylei Weisse, working at the coffee chain helps him secure health insurance and some extra money while he studies at Georgia Perimeter College. What it doesn't provide is the kind of stable schedule that the company promised its workers last year.\n\n\"It's the wild inconsistency\" of the hours that's a problem, Weisse, 32, said. \"We're supposed to get them 10 days in advance, which often happens, but there's no guarantee. If our manager doesn't get it to us on time, we just have to deal with it.\"\n\nThat became a problem recently when Weisse's manager gave him only a few days notice on his work hours, which ended up conflicting with an anatomy and physiology exam at his college. Weisse ended up paying another worker $20 to take his shift so he could take the exam.\n\nThe short notice is especially frustrating because of Starbucks' vow last year to post employees' schedules at least 10 days in advance, as well as the company's insistence that workers provide at least one-month notice when they need to take a day off.\n\nWhat's behind Starbucks price increases? 03:04\n\nWeisse isn't alone in complaining that Starbucks isn't living up to its promises to overhaul its labor practices for its roughly 130,000 baristas. That vow followed an article last year by The New York Times that detailed how workers were struggling to manage childcare and other obligations when the company provided only a few days notice about their schedules.\n\nAbout half of roughly 200 Starbucks baristas in a recent survey said they are still receiving their schedule with less than one week's notice. Others also reported being asked to handle \"clopens,\" split shifts in which employees work a closing shift late into the evening and then an early opening shift the following morning. The company last year promised to end the practice.\n\nOf course, Starbucks isn't alone in using \"just-in-time\" scheduling, with the retail and restaurant industry increasingly turning to software that allows them to change work schedules at the last minute, depending on whether business picks up or slows down. But it is Starbucks that has become a lightning rod on the issue given its vows to improve how it treats employees and its own emphatic claims to valuing workers, whom it labels \"partners.\"\n\n\"Starbucks has the values and wants to do right by their employees,\" said Carrie Gleason, director of the Fair Workweek Initiative at the Center for Popular Democracy, an advocacy group focused on workers'rights, and a co-author of the group's new report on the company's labor practices. \"However, since last year when the company recognized there was a serious problem with the way it scheduled workers and pledged to reform, still so many of the same issues persist.\"\n\nStarbucks didn't respond to requests for comment on the study or on baristas' reports of labor practices that are failing to meet the company's stated goals.\n\nIn an internal memo this week published by Time, Starbucks executive Cliff Burrows wrote that the company couldn't validate the survey, but added that \"the findings suggest, contrary to the expectations we have in place, that some partners are receiving their schedules less than one week in advance and that there is a continuing issue with some partners working a close and then an opening shift the following morning.\" He asks store managers \"to go the extra mile to ensure partners have a consistent schedule.\"\n\nStarbucks ends \"race together\" campaign amid public backlash 00:26\n\nTo be sure, some Starbucks workers are receiving at least 10 days notice on their work hours, with the survey finding that about one-third receive two weeks notice and another 18 percent get their schedules three weeks in advance. But that leaves almost half of workers who only receive one week's notice, making it more difficult from them manage other obligations, such as school, family commitments or other jobs.\n\nClopens remain a problem, as well. About 60 percent of workers who have to handle a clopen receive seven or fewer hours of rest between a closing and an opening shift, the study found.\n\nThat's prompted one former Starbucks employee to start a petition to end the practice of scheduling clopens. Ciara Moran noted in her petition that she sometimes was only able to get four or five hours of sleep on the days she was scheduled for clopens. She said she quit her job because she doubted whether it was possible to get ahead given the demands on workers.\n\nEven if Starbucks stuck with its policy of providing eight hours between shifts, that's not enough time, especially given that many workers in the service sector have long commutes, the study said.\n\nAnother issue singled out by the report is Starbucks' practices on sick time. Since paid time off is only available to workers with at least a year on the job, about 40 percent of employees in the survey said they had dealt with barriers in taking sick days.\n\nIn a perfect world, Weisse said he'd like to receive his schedule either a month or a quarter in advance. Asked if he's upset about the last-minute schedules, Weisse said he had become resigned to it.\n\n\"I don't know if you've ever seen experiments with rats,\" he said. \"First they get upset if you treat them badly. Then they lay down and try not to move too much. I'm like that now. Things are terrible.\"\n", - "report_number": 3206, - "submitters": ["Test User"], - "tags": [], - "text": "[](/moneywatch)\n\nBy Aimee Picchi\n\nSeptember 24, 2015 / 11:41 AM / MoneyWatch\n\nFor Starbucks ([SBUX](http://markets.cbsnews.com/SBUX/quote/)) barista Kylei Weisse, working at the coffee chain helps him secure health insurance and some extra money while he studies at Georgia Perimeter College. What it doesn't provide is the kind of stable schedule that the [company promised its workers](https://www.cbsnews.com/news/for-some-starbucks-workers-job-leaves-bitter-taste/) last year.\n\n\"It's the wild inconsistency\" of the hours that's a problem, Weisse, 32, said. \"We're supposed to get them 10 days in advance, which often happens, but there's no guarantee. If our manager doesn't get it to us on time, we just have to deal with it.\"\n\nThat became a problem recently when Weisse's manager gave him only a few days notice on his work hours, which ended up conflicting with an anatomy and physiology exam at his college. Weisse ended up paying another worker $20 to take his shift so he could take the exam.\n\nThe short notice is especially frustrating because of Starbucks' vow last year to post employees' schedules at least 10 days in advance, as well as the company's insistence that workers provide at least one-month notice when they need to take a day off.\n\n[What's behind Starbucks price increases? 03:04](https://www.cbsnews.com/video/whats-behind-starbucks-price-increases/)\n\nWeisse isn't alone in complaining that Starbucks isn't living up to its promises to overhaul its labor practices for its roughly 130,000 baristas. That vow followed an [article last year by The New York Times](http://www.nytimes.com/interactive/2014/08/13/us/starbucks-workers-scheduling-hours.html?_r=1) that detailed how workers were struggling to manage childcare and other obligations when the company provided only a few days notice about their schedules.\n\nAbout half of roughly 200 Starbucks baristas [in a recent survey](http://static1.squarespace.com/static/556496efe4b02c9d26fdf26a/t/56027485e4b02ead4f16bc2a/1443001477305/The+Grind.pdf) said they are still receiving their schedule with less than one week's notice. Others also reported being asked to handle \"clopens,\" split shifts in which employees work a closing shift late into the evening and then an early opening shift the following morning. The company last year promised to end the practice.\n\nOf course, Starbucks isn't alone in using [\"just-in-time\" scheduling](https://www.cbsnews.com/news/in-retail-life-on-the-job-often-leads-to-a-dead-end/), with the retail and restaurant industry increasingly turning to software that allows them to change work schedules at the last minute, depending on whether business picks up or slows down. But it is Starbucks that has become a lightning rod on the issue given its vows to improve how it treats employees and its own [emphatic claims](http://www.starbucks.com/careers/working-at-starbucks) to valuing workers, whom it labels \"partners.\"\n\n\"Starbucks has the values and wants to do right by their employees,\" said Carrie Gleason, director of the Fair Workweek Initiative at the Center for Popular Democracy, an advocacy group focused on workers'rights, and a co-author of the group's new [report on the company's labor practices](http://populardemocracy.org/sites/default/files/The%2BGrind.pdf). \"However, since last year when the company recognized there was a serious problem with the way it scheduled workers and pledged to reform, still so many of the same issues persist.\"\n\nStarbucks didn't respond to requests for comment on the study or on baristas' reports of labor practices that are failing to meet the company's stated goals.\n\nIn an internal memo this week [published by Time](http://time.com/4047359/starbucks-scheduling-labor-practices-memo/), Starbucks executive Cliff Burrows wrote that the company couldn't validate the survey, but added that \"the findings suggest, contrary to the expectations we have in place, that some partners are receiving their schedules less than one week in advance and that there is a continuing issue with some partners working a close and then an opening shift the following morning.\" He asks store managers \"to go the extra mile to ensure partners have a consistent schedule.\"\n\n[Starbucks ends \"race together\" campaign amid public backlash 00:26](https://www.cbsnews.com/video/starbucks-ends-race-together-campaign-amid-public-backlash/)\n\nTo be sure, some Starbucks workers are receiving at least 10 days notice on their work hours, with the survey finding that about one-third receive two weeks notice and another 18 percent get their schedules three weeks in advance. But that leaves almost half of workers who only receive one week's notice, making it more difficult from them manage other obligations, such as school, family commitments or other jobs.\n\nClopens remain a problem, as well. About 60 percent of workers who have to handle a clopen receive seven or fewer hours of rest between a closing and an opening shift, the study found.\n\nThat's prompted one former Starbucks employee to start a petition to end the practice of scheduling clopens. Ciara Moran noted in her petition that she sometimes was only able to get [four or five hours of sleep](https://www.coworker.org/petitions/starbucks-end-clopens-now) on the days she was scheduled for clopens. She said she quit her job because she doubted whether it was possible to get ahead given the demands on workers.\n\nEven if Starbucks stuck with its policy of providing eight hours between shifts, that's not enough time, especially given that many workers in the service sector have long commutes, the study said.\n\nAnother issue singled out by the report is Starbucks' practices on sick time. Since paid time off is only available to workers with at least a year on the job, about 40 percent of employees in the survey said they had dealt with barriers in taking sick days.\n\nIn a perfect world, Weisse said he'd like to receive his schedule either a month or a quarter in advance. Asked if he's upset about the last-minute schedules, Weisse said he had become resigned to it.\n\n\"I don't know if you've ever seen experiments with rats,\" he said. \"First they get upset if you treat them badly. Then they lay down and try not to move too much. I'm like that now. Things are terrible.\"", - "title": "​Is Starbucks shortchanging its baristas?", - "url": "https://www.cbsnews.com/news/is-starbucks-shortchanging-its-baristas", - "source_domain": "cbsnews.com", - "user": "1" - } - ] - } -} diff --git a/site/gatsby-site/playwright/fixtures/history/versionReports.json b/site/gatsby-site/playwright/fixtures/history/versionReports.json deleted file mode 100644 index 32540363a4..0000000000 --- a/site/gatsby-site/playwright/fixtures/history/versionReports.json +++ /dev/null @@ -1,44 +0,0 @@ -{ - "data": { - "reports": [ - { - "__typename": "Report", - "_id": "5d34b8c29ced494f010ed472", - "authors": ["Caroline O'Donovan"], - "cloudinary_id": "reports/img.buzzfeed.com/buzzfeed-static/static/2015-06/2/17/campaign_images/webdr02/kronos-makes-changes-to-software-accused-of-produ-2-14421-1433279634-2_dblbig.jpg", - "date_published": "2015-06-03", - "description": "Kronos, a workforce management company, thinks giving managers more data can make scheduling practices more fair. But ultimately, the underpinnings of the problem may go deeper than the algorit", - "epoch_date_submitted": 1559347200, - "image_url": "https://img.buzzfeed.com/buzzfeed-static/static/2015-06/2/17/campaign_images/webdr02/kronos-makes-changes-to-software-accused-of-produ-2-14421-1433279634-2_dblbig.jpg", - "inputs_outputs": null, - "language": "en", - "report_number": 3205, - "source_domain": "buzzfeednews.com", - "submitters": ["Catherine Olsson"], - "tags": [], - "text": "In April, the New York attorney general's office launched an [investigation](http://www.reuters.com/article/2015/04/13/us-retail-workers-nyag-idUSKBN0N40G420150413) into the scheduling practices of 13 national retail chains, distributing a letter to the Gap, Target, J.C. Penney, and 10 other companies. The letter asked, among other things, whether these companies' store managers use software manufactured by a company called Kronos to algorithmically generate schedules.\n\nA few months later, Kronos was also featured prominently in an [article](http://www.nytimes.com/interactive/2014/08/13/us/starbucks-workers-scheduling-hours.html) published by the _New York Times_ about the ill effects of erratic scheduling on Starbucks employees, especially one particular family. In a [follow-up piece](http://www.nytimes.com/times-insider/2014/08/22/times-article-changes-a-policy-fast/), the author, Jodi Kantor, points directly to Kronos' scheduling software as the root of the problem. \"I saw that her life was coming apart and that the Starbucks software had contributed to the crisis,\" Kantor wrote of one of the story's subjects.\n\nThe piece's argument centered around the financial and scheduling unpredictability engendered by platforms like Kronos. When you don't know if your shift might be canceled, if or when you'll be called in, or what your hours will look like next week or the week after, it becomes [very difficult](http://www.buzzfeed.com/sapna/victorias-secret-keeps-workers-on-call-and-unpaid#.wspVMPngB) to make even the most\n\nbasic plans for your future. This can have devastating long-term financial and emotional impacts on workers. According to a [recent study](http://www.epi.org/publication/irregular-work-scheduling-and-its-consequences/) by the Economic Policy Institute, a left-leaning think tank in Washington, D.C., 17 percent of the American workforce is negatively affected by unstable schedules.\n\nFor their part, Kronos representatives argue that the algorithm is far from the root of the problem. \"The populist view is that scheduling is evil, in that it's causing erratic schedules for employees, and so forth,\" Charlie DeWitt, vice president of business development for Kronos, told BuzzFeed News. \"The fact of the matter is it's an algorithm. It does whatever you want it to do.\"\n\nAnd you don't necessarily need to work for Kronos to believe that in a competitive retail climate, the problem is more complicated than technology alone. [Lonnie Golden](http://www.abington.psu.edu/academics/faculty/dr-lonnie-golden), a Penn State economist who has extensively studied the impact of erratic scheduling, acknowledges that Kronos' product itself is less to blame than the managers who make staffing decisions based on the data it provides. \"It's not necessarily the technology that's responsible for minimum to no advance notice,\" he said. \"It's the way in which it's applied.\"\n\nBut, he added, \"where there's a technology problem, there's usually a technology solution.\" And while Kronos maintains that managers, and not the software, are responsible for early dismissals and last-minute shift cancellations, the company is nonetheless pursuing some technological solutions.\n\nKronos wants to help managers better understand how scheduling adjustments affect workers and, ultimately, the bottom line. Though the company maintains that its software doesn't produce the kind of erratic schedules that hurt wage workers, DeWitt said there was nonetheless an interest in figuring out why that perception existed — and, if possible, fixing it.\n\nTo that end, earlier this month at a [retail conference in Philadelphia](http://www.kronos.com/microsites/RetailExecSummitSpring15/), the company announced that it's working on a new plug-in that will give managers better insight into workers' schedule stability, equity of hours worked among employees, and the consistency of schedules from week to week. In addition, Kronos is improving a feature meant to help give employees more control over their schedules: Though the software already incorporates employee availability and preferences into its scheduling calculations, improvements to a shift-swapping feature on its employee-facing web and mobile apps will theoretically allow employees to work around conflicts among themselves.\n\nGolden said increased employee input and control would be a good thing. But some retailers, DeWitt pointed out, are uncomfortable making workers use an app outside of work hours; indeed, the practice could be seen as a shift of management responsibilities onto lower-paid individuals.\n\nPart of the idea behind the new Kronos plug-in is to help companies tie fairer scheduling practices to reduction in absenteeism and turnover, which can be enormously costly. In other words, if Kronos can help executives see the connection between treating workers fairly and a store's ability to increase revenue, DeWitt said, managers will have an impetus to create more predictable, stable schedules.\n\nAnd just because companies are looking at this kind of data doesn't mean they have to use it. \"Companies like Kronos and Workplace Systems are starting to integrate some of these principles into their software,\" said [Carrie Gleason](http://populardemocracy.org/carrie-gleason), director of the Fair Workweek Initiative at the Center for Popular Democracy, \"but it's all optional, so companies can decide not to do it.\" While [12 states](http://populardemocracy.org/campaign/restoring-fair-workweek) are currently considering legislation that would create new labor standards around the workweek, Gleason said the technology alone lacks a mechanism for enforcement.\n\nGiven market pressures and standard management practices, it's unlikely that any change to Kronos' technology would give workers more power — especially because, given the [competitive retail climate](http://www.buzzfeed.com/sapna/retail-winter-of-death#.krGlE3qDm) at the moment, the bottom line tends to be the priority. \"It's not just bad managers. They have extreme pressure to increase productivity on an ever-shrinking labor budget,\" Gleason said.\n\nWith these changes, Kronos has taken logical steps toward both repairing its reputation and making sure its software creates sustainable work environments. But while the company cannot control exactly how the algorithm that forecasts schedules and optimizes workforces is deployed inside different workplaces, the Kronos engineers who designed the product are nonetheless the partial architects of work environments that have been proven to be untenable for low-wage workers. The Kronos scheduling algorithm isn't designed to serve those people; it's designed to be sold to their bosses, and as such, will ultimately be shaped to serve the needs of management — until regulations exist that compel them to change how it's used.", - "title": "Report 3205 title", - "url": "https://www.buzzfeednews.com/article/carolineodonovan/kronos-makes-changes-to-software-accused-of-producing-bad-sc" - }, - { - "__typename": "Report", - "_id": "5d34b8c29ced494f010ed471", - "authors": ["Alain Sherter"], - "cloudinary_id": "reports/cbsnews2.cbsistatic.com/hub/i/r/2014/09/25/aa2708c1-7e84-4573-8f79-add177a690cd/thumbnail/1200x630/2505bd91ab74f8e2ce3e7c6f8a5d77f3/starbucks.jpg", - "date_published": "2014-09-26", - "description": "Baristas at the coffee chain worry the company won't fulfill its promise to improve working conditions", - "epoch_date_submitted": 1559347200, - "image_url": "https://cbsnews2.cbsistatic.com/hub/i/r/2014/09/25/aa2708c1-7e84-4573-8f79-add177a690cd/thumbnail/1200x630/2505bd91ab74f8e2ce3e7c6f8a5d77f3/starbucks.jpg", - "inputs_outputs": null, - "language": "en", - "report_number": 3206, - "source_domain": "cbsnews.com", - "submitters": ["Catherine Olsson"], - "tags": [], - "text": "Liberte Locke, a 32-year-old \"barista\" at a Starbucks (SBUX) in New York City, is fed up.\n\n\"Starbucks' attitude is that there's always someone else who can do the job,\" she said in running through her complaints about life at the java giant.\n\nIf that isn't necessarily the consensus among Starbucks workers, interviews with nine current and former baristas at the company make clear it's not an isolated opinion, either. Even those who say they like their job paint a picture of a business that underpays front-line workers, enforces work rules arbitrarily, and too often fails to strike a balance between corporate goals and employee needs.\n\nOf course, such complaints are nothing new in retail, where low pay and erratic schedules are the norm. But by its own account, Starbucks is no ordinary company and is ostensibly a far cry from the fast-food outlets now facing a nationwide uprising by employees tired of working for peanuts.\n\nThat's evident in the company's recruitment pitch. Starbucks invites job-seekers to \"become a part of something bigger and inspire positive change in the world,\" describing it as a chance to discover a \"deep sense of purpose.\"\n\nDamage control\n\nThat image suffered a serious blow last month after The New York Times vividly chronicled a Starbucks worker struggling with the company's scheduling practices. The story, which centered on a 22-year-old barista and single mother, amounted to a public relations nightmare for Starbucks. Perhaps not coincidentally, within days of the story's publication top executives were promising reform.\n\nIn a memo to employees earlier this month, for instance, Chief Operating Officer Troy Alstead vowed to \"transform the U.S. partner experience,\" referring to Starbucks' more than 130,000 baristas. Inviting worker feedback, he said Starbucks will examine its approach to employee pay, revisit its dress code, make it easier for people to ask for time off, and consider other changes aimed at helping baristas balance work and their personal lives.\n\nAmong other changes, the company said it would end the practice of \"clopening,\" when an employee responsible for closing a store late at night is also assigned to open it early in the morning.\n\n\"We recognize that we can do more for our partners who wear the apron every day,\" he wrote.\n\nSome baristas did not feel this August memo from Starbucks went far enough in proposing ways to improve work conditions, so they marked it up with their own ideas. CBS\n\nAlthough Starbucks workers welcome this pledge to respect the apron, they fear the company is more intent on dousing the PR flames than on genuinely improving employees' experience. After the retailer last month sent an email to workers outlining possible solutions to the kind of scheduling problems and related issues detailed by the Times, a group of baristas gave the proposal a C- and posted online a marked-up version of the memo listing their own demands (see image above).\n\n\"We hope you're ready for a commitment to give us schedules that don't mess with taking care of kids, going to school or holding onto that second job we need because Sbux wages don't make ends meet,\" wrote the baristas, who are working with a union-backed labor group, the Center for Popular Democracy.\n\nRetail jungle\n\nDespite the recent media focus on Starbucks, the company's labor practices are generally no worse than those of many large retailers. In some ways they're better, with the company offering health care to part-time, as well as full-time, workers; unusually generous 401(k) matching contributions; annual stock grants to employees; and tuition reimbursement.\n\nStarbucks highlights such benefits as an example of its commitment to employees. \"Sharing success with one another has been core to the company's heritage for more than 40 years,\" Alstead said in the September memo.\n\nMeanwhile, some baristas say they enjoy their work and feel valued by Starbucks. \"It's a decent place to work, and my manager and co-workers are great,\" said one employee who asked not to be identified.\n\nBut other current and former workers claim Starbucks has changed in recent years, saying that corporate leaders' intense focus on slashing costs has short-circuited its professed commitment to workers. Mostly, they say Starbucks doesn't listen to employees and even punishes those who identify problems.\n\n\"The biggest problem is that baristas don't have a voice,\" said Sarah Madden, a former Starbucks barista who left the company this spring after two years with the coffee vendor. \"They can't speak to issues that they know exist. Workers know how to fix them, but when [they] speak up there are serious repercussions -- your hours get cut, you're transferred to another store or isolated from other people.\"\n\nEmployees interviewed for this article said one result of Starbucks' cost-containment push is that stores are frequently understaffed, hurting customer service and forcing managers to scramble to find staff. That problem is common across the big-box s", - "title": "Report 3206 title", - "url": "https://www.cbsnews.com/news/for-some-starbucks-workers-job-leaves-bitter-taste/" - } - ] - } -} diff --git a/site/gatsby-site/playwright/fixtures/incidents/fullIncident10.json b/site/gatsby-site/playwright/fixtures/incidents/fullIncident10.json deleted file mode 100644 index 89638145c7..0000000000 --- a/site/gatsby-site/playwright/fixtures/incidents/fullIncident10.json +++ /dev/null @@ -1,582 +0,0 @@ -{ - "data": { - "incident": { - "AllegedDeployerOfAISystem": [ - { - "entity_id": "boeing", - "name": "Boeing" - } - ], - "AllegedDeveloperOfAISystem": [ - { - "entity_id": "boeing", - "name": "Boeing" - } - ], - "AllegedHarmedOrNearlyHarmedParties": [ - { - "entity_id": "airplane-passengers", - "name": "Airplane Passengers" - }, - { - "entity_id": "airplane-crew", - "name": "Airplane Crew" - } - ], - "date": "2018-10-27", - "description": "A Boeing 737 crashed into the sea, killing 189 people, after faulty sensor data caused an automated manuevering system to repeatedly push the plane's nose downward.", - 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Perez"], - "cloudinary_id": "reports/placeholder.svg", - "date_published": "2019-03-14", - "description": "Present day Air travel is one of the safest modes of travel. Statistics from the US Department of Transportation show that in 2007 and 2016 there were 11 fatalities per trillion miles of commercial ai", - "epoch_date_submitted": 1607299200, - "image_url": "placeholder.svg", - "inputs_outputs": null, - "language": "en", - "report_number": 1342, - "source_domain": "medium.com", - "submitters": ["Ingrid Dickinson (CSET)"], - "tags": [], - "text": "Present day Air travel is one of the safest modes of travel. Statistics from the US Department of Transportation show that in 2007 and 2016 there were 11 fatalities per trillion miles of commercial air travel. This is in stark contrasts to the 7,864 fatalities per trillion miles of travel on the highway ( You can check the statistics here: fatalities and miles of travel per mode of transport). Incremental improvements to air travel is a marvel of technical innovation. However, when an aircraft accident does occur, we are forced to take notice due to the magnitude of a single event.\n\nAir travel today is at a level of technical maturity that when a plane crashes by accident (i.e not due to man-made causes like terrorism or misfiring of missiles), then it is surprisingly due not to pilot error or physical equipment failure but rather because of a computer error. That is, an aircraft accident is caused by a software bug.\n\nEveryone today is intimately familiar with software bugs. Microsoft blue screen of death and the use of ctrl-alt-delete have been burned into our experiences. Even in better designed operating systems that we find in smartphones, it’s is not uncommon to force a reboot. This is much less common that we often have to look up the procedure, but it does happen nevertheless.\n\nSoftware is notoriously difficult to make bug free. It is the nature of the beast. This is because, to build bug-free software systems, we need to explicitly list all the scenarios that can go wrong and how, and then test our software for those conditions. Unfortunately, that list tends to be unbounded if our designs don’t restrict the scope of a software’s applicability. In short, software developers are able to manage the unbounded complexity by narrowing the scope of applicability. That is why even the most sophisticated “artificial intelligent” applications work well in the most narrow of areas. It is very easy to get frustrated by the limitations of voice assistants like Alexa. That’s because AI technology has not reached the level of maturity that is required for open-ended general conversation. In short, bug-free depends fundamentally on a narrow scope of application and extensive testing within this narrow scope.\n\nAs we build more sophisticated software that has higher degrees of complexity, we need to understand the scope of an application and an ever-increasing scope demands more on the testing of these systems. Thus to understand this complexity better, we need to understand the kinds of automation we are building.\n\nAs I mentioned earlier, the USDOT shows that there were over 37,000 fatalities in highway accidents in 2017 alone. Thus it makes logical sense to understand how automation affects the safety of road vehicles. The Society of Automation Engineering (SAE) has an international standard which defines six levels of driving automation (SAE J3016). This is a useful framework for classifying the levels of automation in domains outside that of cars. A broader prescription is as follows:\n\nLevel 0 (Manual Process)\n\nThe absence of any automation.\n\nLevel 1 (Attended Process)\n\nUsers are aware of the initiation and completion of the performance of each automated task. The user may undo a task in the event of incorrect execution. Users, however, are responsible for the correct sequencing of tasks.\n\nLevel 2 (Attended Multiple Processes)\n\nUsers are aware of the initiation and completion of a composite of tasks. The user, however, is not responsible for the correct sequencing of tasks. An example will be the booking of a hotel, car, and flight. The exact ordering of the booking may not be a concern of the user. However, failure of the performance of this task may require more extensive manual remedial actions. An unfortunate example of a failed remedial action is the re-accommodation of United Airlines’ paying customer.\n\nLevel 3 (Unattended Process)\n\nUsers are only notified in exceptional situations and are required to do the work in these conditions. An example of this is in systems that continuously monitor the security of a network. Practitioners take action depending on the severity of an event.\n\nLevel 4 (Intelligent Process)\n\nUsers are responsible for defining the end goals of automation, however, all aspects of the process execution, as well as the handling of in-flight exceptional conditions, are handled by the automation. The automation is capable of performing appropriate compensating action in events of in-flight failure. The user however is still responsible for identifying the specific context in which automation can be safely applied to.\n\nLevel 5 (Fully Automated Process)\n\nThis is a final and future state where human involvement is no longer required in the processes. This, of course, may not be the final level because it does not assume that the process is capable of optimizing itself to make improvements.\n\nLevel 6 (Self Optimizing Process)\n\nThis is automation that requires no human involvement and is also capable of improving itself over time. This level goes beyond the SAE requirements but may be required in certain high-performance competitive environments such as Robocar races and stock trading.\n\nThe automobiles of today have extremely sophisticated software that controls many parts of the functioning of the system. This software works at many levels and at each level the risks are different. Some software works at an extremely narrow scope that we are unaware that it is operating. So for example, a car’s fuel injection system is, in fact, fully automated. We can say this about many of the functions of a car that deals with its engine performance. So for example, many car enthusiasts buy programmers and chips that provide after-market tweaks on a car’s performance characteristics. Failure of any of these kinds of systems can still be fatal. SAE’s standards described above however apply to driving automation and not engine automation. There is a stark difference in automation that affect steering and automation that maintains the smooth running of engines.\n\nAutomation such as traction control or car stabilization does affect steering. These are engaged in exceptional narrow conditions to ensure greater passenger safety. Controlled behavior is injected in a situation so that a driver can gain better control of a vehicle that he otherwise could not have done so himself. In this context, a driver is actually momentarily not controlling the car.\n\nThere have been many cases of planes falling from the skies due to software bugs. My earliest memory of this kind of a catastrophe is Lauda Air Flight 004 in May 1991. This is when one of the engines reverse trusters engaged in mid-flight forcing the plane to spiral out of control and crash. There was no official conclusion as to the cause, however, the aviation writer Macarthur Job said that “had that Boeing 767 been of an earlier version of the type, fitted with engines that were controlled mechanically rather than electronically, then that accident could not have happened.”\n\nMore recently, there is the case of Air France 447 in 2009. The official conclusion was that there was a “temporary inconsistency between the measured speeds, likely as a result of the obstruction of the pitot tubes by ice crystals, causing autopilot disconnection and reconfiguration.” The verdict was that the human pilots were eventually part of the fault due to their inability to react appropriately to the anomalous situation. To say this differently, the pilots received incorrect information from the instrumentation and thus took inappropriate action to stabilize the plane.\n\nThere are other cases of computer caused failures. Qantas flight 72, it was determined that the CPU of the air data inertial reference unit (ADIRU) corrupted the angle of attack (AOA) data. Malaysia Air 124 that plunged 200 feet in midflight. The instrumentation displayed that the plane was “going too fast and too slow simultaneously”.\n\nIn general, it is the responsibility for the pilots to properly perform compensating actions in the case of equipment failure (known as alternate law). The point though is that computer error due to equipment failure should be no different from regular equipment error and it is the responsibility of the pilots to take appropriate measures. Typically, on equipment error, the autopilot is disengaged and the plane is to be flown manually. This is Level 3 (unattended process) automation where the scope when automation is in play is explicit. In Level 3, a pilot is made aware of an exceptional condition and takes manual control of the plane.\n\nIn Level 4 (intelligent process), a pilot must be able to recognize the exceptional condition and is able to specify when automation is applicable. Today, we have self-driving cars that are deployed in narrow applications. We have cars that can self-park and we have cars that can drive in good weather conditions on the highway. These are Level 4 automation where is up to the judgment of the driver to engage the automation. Autopilot in planes are Level 4 automation and is engaged in contexts of low complexity.\n\nThen there is the case of Boeing’s 737 Max 8’s MCAS. This I will argue is a Level 5 automation, this is a fully automated process wherein it is expected to function in all scenarios. Like electronics that control engine performance, fully automated processes aren’t generally problematic, however, when you involve driving (or steering for planes) then it opens up the question of the maturity of this level of automation.\n\nAirbus has what is called ‘Alpha Protection’:\n\n“Alpha protection” software is built into every Airbus aircraft to prevent the aircraft from exceeding its performance limits in angle of attack, and kicks in automatically when those limits are reached.\n\nFrom the definition, Alpha protection is automation that is always measuring, however, it isn’t always active. It is like a speed limiter that exits in cars today, it is constantly measuring, but is activated only when measurements exceed thresholds. However, what happens when the measurements are incorrect due to faulty sensors? One could argue that this might have been what happened to Air France 447. That is, the automation became active when the pilots did not expect it. Faulty sensors are always problematic, but faulty sensors that can trigger automated behavior can be extremely dangerous.\n\nThe Boeing 737 Max 8 has a system known as Maneuvering Characteristics Augmentation System (MCAS). The business motivation behind MCAS is itself quite revealing. Apparently, it is analogous to a software patch that attempts to fix a physical flaw of the aircraft. The Boeing 737 aircraft, introduced in 1968, is an extremely mature and reliable aircraft. The 737 is the best selling aircraft in the world, selling over 10,000 aircraft since its inception. It is has been favored by many short-haul budget airlines that have risen in the past decade. Its main competitor in the Airbus A320, where over 8,000 planes have been delivered since its inception in 1988.\n\nIn 2008, a joint American-French company CFM launched a more fuel and cost-efficient engine known as the Leap engine. Airbus fitted their new planes (Airbus A320neo) with this new engine. The reason behind the economy of the Leap engine is due to its much larger air intake diameter.\n\nTo be competitive, the Max 8, was retrofitted also with this new engine. However, unlike the A320neo, there was not enough ground clearance for the Leap engine. To compensate for this problem, Boeing reduced the distance between the engine and the underside of the wing. This, however, had the effect of changing the center of mass of the plane. The Max 8 now had the dynamic tendency of raising its nose and as a consequence increasing the risk of a stall.\n\nTo paper over this tendency, Boeing developed MCAS. The purpose of MCAS is that it is software dedicated to compensating for this flaw:\n\nBoeing engineers, in turn, came up with another makeshift solution. They developed a software that would work in the background. As soon as the nose of the aircraft pointed upward too steeply, the system would automatically activate the tailplane and bring the aircraft back to a safe cruising plane. The pilots wouldn’t even notice the software’s intervention — at least that was the idea.\n\nEmploying software to paper over a plane’s natural instability is not new. Many of the more advanced fighter jets are designed to be unstable to ensure greater maneuverability. The fighter pilots are also trained to anticipate the peculiar flight characteristics of their planes. In contrast, there have been many complaints that pilots of the Max 8 were not properly informed of the existence of the MCAS system:\n\n“There are 1,400 pages and only one mention of the infamous Maneuvering Characteristics Augmentation System (MCAS) … in the abbreviations sections. But the manual does not include an explanation of what it is…”\n\nPerhaps Boeing determined that information about this system wasn’t worth attention by pilots. After all, the intention of the MCAS system was to make the 737 Max 8 to give the same “feel” as the previous model the 737 NG. This is what we call in software circles as virtualization. That is, this is software that renders a virtual machine on the pilot’s user interface to the plane so it feels and acts like another kind of plane (i.e. one that is structurally balanced).\n\nThere is a “law” in software development knows as “The Law of Leaky Abstractions” which states “All non-trivial abstractions, to some degree, are leaky.” MCAS is perhaps a leaky abstraction, that is, it tries to creates a virtual abstraction of a legacy 737 NG without Leap engines, to hide an unbalanced airplane. Surely, nothing can leak with this kind of abstraction? It is one thing to abstract away virtual machinery and its entirely another thing to attempt to abstract away physical reality. However, in both cases, something will eventually leak through.\n\nSo does the MCAS behave when its abstractions begin to leak? Here is what is reported by pilots of the plane:\n\n“On both the NG and the MAX, when you have a runaway trim stab this can be stopped temporarily by pulling the control column in the opposite direction. But when the MCAS is activated because of a high angle of attack, this can only be stopped by cutting the electrical trim motor.”\n\nHow a pilot responds to an abstraction leak can be very different from that of the real thing it is trying to abstract. With faulty sensors, one can turn this off and use one’s understanding of the situation and the plane to make good decisions. However, when one’s understanding of the nature of the plane is virtual and not real, then you just can’t revert to reality. Reality is outside of the pilot’s comprehension and thus a cause to inproper decision making. A virtual trashcan works like a regular trash can in that you can still recover the documents you place in the trash before it is emptied. Reality however is very different than the virtual world, many times there is no undo function!\n\nThen there’s this leaky abstraction when the plane itself has exhibited its own intentions:\n\nBut the EFS never acts by itself, so we were astounded when we heard what the real reason was. (…) However, in some cases — as happened on Flight 610 — the MCAS moves by itself.\n\nand this:\n\nMCAS is activated without pilot input and only operates in manual, flaps up flight.\n\nThis is because, a virtual abstraction of a real plane, is the same as Level 5 automation! If MCAS is turned off, the pilots will find themselves to be flying an entirely different plane. When you abstract away interaction with reality, you cannot avoid introducing a process that mediates between a pilot’s action and the actual actions of the plane. The behavior of the real plane will depend on the environment that it is in. The behavior of a virtual plane will depend on just the working sensors that are available to render the virtual simulation. Level 5 automation requires a kind of intelligence that is aware of what sensors are faulty and furthermore is able to navigate a problem with partial and unobserved information. The smarts to enable this kind of Artificial Intelligence is simply not available in our current state of technological development.\n\nIn short, Boeing has decided to implement technology that is simply too ambitious. Not all software has the same level of complexity. This is not an issue of insufficient testing to uncover logical flaws in the software. This is not an issue of robustly handling sensor and equipment failure. This is an issue of attempting to implement an overly ambitious and thus a dangerous solution.\n\nAir travel is extremely reliable, but introducing software patches as a means to virtualize physical behavior can lead to unintended consequences. The reason that we still fly planes with pilots in them is that we expect pilots to be able to solve unexpected situations that automation cannot handle. MCAS like virtualization, handcuffs pilots from differentiating between the real and the simulated. I would thus recommend to regulators that in the future, MCAS like virtualization should be treated and tested very differently from other automation. They should be treated as Level 5 automation with a more exhaustive level of scrutiny.\n\n", - "title": "AI Safety, Leaking Abstractions and Boeing’s 737 Max 8", - "url": "https://medium.com/intuitionmachine/ai-safety-leaking-abstractions-and-boeings-737-max-8-5d4b3b9bf0c3" - }, - { - "authors": ["Niniek Karmini", "David Koenig"], - "cloudinary_id": "reports/www.courthousenews.com/wp-content/uploads/2018/11/AP18332052000971-300x206.jpg", - "date_published": "2018-11-29", - "description": "JAKARTA, Indonesia (AP) — Pilots fought against an automated system that pitched a Boeing jetliner’s nose down repeatedly because of a faulty sensor until they finally lost control and plunged into the Java Sea last month, Indonesian investigators said Wednesday.\nAt a news conference, safety officials said they were still struggling to understand why the plane crashed, killing all 189 people on board.\n\nThe National Transportation Safety Commission’s Nurcahyo Utomo said investigators were trying to figure out from interviews with engineers why they certified that the Boeing 737 MAX 8 was airworthy and whether they had followed required maintenance procedures. Pilots of previous flights had reported problems with control systems on the brand-new jet.", - "epoch_date_submitted": 1559347200, - "image_url": "http://www.courthousenews.com/wp-content/uploads/2018/11/AP18332052000971-300x206.jpg", - "inputs_outputs": null, - "language": "en", - "report_number": 389, - "source_domain": "courthousenews.com", - "submitters": ["Catherine Olsson"], - "tags": [], - "text": "JAKARTA, Indonesia (AP) — Pilots fought against an automated system that pitched a Boeing jetliner’s nose down repeatedly because of a faulty sensor until they finally lost control and plunged into the Java Sea last month, Indonesian investigators said Wednesday.\n\nAt a news conference, safety officials said they were still struggling to understand why the plane crashed, killing all 189 people on board.\n\nThe National Transportation Safety Commission’s Nurcahyo Utomo said investigators were trying to figure out from interviews with engineers why they certified that the Boeing 737 MAX 8 was airworthy and whether they had followed required maintenance procedures. Pilots of previous flights had reported problems with control systems on the brand-new jet.\n\nThe board issued a preliminary report that stopped short of declaring a probable cause of the crash — the investigation is continuing — but it provided new details about the pilots’ struggle to fly the highly automated jet and Lion Air’s inability to fix problems with sensors on the plane.\n\nSensors that measure speed were flushed and checked, and an electrical plug was cleaned before the fatal flight. Mechanics, however, did not check a sensor that measures whether the nose of the plane is pointing up or down.\n\nThat “angle of attack” sensor gave faulty readings throughout the short flight, triggering a system that automatically pointed the plane’s nose down more than two dozen times, with pilots responding by manually fighting to correct the pitch. Pilots even asked air traffic controllers to tell them how fast and high they were flying.\n\nThe malfunctions and warnings from the plane’s control system appeared to overwhelm the pilots almost as soon as the jet became airborne, said another investigator, Ony Suryo Wibowo.\n\n“The problem is if multiple malfunctions occur all at once, which one should be prioritized?” Wibowo said.\n\nIn a statement following release of the report, U.S.-based Boeing declared that the MAX, its newest plane, is safe. The manufacturer played up the possibility of pilot error.\n\nBoeing noted that the crew of the plane’s previous flight one day earlier had responded correctly to the automatic nose-down pitch and flew the plane manually. They also ran safety checklists. The preliminary report does not say whether pilots on the deadly flight took those steps, Boeing pointed out.\n\nBoeing has said that the procedure to correct an automatic nose-down pitch is in the plane’s operating manual and pilots should have known about it.\n\nSearchers have not yet recovered the plane’s cockpit voice recorder, which could tell investigators what the pilots were doing — or failing to do — to regain control of the plane during the brief, erratic flight.\n\nThe report by Indonesia’s safety commission repeated earlier recommendations made just after the disaster that pilots be better versed in emergency procedures and aware of past aircraft problems. They recommended that Lion Air, a fast-growing low-cost airline based in Jakarta, ensure that pilots follow proper procedures “to improve the safety culture.”\n\nMary Schiavo, a former inspector general of the U.S. Department of Transportation, said the preliminary report offered a road map of final recommendations that are likely to emerge from the investigation.\n\n“They will be looking for more precise reporting of problems (by pilots), and certainly a better maintenance response,” she said.\n\nPeter Lemme, an expert in aviation and satellite communications and a former Boeing engineer who wrote an analysis of the data on his blog, compared the scene in the cockpit to “a deadly game of tag” in which the plane pointed down, the pilots countered by manually aiming the nose higher, only for the sequence to repeat about five seconds later.\n\nThat happened 26 times during the 11-minute flight, but pilots failed to recognize what was happening and follow the known procedure for countering incorrect activation of the automated safety system, Lemme told The Associated Press.\n\nLemme said he was troubled that there weren’t easy checks to see if sensor information was correct, that the crew of the fatal flight apparently wasn’t warned about the problems on previous flights and that the Lion Air jet wasn’t fully repaired after those flights.\n\n“Had they fixed the airplane, we would not have had the accident,” he said. “Every accident is a combination of events, so there is disappointment all around here,” he said.\n\nThe U.S. National Transportation Safety Board and Boeing experts are helping the Indonesian investigators.\n\nBoeing has a great deal at stake in defending its plane.\n\nMore than 200 MAX jets have been delivered to airlines around the world. Pilots at American Airlines and Southwest Airlines complained this month that they had not been given all information about the new automated anti-stall safety system on the MAX.\n\nBoeing shares fell 14 percent in the last three weeks through Tuesday, as investigators focused on t", - "title": "Report Faults Safety Failures, Defects in Lion Air Crash", - "url": "https://www.courthousenews.com/report-faults-safety-failures-defects-in-lion-air-crash/" - }, - { - "authors": ["Niniek Karmini", "David Koenig"], - "cloudinary_id": "reports/storage.googleapis.com/afs-prod/media/media:2f65d418951d44d59e9c68c92123f4d7/2853.jpeg", - "date_published": "2018-11-29", - "description": "JAKARTA, Indonesia (AP) — Pilots fought against an automated system that pitched a Boeing jetliner's nose down repeatedly because of a faulty sensor until they finally lost control and plunged into the Java Sea last month, Indonesian investigators said Wednesday. At a news conference, safety officials said they were still struggling to understand why the plane crashed, killing all 189 people on board. The National Transportation Safety Commission's Nurcahyo Utomo said investigators were trying to figure out from interviews with engineers why they certified that the Boeing 737 MAX 8 was airworthy and whether they had followed required maintenance procedures. Pilots of previous flights had reported problems with control systems on the brand-new jet.", - "epoch_date_submitted": 1559347200, - "image_url": "https://storage.googleapis.com/afs-prod/media/media:2f65d418951d44d59e9c68c92123f4d7/2853.jpeg", - "inputs_outputs": null, - "language": "en", - "report_number": 388, - "source_domain": "apnews.com", - "submitters": ["Catherine Olsson"], - "tags": [], - "text": "National Transportation Safety Committee investigator Nurcahyo Utomo holds a model of an airplane during a press conference on the committee's preliminary findings on their investigation on the crash of Lion Air flight 610, in Jakarta, Indonesia, Wednesday, Nov. 28, 2018. Black box data collected from their crashed Boeing 737 MAX 8 show Lion Air pilots struggled to maintain control as the aircraft's automatic safety system repeatedly pushed the plane's nose down, according to a preliminary investigation into last month's disaster. (AP Photo/Achmad Ibrahim)\n\nJAKARTA, Indonesia (AP) — Pilots fought against an automated system that pitched a Boeing jetliner’s nose down repeatedly because of a faulty sensor until they finally lost control and plunged into the Java Sea last month, Indonesian investigators said Wednesday.\n\nAt a news conference, safety officials said they were still struggling to understand why the plane crashed, killing all 189 people on board.\n\nThe National Transportation Safety Commission’s Nurcahyo Utomo said investigators were trying to figure out from interviews with engineers why they certified that the Boeing 737 MAX 8 was airworthy and whether they had followed required maintenance procedures. Pilots of previous flights had reported problems with control systems on the brand-new jet.\n\nThe board issued a preliminary report that stopped short of placing blame for the crash — the investigation is continuing — but it provided new details about the pilots’ struggle to fly the highly automated jet and Lion Air’s inability to fix problems with sensors on the plane.\n\nSensors that measure speed were flushed and checked, and an electrical plug was cleaned before the fatal flight. Mechanics, however, did not check sensors that measure whether the nose of the plane is pointing up or down.\n\nAn “angle of attack” sensor gave faulty readings throughout the short flight, triggering a system that automatically pointed the plane’s nose down more than two dozen times, with pilots responding by manually fighting to correct the pitch. Pilots even asked air traffic controllers to tell them how fast and high they were flying.\n\nThe malfunctions and warnings from the plane’s control system appeared to overwhelm the pilots almost as soon as the jet became airborne, said another investigator, Ony Suryo Wibowo.\n\n“The problem is if multiple malfunctions occur all at once, which one should be prioritized?” Wibowo said.\n\nIn a statement following release of the report, U.S.-based Boeing declared that the MAX, its newest plane, is safe. The manufacturer played up the possibility of pilot error.\n\nBoeing noted that the crew of the plane’s previous flight one day earlier had responded correctly to the automatic nose-down pitch and flew the plane manually. They also ran safety checklists. The preliminary report does not say whether pilots on the deadly flight took those steps, Boeing pointed out.\n\nBoeing has said that the procedure to correct an automatic nose-down pitch is in the plane’s operating manual and pilots should have known about it.\n\nSeveral experts said, however, that Boeing likely will have to consider changes in the new anti-stall system, perhaps developing an algorithm to disregard sensor readings that appear off-base.\n\nThe report offered new details on persistent problems with sensors on the Lion Air jet and the airline’s efforts to fix them.\n\nJohn Cox, a safety consultant and former airline pilot, said Lion Air should have taken the troubled plane on a maintenance test flight.\n\n“I don’t think the airplane was ready for passenger service because they had not validated that they had fixed the problem,” he said.\n\nSearchers have not yet recovered the plane’s cockpit voice recorder, which could tell investigators what the pilots were doing — or failing to do — to regain control of the plane during the brief, erratic flight.\n\nThe report by Indonesia’s safety commission did not draw conclusions about why the crew lost control of the plane, but it repeated earlier recommendations that pilots be better versed in emergency procedures and aware of past aircraft problems. They recommended that Lion Air, a fast-growing low-cost airline based in Jakarta, ensures that pilots follow proper procedures “to improve the safety culture.”\n\nMary Schiavo, a former inspector general of the U.S. Department of Transportation, said the preliminary report offered a road map of final recommendations that are likely to emerge from the investigation.\n\n“They will be looking for more precise reporting of problems (by pilots), and certainly a better maintenance response,” she said.\n\nPeter Lemme, an expert in aviation and satellite communications and a former Boeing engineer who wrote an analysis of the data on his blog, compared the scene in the cockpit to “a deadly game of tag” in which the plane pointed down, the pilots countered by manually aiming the nose higher, only for the sequence to repeat about five seconds later.\n\nThat happened 26 times during the 11-mi", - "title": "Report faults safety failures, defects in Lion Air crash", - "url": "https://www.apnews.com/4cf97e9b0a004cf5a0db401bb1d222e1" - }, - { - "authors": ["Helen Regan", "Masrur Jamaluddin"], - "cloudinary_id": "reports/cdn.cnn.com/cnnnext/dam/assets/181107144739-01-lion-air-1106-super-tease.jpg", - "date_published": "2018-11-08", - "description": "Problems were reported on a Lion Air jet that crashed into the sea off Jakarta even after technicians replaced a sensor on board the aircraft, investigators said.", - "epoch_date_submitted": 1559347200, - "image_url": "https://cdn.cnn.com/cnnnext/dam/assets/181107144739-01-lion-air-1106-super-tease.jpg", - "inputs_outputs": null, - "language": "en", - "report_number": 387, - "source_domain": "edition.cnn.com", - "submitters": ["Catherine Olsson"], - "tags": [], - "text": "(CNN) Problems were reported on a Lion Air jet that crashed into the sea off Jakarta even after technicians replaced a sensor on board the aircraft, investigators said.\n\nIndonesian authorities confirmed Wednesday that the angle of attack (AOA) sensor was replaced after a flight from Manado, in North Sulawesi to Denpasar, Bali on October 28. The Boeing 737 MAX 8 then made another flight to Jakarta that same day, and the pilots reported further problems.\n\nAll 189 people on board Flight 610 died when the new Boeing 737 MAX 8 crashed into the sea on October 29, 13 minutes after taking off from Jakarta on a short flight to Pangkal Pinang on the Indonesian island of Bangka.\n\nInvestigators said the jet experienced problems on its last four flights -- including, crucially, the flight that crashed, according to Soerjanto Tjahjono, the head of the National Transportation Safety Committee (KNKT).\n\nBoeing released an operational bulletin on Wednesday, warning all airlines about how to address any erroneous readings related to the AOA sensor. The Federal Aviation Administration (FAA) later issued its own directive that advised pilots about how to respond to similar problems.\n\nSearch for voice recorder\n\nAlmost two weeks after the crash, authorities are still searching for the plane's cockpit voice recorder (CVR), which is believed to be buried under deep mud. If found, it should reveal what happened in the cockpit in the final seconds of the flight.\n\nInvestigators are already examining the flight data recorder that was pulled off the sea bed, some 30 meters under water, on November 1.\n\nPhotos: In photos: Lion Air plane crashes off Indonesia A relative of one of the crash victims tosses flower petals from an Indonesian Navy ship on Tuesday, November 6. Hide Caption 1 of 27 Photos: In photos: Lion Air plane crashes off Indonesia Victims' families and colleagues react on a Navy ship as they're ferried to the crash site on November 6. Hide Caption 2 of 27 Photos: In photos: Lion Air plane crashes off Indonesia An Indonesian official examines a turbine engine from the plane on Sunday, November 4. Hide Caption 3 of 27 Photos: In photos: Lion Air plane crashes off Indonesia People in Jakarta grieve over the coffin of a relative on November 4. Hide Caption 4 of 27 Photos: In photos: Lion Air plane crashes off Indonesia Indonesian Navy divers recover a flight data recorder on Thursday, November 1. Hide Caption 5 of 27 Photos: In photos: Lion Air plane crashes off Indonesia Families of the victims look through personal items that were recovered from the wreckage. Hide Caption 6 of 27 Photos: In photos: Lion Air plane crashes off Indonesia A police officer arranges shoes recovered during search operations. Hide Caption 7 of 27 Photos: In photos: Lion Air plane crashes off Indonesia A Navy ship arrives at a search area in the waters of Karawang. Hide Caption 8 of 27 Photos: In photos: Lion Air plane crashes off Indonesia A relative of a passenger cries at a Jakarta hospital on Tuesday, October 30. Family members have been providing authorities with DNA samples to help identify victims of the crash. Hide Caption 9 of 27 Photos: In photos: Lion Air plane crashes off Indonesia Officials transport a body bag in Jakarta. Hide Caption 10 of 27 Photos: In photos: Lion Air plane crashes off Indonesia Indonesian President Joko Widodo inspects debris. Hide Caption 11 of 27 Photos: In photos: Lion Air plane crashes off Indonesia Victims' relatives embrace at a police hospital in Jakarta. Hide Caption 12 of 27 Photos: In photos: Lion Air plane crashes off Indonesia Soldiers drag ashore an inflatable raft containing debris from the plane. Hide Caption 13 of 27 Photos: In photos: Lion Air plane crashes off Indonesia A wallet is seen in the water where the plane went down. Hide Caption 14 of 27 Photos: In photos: Lion Air plane crashes off Indonesia A relative of a passenger cries at a hospital in Jakarta. Hide Caption 15 of 27 Photos: In photos: Lion Air plane crashes off Indonesia President Widodo (front row, second from right) tours the operations center in Jakarta where debris is laid out. Hide Caption 16 of 27 Photos: In photos: Lion Air plane crashes off Indonesia Police work to identify personal belongings that are believed to be from the plane's wreckage. Hide Caption 17 of 27 Photos: In photos: Lion Air plane crashes off Indonesia A police officer studies a map in the search-and-rescue command center. Hide Caption 18 of 27 Photos: In photos: Lion Air plane crashes off Indonesia A diver with the Indonesian Navy enters the water off the north coast of Karawang. Hide Caption 19 of 27 Photos: In photos: Lion Air plane crashes off Indonesia Rescue workers carry a body that was recovered from the waters near Jakarta on October 29. Hide Caption 20 of 27 Photos: In photos: Lion Air plane crashes off Indonesia A woman in Pangkal Pinang prays as she and others wait for news on October 29. Hide Caption 21 of 27 Photos: In photos: Lion Air plane crashes off ", - "title": "Lion Air: Sensor was replaced day before crash but problems persisted", - "url": "https://edition.cnn.com/2018/11/08/asia/lion-air-sensor-replaced-intl/index.html" - }, - { - "authors": ["Barbara S. Peterson"], - "cloudinary_id": "reports/hips.hearstapps.com/hmg-prod.s3.amazonaws.com/images/investigators-examine-engine-parts-from-the-ill-fated-lion-news-photo-1058470458-1541688511.jpg?crop=1.00xw:0.733xh;0,0.0375xh&resize=1200:*", - "date_published": "2018-11-08", - "description": "Mounting evidence that something was very wrong with the sensors on board the Boeing 737 MAX and the flight information being sent to the pilots.", - "epoch_date_submitted": 1559347200, - "image_url": "https://hips.hearstapps.com/hmg-prod.s3.amazonaws.com/images/investigators-examine-engine-parts-from-the-ill-fated-lion-news-photo-1058470458-1541688511.jpg?crop=1.00xw:0.733xh;0,0.0375xh&resize=1200:*", - "inputs_outputs": null, - "language": "en", - "report_number": 386, - "source_domain": "popularmechanics.com", - "submitters": ["Catherine Olsson"], - "tags": [], - "text": "The FAA has issued an emergency directive to anyone flying the Boeing 737 MAX, the type of plane that crashed in the Lion Air Flight JH 610 incident, related to the faulty sensors that reportedly fed bad information to the pilots. Meanwhile, investigators have reported that the plane received a replacement angle-of-attack sensor (a system that measures whether the plane's nose is too high relative to the current of air) the day before the deadly crash.\n\nAs Bloomberg reported earlier this week, Boeing issued a bulletin to 737 MAX operators warning them that faulty inputs from airflow sensors could have contributed to the Lion Air crash. The Boeing statement gave few details, saying that “the investigation is ongoing.” The FAA's directive backs up that bulletin by giving anyone who flies the 737 MAX three days, starting Wednesday, to revise their airplane flight manual for that model.\n\nThe FAA said it was taking the action to “address possible erroneous angle of attack inputs” that could put the plane in a sudden dive. The FAA said airline operators need to revise the manual “to give crew Horizontal stabilizer trim procedures to follow under certain conditions.\" In the U.S., the order affects American, Southwest, and United, which together operate 45 MAX jets.\n\nHere's the official statement:\n\n“Boeing has released a Flight Crew Operations Manual Bulletin regarding the potential for erroneous angle of attack input on 737 Max aircraft. The FAA plans to mandate the Flight Crew Operations Manual Bulletin by issuing an Airworthiness Directive (AD). The FAA continues to work closely with Boeing, and as a part of the investigative team on the Indonesia Lion Air accident, will take further appropriate actions depending on the results of the investigation. The FAA has alerted affected domestic carriers and foreign airworthiness authorities who oversee air carriers that use the 737 MAX of the agency’s forthcoming action.\"\n\nThe new revelation—that the doomed 737 received a new sensor after a flight on October 28, the day before the deadly crash—adds to the growing evidence of a serious problem with the sensors and flight data being fed to the crew of Lion Air 610. Data downloaded from the flight data recorder, which was recovered by divers searching the underwater crash site, have revealed a series of glitches in three flights prior to the crash that gave flawed airspeed and other information to the cockpit.\n\nIf AOA sensors fail, it can cause the plane’s computers to incorrectly show the plane is heading into an aerodynamic stall—which, in turn, can put the jet into a sudden dive to restore airspeed.\n\n“It’s too early to tell if the sensors caused the accident,” said John Goglia, an aviation safety expert and former member of the National Transportation Safety Board. He said that sensor equipment has become more automated with each new generation of aircraft, and that, in turn, introduces a new risk.\n\n“This could be a training issue,” he said. “The automation keeps marching forward and this is most advanced model that Boeing has built.” It’s still unclear whether the issue is one of an actual flaw, or a procedure that wasn’t correctly followed.", - "title": "Lion Air Flight JH 160 Crash Sensor Replacement", - "url": "https://www.popularmechanics.com/flight/airlines/a24841961/sensor-replaced-one-day-before-lion-air-flight-jh-160-crash/" - }, - { - "authors": ["Jeremy Bogaisky"], - "cloudinary_id": "reports/thumbor.forbes.com/thumbor/600x315/https%3A%2F%2Fspecials-images.forbesimg.com%2Fimageserve%2F1057534088%2F960x0.jpg", - "date_published": "2018-11-09", - "description": "With autopilot and auto landing, many airline pilots now aren't getting regular manual flying practice. That may have played a role in the Lion Air crash, says safety consultant Keith Mackey.", - "epoch_date_submitted": 1559347200, - "image_url": "https://thumbor.forbes.com/thumbor/600x315/https%3A%2F%2Fspecials-images.forbesimg.com%2Fimageserve%2F1057534088%2F960x0.jpg", - "inputs_outputs": null, - "language": "en", - "report_number": 385, - "source_domain": "forbes.com", - "submitters": ["Catherine Olsson"], - "tags": [], - "text": "AFP/Getty Images\n\n[Updated: Nov. 13]\n\nThe FAA issued an emergency airworthiness directive Wednesday to airlines operating the new Boeing 737 MAX, calling on them to better instruct pilots on how to deal with a potential faulty reading from a key gauge, an angle of attack sensor, that's supposed to help keep planes from falling out of the sky. The directive follows the discovery that the sensor was malfunctioning on a Lion Air 737 MAX that plunged into the sea off Indonesia on Oct. 29, killing all 189 aboard.\n\nThe accident and the FAA warning, which comes after Boeing issued a similar bulletin, may be less an indication that there’s anything wrong with the new version of Boeing’s top-selling plane than a sign of how increasingly automated flight systems are eroding pilot skills, says Keith Mackey, a Florida-based safety consultant who’s a former airline pilot and accident investigator.\n\nTo put it simply, says Mackey, the FAA’s directive tells pilots to turn off the plane's autopilot system, and if necessary the pitch trim system too, and fly the plane yourself.\n\n“It’s unfortunate that they even have to write something like this,” says Mackey. “It should be understood.”\n\nThe angle of attack sensor is a vane on the wing that gauges airflow to determine if the wing is generating enough lift. That airflow can be disrupted if the plane slows down or goes into too steep a climb, putting the plane in danger of stalling. In that situation the 737 MAX has a pitch trim system that automatically pushes the nose down to prevent a stall. The FAA's and Boeing's bulletins explain to pilots how to disengage the system in the event that a faulty reading from the angle of attack sensor leads it to push the nose down.\n\nShortly after takeoff from Jakarta, the Lion Air plane’s pilots requested to return to the airport, but they never turned back, plunging into the water at high speed.\n\nBased on the evidence available, says Mackey, the pilots may not have disengaged the autopilot or the trim system, which given the faulty angle of attack reading, may have lowered the nose and dived the plane into the water.\n\n“The pilot should have recognized that I’m getting an erroneous indication on my airspeed,” he says. “It’s a nice day, you can look out the window and see that the airplane isn’t flying nose high.”\n\nHe sees the accident as indicative of two problems: the increasing use of autopilot is giving pilots less flight time to maintain basic skills, and a lower level of experience and training among pilots in the developing world.\n\nMany airlines will require pilots shortly after takeoff to engage the autopilot, which in modern jets essentially manage the direction, speed and altitude of a plane based on instructions programmed by the pilot. Planes can also auto land at airports that have the necessary equipment.\n\n“You get a lot of takeoffs and landings but no one gets much flying practice,” says Mackey. “They’re getting to be good computer programmers, they know which buttons to push and when to push them. When something begins to fail it becomes a puzzlement.”\n\nThe 737 MAX is the only Boeing plane in which the angle of attack sensor triggers automated movements of the jet's tail, the aviation safety consultant John Cox told the Seattle Times.\n\nBoeing appears not to have informed pilots and airlines that a new automated trim system had been introduced in the MAX, failing to add mention of it to the flight crew operations manual or to include it in training for pilots, Aviation Week and the Wall Street Journal reported Nov. 13.\n\n\"If [it] was not covered in the MAX training, that is an error,\" says Mackey, but \"a pilot proficient in hand flying the airplane should have recognized an automation failure issue quickly and reverted to hand flying the airplane to an uneventful landing, with or without the training.\"\n\nIn developing countries with small military and private aviation sectors, airlines tend to rely on Western schools for pilot training, putting many pilots at the controls of large aircraft with fewer flight hours, he says.\n\nThose same factors limit the pool of trained mechanics. With air travel taking off in Asia, that can lead to systematic maintenance issues at growing airlines, says Mackey.\n\nThe Lion Air plane’s angle of attack sensor had registered incorrect airspeed readings on the four flights prior to the one that crashed, according to the Indonesia National Transportation Safety Committee, and the sensor had been replaced the day before.\n\nMackey says it’s a red flag that the issue went unaddressed for so long.\n\nBoeing has a lot riding on the 737 MAX: It has more than 4,500 orders on the books and has delivered 219. Lion Air was the launch customer for the 737 MAX 8, the variant of the plane that crashed, and the MAX 9.\n\nBoeing's bulletin states that the angle of attack sensor can trigger the 737 MAX's automatic pitch trim system to push the nose down for as long as 10 seconds. If pilots manually pull the nose back up, the system can reengage", - "title": "Crash Of Lion Air 737 MAX Raises Questions About Autopilot And Pilot Skills", - "url": "https://www.forbes.com/sites/jeremybogaisky/2018/11/08/crash-of-lion-air-737-max-raises-questions-about-autopilot-and-pilot-skills/#133731448477" - }, - { - "authors": ["Andy Pasztor", "Andrew Tangel"], - "cloudinary_id": "reports/s.marketwatch.com/public/resources/MWimages/MW-GY683_Southw_ZG_20181116075039.jpg", - "date_published": "2018-11-16", - "description": "During the three weeks before Lion Air Flight 610 plunged into waters off Indonesia, Southwest Airlines Co. replaced two malfunctioning flight-control...", - "epoch_date_submitted": 1559347200, - "image_url": "http://s.marketwatch.com/public/resources/MWimages/MW-GY683_Southw_ZG_20181116075039.jpg", - "inputs_outputs": null, - "language": "en", - "report_number": 384, - "source_domain": "marketwatch.com", - "submitters": ["Catherine Olsson"], - "tags": [], - "text": "During the three weeks before Lion Air Flight 610 plunged into waters off Indonesia, Southwest Airlines Co. LUV, +0.25% replaced two malfunctioning flight-control sensors of the same type that has been publicly implicated in the crash, according to a summary of Southwest maintenance records reviewed by The Wall Street Journal.\n\nBoth U.S. maintenance issues involved a Boeing Co. BA, +2.56% 737 MAX 8, the same model that crashed last month in Indonesia. The sensors measure whether the jetliner is angled above or below level flight. Those sensors, or related hardware, needed repairs in the Southwest instances, according to the summary document. The document also indicates Southwest pilots reported they couldn’t engage automated throttle settings, similar to cruise control on a car.\n\nA Southwest spokeswoman said the sensors didn’t fail and were removed as a precautionary measure as part of a troubleshooting process. She said at least one was repaired.\n\nInvestigators have confirmed the same type of sensor failed on the Lion Air flight, but they haven’t determined precisely what happened between that failure and the crash.\n\nSince the accident, which killed 189 people, Boeing has warned airlines about the potential for erroneous data from what are called “angle-of-attack” sensors. “We have not experienced a sensor failure or flight issue as described in Boeing’s bulletin,” the Southwest spokeswoman said.\n\nThe Southwest incidents didn’t result in emergencies and no one was hurt. They prompted what appear to be routine reports by mechanics checking out problems with the sensors.\n\nAn expanded version of this report appears on WSJ.com.", - "title": "Southwest replaced flight-control sensors of the kind implicated in Lion Air crash", - "url": "https://www.marketwatch.com/story/southwest-replaced-flight-control-sensors-of-the-kind-implicated-in-lion-air-crash-2018-11-16" - }, - { - "authors": ["Alan Levin", "Harry Suhartono", "Julie Johnsson"], - "cloudinary_id": "reports/timedotcom.files.wordpress.com/2018/10/lion-air-jet.jpg?quality=85", - "date_published": "2018-10-31", - "description": "The instruments were checked by maintenance workers overnight", - "epoch_date_submitted": 1559347200, - "image_url": "https://timedotcom.files.wordpress.com/2018/10/lion-air-jet.jpg?quality=85", - "inputs_outputs": null, - "language": "en", - "report_number": 383, - "source_domain": "time.com", - "submitters": ["Catherine Olsson"], - "tags": [], - "text": "(Bloomberg) — The Lion Air jet that crashed into the Java Sea off Indonesia earlier this week had experienced problems with the sensors used to calculate altitude and speed on its previous flight, an issue that could help explain why the plane dove into the water.\n\nPilots on the nearly new Boeing Co. 737 Max 8 reported the issue after flying from Denpasar to Jakarta the night before the accident, Lion Air spokesman Danang Mandala Prihantoro said Wednesday. The instruments were checked by maintenance workers overnight and the plane was cleared to fly, Prihantoro said.\n\nWhile it will be days or weeks before definitive information emerges in the crash shortly after takeoff with 189 people aboard, discrepancies in speed and altitude readings can cause confusion on the cockpit and have led to accidents in the past, including the 2009 crash of an Air France plane in the Atlantic Ocean.\n\nFlight-tracking data before the two-month old Lion Air jet crashed showed the plane was varying its altitude and speed, a possible indication that the pilots weren’t getting accurate information from the aircraft’s air-pressure sensors.\n\nThe Brief Newsletter Sign up to receive the top stories you need to know right now. View Sample Sign Up Now\n\nErroneous sensors could be an explanation for the flight track data, said John Cox, president of Safety Operating Systems and a former airline pilot. But Cox and others cautioned that it’s too soon to say what happened on the Lion Air flight and some of the flight data — such as speeds that weren’t extreme and none of the highly abrupt maneuvers that preceded the Air France jet’s loss of control — may suggest some other cause.\n\nIndonesia’s National Search and Rescue Agency has picked up signals from so-called pingers on the Flight JT610’s crash-proof flight recorders.\n\n“We have located the area where we strongly believe the plane’s black box is located. The large parts of the aircraft should be nearby,” National Military Chief Hadi Tjahjanto said.\n\nInspections of the 737 Max 8 aircraft operated by Lion Air and Garuda Indonesia since the accident found no technical issues, according to a statement by the country’s Transport Ministry. A review of maintenance documents found no additional issues were reported on the airspeed and altimeter system in the past 3 months.\n\nInvestigators from around the world, including a team from Boeing, are onsite poring over evidence. For now, the planemaker has told 737 Max operators that it doesn’t recommend they take any action at this time.\n\nEven with modern GPS tracking, planes need to calculate their precise speed through the air. To determine airspeed — which can vary substantially compared to the speed over the ground due to winds — aircraft rely on Pitot tubes which measure the air rushing into them.\n\nBy comparing that pressure against the ambient air pressure — which is obtained by what are known as static ports — aircraft can determine airspeed.\n\nIf either of the pressure sensors are blocked, it can cause erroneous readings. In the case of the Air France flight, investigators concluded that a high-altitude ice storm clogged the Pitot tubes.\n\nJetliners are equipped with three separate airspeed sensor systems as backups. If one goes bad, pilots are trained to check the other readings and disregard the one that’s incorrect.\n\nUnited Technologies Corp. is a supplier of the systems to the 737 Max, according to Airframer.com, a website that tracks suppliers of aircraft components. A company representative said she wasn’t immediately able to confirm whether its equipment was on the Lion Air plane.\n\nLion Air and investigators haven’t provided details about the issue on the previous flight Sunday night. Data provided by flight tracking company FlightRadar24 showed that the jet took off and reached an altitude of 5,550 feet (1,692 meters), then dropped to 4,625 feet, an unusual altitude loss at a time when aircraft normally climb steadily.\n\nThe plane then resumed its climb, but never exceeded 28,000 feet. Jetliners usually don’t fly below 30,000 feet because cruising at the lower altitudes is less fuel efficient. However, planes with partially malfunctioning altitude sensors aren’t allowed above 28,000 feet.\n\n“Every aircraft that we have will go through transit, pre-flight and post-flight checks,” said Lion Air’s Prihantoro. “We are conducting inspection and maintenance if needed on every aircraft.”\n\nAir France\n\nIndonesia’s National Transportation Safety Committee, which is leading the investigation, has interviewed pilots from the previous flight, Ony Soerjo Wibowo, an investigator, told a news conference on Tuesday.\n\nIn the Air France accident, investigators concluded that the Pitot tubes had become clogged with ice and all three of the plane’s speed indicators failed at different times, according to the final report of France’s Office of Investigation and Analysis.\n\nThe routine flight from Rio de Janeiro to Paris was interrupted with a cacophony of alarms and confusing informatio", - "title": "Lion Air Jet Had Airspeed Sensor Failure on Previous Flight", - "url": "http://time.com/5441004/lion-air-jet-sensor-failure/" - }, - { - "authors": ["Reuters Editorial"], - "cloudinary_id": "reports/s4.reutersmedia.net/resources_v2/images/rcom-default.png", - "date_published": "2018-11-07", - "description": "Boeing Co said on Wednesday it had issued a bulletin to airlines reminding pilots how to handle circumstances where there was erroneous data from \"angle of attack\" sensors, in the wake of the Lion Air crash in Indonesia last week.", - "epoch_date_submitted": 1559347200, - "image_url": "https://s4.reutersmedia.net/resources_v2/images/rcom-default.png", - "inputs_outputs": null, - "language": "en", - "report_number": 382, - "source_domain": "reuters.com", - "submitters": ["Catherine Olsson"], - "tags": [], - "text": "ZHUHAI, China, Nov 7 (Reuters) - Boeing Co said on Wednesday it had issued a bulletin to airlines reminding pilots how to handle circumstances where there was erroneous data from “angle of attack” sensors, in the wake of the Lion Air crash in Indonesia last week.\n\nThe manufacturer said in a statement that the Indonesian National Transportation Safety Committee had indicated that the Boeing 737 MAX jet involved in the crash, which killed all 189 people on board, had experienced erroneous input from one of the sensors.\n\nThe angle of attack probe measures the attitude of the plane relative to the air flow and provides crucial data for flight controls. An angle that is too high can throw the aircraft into an aerodynamic stall.\n\nThe Boeing statement did not say whether its advice was specific to the 737 MAX or included other aircraft models. (Reporting by Tim Hepher in Zhuhai, China; Writing by Jamie Freed; Editing by Muralikumar Anantharaman)", - "title": "Boeing issues bulletin for pilots after Lion Air sensor data error", - "url": "https://www.reuters.com/article/indonesia-crash-boeing/boeing-issues-bulletin-for-pilots-after-lion-air-sensor-data-error-idUSB9N1X603X" - }, - { - "authors": ["Stanley Widianto", "Ashley Halsey III", "Aaron Gregg"], - "cloudinary_id": "reports/www.mercurynews.com/wp-content/uploads/2018/11/Indonesia-Lion-Air-Crash.jpg?w=1024&h=642", - "date_published": "2018-11-29", - "description": "JAKARTA, Indonesia – A malfunctioning sensor and an automated response from the aircraft’s software stymied pilots’ efforts to control a doomed Indonesian flight that went careening into the sea last month, according to a preliminary investigative report released Wednesday.\nThe report, which stops short of determining the cause of the crash or analyzing findings, chronicles the chaotic moments on the Lion Air flight before it crashed into the waters off the coast of Java last month, killing all 189 passengers and crew on board.\n\nIt details how sensors and other equipment were checked and fixed before the aircraft’s final flight, but not the “angle of attack” sensor. That measures where the nose is pointing and was showing erroneous readings throughout the short time the plane was airborne.", - "epoch_date_submitted": 1559347200, - "image_url": "https://www.mercurynews.com/wp-content/uploads/2018/11/Indonesia-Lion-Air-Crash.jpg?w=1024&h=642", - "inputs_outputs": null, - "language": "en", - "report_number": 381, - "source_domain": "mercurynews.com", - "submitters": ["Catherine Olsson"], - "tags": [], - "text": "JAKARTA, Indonesia – A malfunctioning sensor and an automated response from the aircraft’s software stymied pilots’ efforts to control a doomed Indonesian flight that went careening into the sea last month, according to a preliminary investigative report released Wednesday.\n\nThe report, which stops short of determining the cause of the crash or analyzing findings, chronicles the chaotic moments on the Lion Air flight before it crashed into the waters off the coast of Java last month, killing all 189 passengers and crew on board.\n\nIt details how sensors and other equipment were checked and fixed before the aircraft’s final flight, but not the “angle of attack” sensor. That measures where the nose is pointing and was showing erroneous readings throughout the short time the plane was airborne.\n\nWith the sensor insisting the nose was too high, an automatic feature kicked in, sending the plane plummeting as the pilots wrestled to regain control. Unable to trust their readings, the pilots resorted to asking air traffic control about their speed and altitude.\n\nLion Air Flight 610 plunged into the Java Sea on Oct. 29 just after taking off from the Indonesian capital, Jakarta, killing the eight crew members and 181 passengers on board, including a child and two infants.\n\nThe crash appears to have been caused by a mix of brand new technology and cockpit confusion as the pilots fought to gain altitude after an early-morning takeoff from Jakarta. The flight crew – at an altitude of just 5,000 feet – had very little time to resolve the issue before the plane crashed into the sea at a reported 450 miles per hour.\n\nThough the report contains no conclusion assigning blame, its descriptions of automated systems overtaking the aircraft – leaving pilots both confused and powerless – poses questions for Boeing and Lion Air about whether the cockpit crew was prepared for this scenario. After the Lion Air crash, pilots in the United States accused Boeing of withholding safety information on its new 737 model.\n\nThe aircraft’s pilots asked to return to Jakarta just two minutes after takeoff, reporting a “flight-control problem” but not specifying what it was.\n\nBlack-box data released by Indonesian investigators showed that the pilots were pulling back on the control column, attempting to raise the plane’s nose, with almost 100 pounds of pressure before they crashed.\n\nThe Indonesian National Transportation Safety Committee, which produced the report, also said that Lion Air, a Jakarta-based low-cost airline, should improve its “safety culture.”\n\nNo engineer briefed the pilots of the crashed plane on the multiple problems the aircraft experienced on previous flights, and it was up to him to review the maintenance logs.\n\nThe report, however, contains no conclusion on who was at fault.\n\n“When it comes to faulting, I don’t know. Our job isn’t to find faults,” National Transportation Safety Committee investigator Nurcahyo Utomo said at a news conference Wednesday.\n\nThe aircraft was the most recent incarnation of the venerable Boeing 737, a plane that first flew in 1967 and has gone through multiple iterations before it emerged as the 737 Max.\n\nThe 737 Max was equipped with more-powerful engines that are mounted farther forward on the wing, requiring that additional software be added to the autopilot to provide more control.\n\nThat software, which has been described as several lines of coding, was identified in the Boeing manual as the maneuvering characteristics augmentation system, or MCAS.\n\nWhen the sensors transmitted faulty data to the cockpit of Flight 610, the new MCAS system sensed a stall – that point at which planes do not have enough airspeed to create lift – and sought to correct for it by repeatedly pointing the nose of the aircraft down.\n\nA feature in previous 737 models that allowed pilots to manually override an “electric trimming” process – which automatically budges the nose downward to prevent a stall, does not work in Boeing’s 737 Max 8 planes, Boeing explained in a Nov. 7 bulletin.\n\nThat same week, the Federal Aviation Administration issued an emergency notice to all airlines that fly the 737 Max, warning them that erroneous sensor inputs “could cause the flight crew to have difficulty controlling the airplane,” leading to “possible impact with terrain.”\n\nThe deviation probably was caused by what is called a “runaway stabilizer.” Stabilizers are essentially those small wings on either side at the tail end of the plane. They each have flaps – called elevators – that help control the elevation of the plane.\n\nIn case of a runaway stabilizer, pilots are instructed in the cockpit checklist to hold the control column firmly, disengaging the autopilot that, in this case, contained the MCAS program. Next, they are told, disengage the auto throttle and manually fly the plane.\n\n“This corner of the performance charts is called the ‘coffin corner,’ ” said Mary Schiavo, an aviati", - "title": "Report: Lion Air pilots unable to correct for faulty sensor", - "url": "https://www.mercurynews.com/2018/11/28/report-lion-air-pilots-unable-to-correct-faulty-sensor/" - }, - { - "authors": ["Iafrica Editors Curate"], - "cloudinary_id": "reports/www.iafrica.com/wp-content/uploads/2018/11/Lion-air-e1541663580577.jpg", - "date_published": "2018-11-08", - "description": "A crucial sensor was replaced on a Lion Air jet the day before it plunged into the Java Sea, and that sensor replacement may have exacerbated other problems with the plane, Indonesian investigators said on Wednesday.\nThat sensor, known as the “angle of attack” sensor, keeps track of the angle of the aircraft nose to help prevent the plane from stalling and diving.", - "epoch_date_submitted": 1559347200, - "image_url": "https://www.iafrica.com/wp-content/uploads/2018/11/Lion-air-e1541663580577.jpg", - "inputs_outputs": null, - "language": "en", - "report_number": 380, - "source_domain": "iafrica.com", - "submitters": ["Catherine Olsson"], - "tags": [], - "text": "A crucial sensor was replaced on a Lion Air jet the day before it plunged into the Java Sea, and that sensor replacement may have exacerbated other problems with the plane, Indonesian investigators said on Wednesday.\n\nThat sensor, known as the “angle of attack” sensor, keeps track of the angle of the aircraft nose to help prevent the plane from stalling and diving.\n\nEarlier this week, Indonesian officials hinted that airspeed indicators played a role in the deadly October 29 crash that killed all 189 people on board.\n\nThe jet’s airspeed indicator malfunctioned on its last four flights, and that problem was related to the sensor issue, said Soerjanto Tjahjono, chairperson of Indonesia’s National Transportation Safety Committee, on Wednesday.\n\nLion Air’s first two attempts to address the airspeed indicator problem didn’t work, and for the Boeing 737 MAX 8 plane’s second-to-last flight on October 28, the angle of attack sensors were replaced, Tjahjono said.\n\nOn the October 28 flight, from Bali to Jakarta, the pilot’s and co-pilot’s sensors disagreed. The 2-month-old plane went into a sudden dive minutes after takeoff, which the pilots were able to recover from. They decided to fly on to Jakarta at a lower-than-normal altitude.\n\nThe next day, during the deadly crash, the plane hit the water at very high speed just 13 minutes after takeoff from Jakarta. Its flight crew had requested permission to return to the airport several minutes after taking off.\n\n“The point is that after the AOA (sensor) is replaced, the problem is not solved but the problem might even increase. Is this fatal? NTSC (National Transportation Safety Committee) wants to explore this,” he said.\n\nEven if an angle of attack sensor on a jet is faulty, there’s generally a backup system in place for the critical component, and pilots are trained to handle a plane safely if those sensors fail, airline safety experts said.\n\nThere are audio signals and physical warnings that can alert the pilot to malfunctioning equipment or other dangers, said Todd Curtis, director of the Airsafe.com Foundation.\n\n“They should have been completely engaged in what was going on inside that cockpit, and any kind of warning that came up, they would have been wise to pay attention to it,” Curtis said.\n\nInvestigators are likely focused on how a single sensor’s failure resulted in a faulty command that didn’t take into account information from a second sensor, said John Cox, CEO of Safety Operating Systems.\n\n“We don’t know what the crew knew and didn’t know yet,” Cox said. “We will.”\n\nA new procedure release\n\nBoeing, which manufactured the Lion Air plane, issues safety-related bulletins, and had previously circulated instructions about what flight crews should do if sensors fail.\n\nIndonesia’s National Transportation Safety Committee said it had agreed with Boeing on procedures that the airplane manufacturer should distribute globally on how flight crews can deal with “angle of attack” sensor problems.\n\nBut a Boeing statement said a safety bulletin, sent to airlines on Tuesday, directs flight crews to existing guidelines on how they should respond to erroneous “angle of attack” data. It wasn’t immediately clear if it plans an update, though comments from Indonesian officials indicate they expect one.\n\nIndonesian investigators said their flight procedure recommendations to Boeing were based on how the flight crew responded to problems on the Bali-to-Jakarta flight.\n\n“The draft of what will be conveyed by Boeing this morning has been presented to us,” said air accident investigator Nurcahyo Utomo.\n\n“There are some things that we ask for explanation and some that we ask to be removed, and there has been an agreement between the NTSC and Boeing to release a new procedure to all Boeing 737 MAX users in the world,” he said.\n\nIndonesia’s search and rescue agency on Wednesday extended the search effort for a second time, saying it will continue until Sunday. Body parts are still being recovered and searchers continue to hunt for the cockpit voice recorder.\n\nThe Lion Air crash is the worst airline disaster in Indonesia since 1997, when 234 people died on a Garuda flight near Medan. In December 2014, an AirAsia flight from Surabaya to Singapore plunged into the sea, killing all 162 on board.\n\nLion Air is one of Indonesia’s youngest airlines but has grown rapidly, flying to dozens of domestic and international destinations. It has been expanding aggressively in Southeast Asia, a fast-growing region of more than 600 million people.\n\nNews24", - "title": "Lion Air Plane Crash: Crucial 'Angle Of Attack' Sensor Replaced Before Crash", - "url": "https://www.iafrica.com/lion-air-plane-crash-crucial-angle-of-attack-sensor-replaced-before-crash/" - }, - { - "authors": ["CGTN Live"], - "cloudinary_id": "reports/news.cgtn.com/news/3d3d674e3163444e30457a6333566d54/img/0b16c22bf73a4262b045df07e92b840c/0b16c22bf73a4262b045df07e92b840c.jpg", - "date_published": "2018-11-10", - "description": "Indonesia's National Transportation Safety Committee said on Wednesday that a crucial sensor had been replaced on a Lion Air jet the day before it plunged into the Java Sea, killing all 189 people on board.\n\nThat sensor, known as the \"angle of attack\" (AOA) sensor, keeps track of the angle of the aircraft nose to help prevent the plane from stalling and diving.", - "epoch_date_submitted": 1559347200, - "image_url": "https://news.cgtn.com/news/3d3d674e3163444e30457a6333566d54/img/0b16c22bf73a4262b045df07e92b840c/0b16c22bf73a4262b045df07e92b840c.jpg", - "inputs_outputs": null, - "language": "en", - "report_number": 379, - "source_domain": "news.cgtn.com", - "submitters": ["Catherine Olsson"], - "tags": [], - "text": "Indonesia's National Transportation Safety Committee said on Wednesday that a crucial sensor had been replaced on a Lion Air jet the day before it plunged into the Java Sea, killing all 189 people on board.\n\nThat sensor, known as the \"angle of attack\" (AOA) sensor, keeps track of the angle of the aircraft nose to help prevent the plane from stalling and diving.\n\nExperts say the sensor is a crucial parameter that helps the aircraft's computers understand whether its nose is too high relative to the current of air. If the sensor fails to send correct information, it can confuse both the aircraft's computer and its pilots, throwing an aircraft into an aerodynamic stall and making it fall.\n\n\nAn Indonesian National Transportation Safety Commission official examines a turbine engine from the crashed Lion Air flight JT610 in Jakarta, Indonesia, November 4, 2018. /VCG Photo\n\nEarlier this week, Indonesian officials hinted that airspeed indicators played a role in Lion Air crash. One of the black boxes showed that the airspeed indicator on the jet malfunctioned on its last four flights, and that problem was related to the sensor issue, said Soerjanto Tjahjono, chairman of Indonesia's National Transportation Safety Committee, on Wednesday.\n\nLion Air's first two attempts to address the airspeed indicator problem didn't work, and the AOA sensors were not replaced until the Boeing 737 MAX 8 plane's second-to-last flight on October 28, said Tjahjono.\n\nOn the October 28 flight, from Bali to Jakarta, the pilot's and copilot's sensors disagreed. The 2-month-old plane went into a sudden dive minutes after takeoff, which the pilots were able to recover from. They decided to fly to Jakarta at a lower-than-normal altitude.\n\n\nWorkers load up recovered debris and belongings believed to be from crashed Lion Air flight JT610. /VCG Photo\n\nThe next day, during the deadly crash, the plane hit the water at very high speed just 13 minutes after takeoff from Jakarta. Its flight crew had requested permission to return to the airport several minutes after taking off.\n\n\"The point is that after the AOA sensor is replaced, the problem is not solved but the problem might even increase. Is this fatal? NTSC (National Transportation Safety Committee) wants to explore this,\" he said.\n\nWarnings must not be ignored\n\nAirline safety experts said the aircraft usually have a backup system that responds to sensor faults, and pilots are trained to handle a plane safely if those sensors fail.\n\nThere are audio signals and physical warnings that can alert the pilot to malfunctioning equipment or other dangers, said Todd Curtis, director of the Airsafe.com Foundation.\n\n\"They should have been completely engaged in what was going on inside that cockpit, and any kind of warning that came up, they would have been wise to pay attention to it,\" Curtis said.\n\n\nRelatives of the victims of the Lion Air crash sprinkle flowers at the crash site. /AP Photo\n\nInvestigators are likely focused on how a single sensor's failure resulted in a faulty command that didn't take into account information from a second sensor, said John Cox, CEO of Safety Operating Systems.\n\n\"We don't know what the crew knew and didn't know yet,\" Cox said, adding \"We will.\"\n\nThe Boeing 737 MAX has three such AOA sensors, Reuters quoted an informed source.\n\nFAA says sensor problem detected in 246 Boeing 737 MAX airplanes worldwide\n\nThe US Federal Aviation Administration (FAA) on Wednesday issued an emergency airworthiness directive on 246 Boeing 737 Max airplanes worldwide, of which 45 airplanes in the US are operated by Southwest Air Co, United Airlines and American Airlines Group Inc.\n\nIt warned airlines that erroneously input from an AOA sensor could cause \"repeated nose-down trim commands of the horizontal stabilizer.\" If this condition is not addressed, it could lead the flight crew to have difficulty controlling the airplane.\n\n\nA Boeing 737 MAX parked outside the hangar. /VCG Photo\n\n\"We are issuing this airworthiness directive because we evaluated all the relevant information and determined the unsafe condition is likely to exist or develop in other products of the same type design,\" said the FAA.\n\nThe directive dictates that operators \"revise the airplane flight manual to give the flight crew horizontal stabilizer trim procedures to follow under some conditions.\"\n\nBoeing 737 Max jet is the latest version of the US aircraft maker, which has been in service for just over a year. And the Lion Air crash was the first involving the new jet.\n\n(With inputs from AP)", - "title": "Indonesian officials: Problems with sensor found on crashed Lion Air jet", - "url": "https://news.cgtn.com/news/3d3d674e3163444e30457a6333566d54/share_p.html" - }, - { - "authors": ["Alan Levin", "Julie Johnsson", "Harry Suhartono"], - "cloudinary_id": "reports/www.insurancejournal.com/app/uploads/2018/11/Bloomberg-Graphic_Top-Boeing-Max-Customers_08Nov2018-580x517.png", - "date_published": "2018-11-08", - "description": "A Boeing Co. warning to 737 Max operators around the globe provides the first clues about how bad data from an airflow sensor might have contributed to", - "epoch_date_submitted": 1559347200, - "image_url": "https://www.insurancejournal.com/app/uploads/2018/11/Bloomberg-Graphic_Top-Boeing-Max-Customers_08Nov2018-580x517.png", - "inputs_outputs": null, - "language": "en", - "report_number": 378, - "source_domain": "insurancejournal.com", - "submitters": ["Catherine Olsson"], - "tags": [], - "text": "A Boeing Co. warning to 737 Max operators around the globe provides the first clues about how bad data from an airflow sensor might have contributed to the deadly crash of an Indonesian airliner last week.\n\nThe bulletin and statements by Indonesian investigators suggest that the pilots on the Lion Air 737 Max 8 were battling the plane as its computers commanded a steep dive during its final moments of flight.\n\nBoeing cautioned that the so-called angle-of-attack sensor can provide false readings in limited circumstances — such as when a plane’s autopilot is switched off — that cause the 737 Max to pitch nose downward. The sensor malfunction can essentially trick the plane into pointing its nose down to gain the speed it thinks it needs to keep flying.\n\nThe Boeing directive doesn’t call for operators to conduct new inspections or take other action. It merely stressed that pilots should follow procedures in the flight manual when encountering erroneous data. Following the protocol should be routine for pilots, though may be more challenging in the heat of the moment when equipment is malfunctioning and alarms are sounding.\n\nAmerican aviation regulators followed by issuing an emergency order Wednesday requiring that airlines follow Boeing’s instructions and add information to pilot manuals showing how to diagnose the problem and respond. Carriers will have three days to update their manuals under the order, issued by the Federal Aviation Administration, according to an emailed statement.\n\nThe FAA said the problem “could cause the flight crew to have difficulty controlling the airplane, and lead to excessive nose-down attitude, significant altitude loss, and possible impact with terrain.”\n\nThe sensor on the Lion Air jet been replaced the day before after it failed on a previous trip, the Indonesia National Transportation Safety Committee said in a briefing Wednesday. The malfunction can cause the plane’s computers to erroneously detect a mid-flight stall in airflow, causing the aircraft to abruptly dive to regain the speed it needs to keep flying.\n\nOn a previous flight from Bali to Jakarta, the angle-of-attack sensor feeding the captain’s flight displays registered a 20-degree difference from the device on the copilot’s side of the cockpit, the committee said. Pilots on that flight were able to compensate.\n\nAn angle-of-attack sensor that had been removed before that previous flight has been brought to the investigators and will be examined in the U.S., the Indonesian officials said.\n\nIt’s still possible the FAA may order Boeing to redesign the equipment or software as investigators piece together details of the Oct. 29 crash, which killed 189 people. The agency said it “will take further appropriate actions depending on the results of the investigation.”\n\nFresh Questions\n\nThe new information about Lion Air Flight 610 raises multiple questions investigators will want to examine on the pilots’ actions, how flight crews were trained and whether the maintenance performed on the system was adequate, said Roger Cox, a former NTSB investigator.\n\n“I would definitely be looking at the man-machine interface and how pilots respond,” said Cox, a former airline pilot who flew earlier versions of the 737 and specialized at the NTSB in cockpit actions.\n\nOne of the puzzling things about the accident is that the plane was flying in clear skies during daylight, so pilots should have been able to handle the problems they faced with airspeed and erroneous sensors, Cox said. However, in rare instances, accidents have been caused by what investigators call a “startle factor.”\n\n“If you don’t take the appropriate action because you’re surprised, you can make a serious error,” he said.\n\nThe pilots union at Southwest Airlines Co., the biggest customer of the 737 Max, hasn’t received any reports from its members of problems with faulty sensor readings, said Jon Weaks, president of the Southwest Airlines Pilots Association.\n\nThe airline confirmed that it hasn’t experienced any of the sensor troubles and said its 26 Max remain operational and no schedule disruption is expected.\n\nUnited Technologies Corp. supplies the angle-of-attack sensors and indicator for the 737 Max, according to Airframers.net. The company didn’t respond to requests for comment. Honeywell International Inc. provides the air data inertial reference unit.\n\nThe Lion Air jetliner plunged into the Java Sea minutes after takeoff from Jakarta airport, nosing downward so suddenly that it may have hit speeds of 600 miles an hour before slamming into the water.\n\nMoments earlier, the pilots radioed a request to return to Jakarta, but never turned back toward the airport, according to Indonesia’s safety commission and flight-tracking data. The agency said the pilots were dealing with an erroneous airspeed indication.\n\nBoeing, which is headquartered in Chicago, said it is cooperating fully and providing technical assistance as the investigation continues. Boeing’s shares rose 1.5 percent to $372.02 at", - "title": "New Clues to Lion Air 737 Max Crash Revealed in Boeing, FAA Warnings", - "url": "https://www.insurancejournal.com/news/international/2018/11/08/507060.htm" - }, - { - "authors": ["Australian Broadcasting Corporation"], - "cloudinary_id": "reports/www.abc.net.au/news/image/10464216-16x9-700x394.jpg", - "date_published": "2018-11-08", - "description": "A crucial sensor that is the subject of a Boeing safety bulletin was replaced on a Lion Air jet the day before it plunged into the Java Sea and possibly worsened other problems with the plane, Indonesian investigators say.", - "epoch_date_submitted": 1559347200, - "image_url": "https://www.abc.net.au/news/image/10464216-16x9-700x394.jpg", - "inputs_outputs": null, - "language": "en", - "report_number": 377, - "source_domain": "abc.net.au", - "submitters": ["Catherine Olsson"], - "tags": [], - "text": "Lion Air plane had angle of attack sensor replaced prior to crash; Boeing issues safety reminder\n\nUpdated\n\nA crucial sensor that is the subject of a Boeing safety bulletin was replaced on a Lion Air jet the day before it plunged into the Java Sea and possibly worsened other problems with the plane, Indonesian investigators have revealed.\n\nKey points: Boeing issues safety bulletin reminding pilots how to handle erroneous data from key sensor\n\nThe angle of attack sensors were replaced on the plane's second-to-last flight\n\nChairman of Indonesia's NTSC says airspeed indicator malfunctions were intertwined with the sensor issue\n\nIndonesia's National Transportation Safety Committee (NTSC) said it had agreed with Boeing on procedures that the airplane manufacturer should distribute globally on how flight crews can deal with \"angle of attack\" sensor problems following the October 29 crash that killed all 189 people on board.\n\nExperts say the angle of attack is a crucial parameter that helps the aircraft's computers understand whether its nose is too high relative to the current of air.\n\nThe sensor keeps track of the angle of the aircraft nose relative to oncoming air to prevent the plane from stalling and diving.\n\nBut a Boeing statement said a safety bulletin, sent to airlines this week, directs flight crews to existing guidelines on how they should respond to erroneous \"angle of attack\" data.\n\nIt was not immediately clear whether Boeing was planning to update the guidelines, though comments from Indonesian officials indicate they expect so.\n\nAirspeed indicator malfunction and sensor issue linked\n\nTransport safety committee chairman Soerjanto Tjahjono said airspeed indicator malfunctions on the jet's last four flights, which were revealed by an analysis of the flight data recorder, were intertwined with the sensor issue.\n\n\"The point is that after the AOA [sensor] is replaced the problem is not solved, but the problem might even increase. Is this fatal? NTSC wants to explore this,\" he said.\n\nLion Air's first two attempts to address the airspeed indicator problem did not work, and for the Boeing 737 MAX 8 plane's second-to-last flight on October 28, the angle of attack sensors were replaced, Mr Tjahjono said.\n\nOn that flight, from Bali to Jakarta, the pilot's and co-pilot's sensors disagreed.\n\nThe two-month-old plane went into a sudden dive minutes after take-off, which the pilots were able to recover from. They decided to fly on to Jakarta at a lower-than-normal altitude.\n\nIndonesian investigators said their flight procedure recommendations to Boeing were based on how the flight crew responded to problems on the Bali to Jakarta flight.\n\n\"The draft of what will be conveyed by Boeing this morning has been presented to us,\" air accident investigator Nurcahyo Utomo said.\n\n\"There are some things that we ask for explanation and some that we ask to be removed, and there has been an agreement between NTSC and Boeing to release a new procedure to all Boeing 737 MAX users in the world.\"\n\nIndonesia's search and rescue agency extended the search effort on Wednesday for a second time, saying it would continue until Sunday.\n\nBody parts are still being recovered and searchers continue to hunt for the cockpit voice recorder.\n\nThe plane hit the water at very high speed just 13 minutes after take-off from Jakarta. Its flight crew had requested permission to return to the airport several minutes after taking off.\n\nThe Lion Air crash is the worst airline disaster in Indonesia since 1997, when 234 people died on a Garuda flight near Medan.\n\nIn December 2014, an AirAsia flight from Surabaya to Singapore plunged into the sea, killing all 162 on board.\n\nLion Air is one of Indonesia's youngest airlines but has grown rapidly, flying to dozens of domestic and international destinations. It has been expanding aggressively in South-East Asia, a fast-growing region of more than 600 million people.\n\nAP/Reuters\n\nTopics: disasters-and-accidents, air-and-space, indonesia, asia\n\nFirst posted", - "title": "Lion Air plane had angle of attack sensor replaced prior to crash; Boeing issues safety reminder", - "url": "https://www.abc.net.au/news/2018-11-08/lion-air-flight-had-crucial-sensor-replaced-prior-to-fatal-crash/10475468" - }, - { - "authors": ["Grant Bradley"], - "cloudinary_id": "reports/www.nzherald.co.nz/resizer/yF7-HI8O2KRMMtki3GBhsuDKm-w=/1200x0/smart/filters:quality(70)/arc-anglerfish-syd-prod-nzme.s3.amazonaws.com/public/TJU32NCPOJFSVJO4SLPNBPNMQ4.jpg", - "date_published": "2018-10-30", - "description": "Faulty sensing equipment on planes has been linked to several crashes", - "epoch_date_submitted": 1559347200, - "image_url": "https://www.nzherald.co.nz/resizer/yF7-HI8O2KRMMtki3GBhsuDKm-w=/1200x0/smart/filters:quality(70)/arc-anglerfish-syd-prod-nzme.s3.amazonaws.com/public/TJU32NCPOJFSVJO4SLPNBPNMQ4.jpg", - "inputs_outputs": null, - "language": "en", - "report_number": 376, - "source_domain": "nzherald.co.nz", - "submitters": ["Catherine Olsson"], - "tags": [], - "text": "Possible faulty equipment that led to the crash of Lion Air Flight 610 was the same sort of sensing gear that contributed to the crash of an Air New Zealand A320 off Perpignan 10 years ago.\n\nA failure of air flow monitoring pitot tubes on board the Lion Air Boeing 737 has been cited as a possible contributor to yesterday's crash off Indonesia which killed 189 people.\n\nA report in the New York Times says the erratic flight path before the high speed plunge indicated a problem with the pressure sensitive instruments near the nose of the plane.\n\n\"The erratic flight path makes us suspect a problem with the pitot-static system,\" said Gerry Soejatman, an Indonesian aviation expert.\n\nData from the flight indicated an ''erratic climb and ground speed problem\", leading him to suspect a problem with the instruments had also been an issue then.\n\nAdvertisement\n\nSeveral plane crashes have been blamed on blockages or other problems with pitot tubes which resulted in erroneous speed or altitude readings, the Times reported.\n\nIn November 2008 an Airbus A320 that had been leased by Air New Zealand to a European carrier crashed, killing two German pilots and five New Zealanders on board.\n\nThe plane had been on a routine test flight, prior to being handed back to Air New Zealand by XL Airways Germany, a charter company that had been leasing it for the previous two years.\n\nAn inquiry found that the crash was triggered by a test that was conducted at low speed and low altitude, throwing the plane into a stall from which it failed to recover.\n\nThe ill-fated manoeuvre arose especially from makeshift preparations for the exercise and poor coordination between the German and New Zealand crew on board, the Bureau d'Enquetes et d'Analyses (BEA) said after a 22-month inquiry.\n\nBut compounding their mistake was a malfunction in two out of three external probes that feed the A320's complex computerised flight system with vital data about air flow.\n\nThe plane had been repainted and rinsed by a French maintenance company three days before the test, the investigation found. Water entered these so-called angle of attack (AOA) sensors, causing them to freeze and thus skewing the avionics.\n\nA year later, in 2009, a Rio-to-Paris Air France flight disappeared over the Atlantic with the loss of all 228 people on board. An inquiry found that the plane flew into a thunderstorm, which froze the crucial speed sensors, resulting in the pilots flying blind and leading to a mid-air stall.\n\nIndonesian actress Conchita Caroline had concerns about the doomed Lion Air plane after flying on it the day before. Photo / Supplied\n\nToday a passenger who was on the ill-fated Lion Air aircraft a day before the crash outlined concerns about the plane.\n\nIndonesian actress and TV presenter Conchita Caroline said her flight from Bali to Jakarta struck problems.\n\nAs the plane readied for takeoff, an engine seemed to die several times, the air conditioning was faulty and the floor beneath her felt hot to touch.\n\nLion Air's chief executive Edward Surat said yesterday there was a report of a technical issue from that fight but that had been resolved ''according to procedure\" before Flight 610 took off.\n\nFollowing the crash Boeing's stock price fell by almost 7 per cent. Lion Air is an important customer for its 737s.", - "title": "Possible fault in Lion Air plane similar to a cause of Air New Zealand Airbus A320 France crash", - "url": "https://www.nzherald.co.nz/business/news/article.cfm?c_id=3&objectid=12151363" - }, - { - "authors": ["USA Today Editorial Board"], - "cloudinary_id": "reports/www.gannett-cdn.com/presto/2018/12/04/USAT/dfc4ad2e-d645-4d86-94ad-02c1f63b6f74-Boeing.ourview.JPG?crop=1161,657,x0,y0&width=3200&height=1680&fit=bounds", - "date_published": "2018-12-05", - "description": "Why did the 737 Max plunge into the sea in Indonesia, killing all 189 on board? Pilots must know about every change on the jets they fly: Our view", - "epoch_date_submitted": 1559347200, - "image_url": "https://www.gannett-cdn.com/presto/2018/12/04/USAT/dfc4ad2e-d645-4d86-94ad-02c1f63b6f74-Boeing.ourview.JPG?crop=1161,657,x0,y0&width=3200&height=1680&fit=bounds", - "inputs_outputs": null, - "language": "en", - "report_number": 375, - "source_domain": "usatoday.com", - "submitters": ["Catherine Olsson"], - "tags": [], - "text": "Why did the 737 Max plunge into the sea in Indonesia, killing all 189 on board? Pilots must know about every change on the jets they fly: Our view\n\nIndonesians recover a plastic box containing the data recorder of Lion Air Flight 610 on Nov. 1, 2018. (Photo: Pradita Utama, epa-EFE)\n\n“Know your airplane, fly your airplane” is a bedrock principle for pilots.\n\nBut pilots can’t know their airplane if the maker fails to disclose a new emergency feature in its flight operations manual, as happened with the Boeing 737 Max.\n\nOn Oct. 29, the feature, known by the acronym MCAS, relentlessly pushed down the nose of Indonesia’s Lion Air Flight 610 more than 20 times before the jet plunged into the Java Sea, killing all 189 on board.\n\nPilots struggled against the new system, which is designed to prevent a stall by automatically pushing the nose down. Tragically, on Flight 610 it was apparently triggered by a sensor delivering a false reading. There was no stall.\n\nThe 737 Max, Boeing's newest version of the workhorse 737 series, is flown by airlines around the world, including American, Southwest and United. Hundreds more are on order. Since the Lion Air crash, American and Southwest pilots have complained about Boeing’s failure to communicate.\n\nBOEING: Safety will always be a core value\n\n“The key to any emergency is identifying the system that is betraying you or failed,” says Dennis Tajer, a veteran 737 captain and spokesman for the Allied Pilots Association, which represents pilots at American.\n\n“We did not know about the system\" until after the Lion Air crash. It was not on previous versions of the 737. Jon Weaks, a Southwest captain and president of Southwest Airlines Pilots Association, put it simply: \"We should have known the system existed.\"\n\nWith all the safety built into today’s jetliners, it typically takes a cascading series of events to bring down a plane. Based on a preliminary report released last week by Indonesian authorities and questions raised by safety experts, the final flight of Lion Air 610 is a textbook example of multiple failures.\n\nInvestigators will need to get to the bottom of several issues involving:\n\n►Boeing. A potential design or manufacturing flaw might have allowed a single sensor, with an erroneous reading, to trigger MCAS — which stands for Maneuvering Characteristics Augmentation System — and the nose-down movements. Two sensors on the jet were measuring what’s known as angle of attack, “one giving you an accurate reading, one inaccurate,” says former National Transportation Safety Board Chair Mark Rosenker. “The problem was the inaccurate one appeared to take over.” He questioned why a single malfunctioning sensor could create a situation potentially leading to a catastrophic failure. The answer might be that it shouldn't.\n\nWithin days of the crash, Boeing issued a bulletin telling 737 Max pilots to deal with erroneous sensor data and nose-down movements by turning off the automatic system, in accordance with “existing procedures.” A Federal Aviation Administration directive followed, warning that erroneous readings could cause “difficulty controlling the airplane” and “possible impact with terrain.”\n\n►Lion Air. The day before the crash, Lion Air maintenance replaced the sensor on the same plane. But pilots got erroneous readings on that flight and experienced a nearly identical problem to the one on Flight 610. Was the sensor installed properly by company crews? Based on the serious problems on Oct. 28, should the plane have been grounded before the fatal flight?\n\n►Flight 610 pilots. On the Oct. 28 flight, pilots initially reacted to the nose-down movements the same way as pilots on Flight 610 the next day. When that didn’t work, they used two switches to cut off the system — a standard emergency procedure. It is baffling that the pilots on Flight 610 failed to do the same thing. Perhaps faced by an inaccurate reading from a faulty sensor and a system they likely knew nothing about, they were confused in the emergency. Perhaps they were not well-trained. For now, that remains a mystery.\n\nThe causes of the crash will be determined by Indonesian authorities and the U.S. National Transportation Safety Board, which has an interest in keeping American fleets safe.\n\nBoeing issued a statement asserting, “We are confident in the safety of the 737 MAX.\" A company spokesman added that the “function performed by MCAS is referenced” in the flight manual, and “existing procedures” to deal with it are documented. The chairman of the United Airlines branch of the Airline Pilots Association echoed that statement, breaking with two other unions and his own union leadership.\n\nBackup systems, what the industry calls redundancy, are designed to keep planes in the air if one component fails. The ultimate safety backups are the pilots — who deserve to know about every change on the aircraft they fly.\n\nUSA TODAY's editorial opinions are decided by its Editorial Board, separate from the news staff. Most editorials are coupl", - "title": "What we've got here is a failure to communicate", - "url": "https://www.usatoday.com/story/opinion/2018/12/04/lion-air-what-weve-got-here-failure-communicate-editorials-debates/2192044002/" - }, - { - "authors": ["Dominic Gates"], - "cloudinary_id": "reports/static.seattletimes.com/wp-content/uploads/2018/11/11272018_Black-box-data_121412-1200x630.jpg", - "date_published": "2018-11-27", - "description": "Data from the fatal Oct. 29 flight that killed 189 people, and from the prior day's flight of the same jet, raises questions about three factors that seem to have...", - "epoch_date_submitted": 1559347200, - "image_url": "https://static.seattletimes.com/wp-content/uploads/2018/11/11272018_Black-box-data_121412-1200x630.jpg", - "inputs_outputs": null, - "language": "en", - "report_number": 374, - "source_domain": "seattletimes.com", - "submitters": ["Catherine Olsson"], - "tags": [], - "text": "Data from the fatal Oct. 29 flight that killed 189 people, and from the prior day's flight of the same jet, raises questions about three factors that seem to have contributed to the crash.\n\nA key instrument reading on Lion Air flight JT610 was faulty even as the pilots taxied out for takeoff. As soon as the Boeing 737 MAX was airborne, the captain’s control column began to shake as a stall warning.\n\nAnd from the moment they retracted the wing flaps at about 3,000 feet, the two pilots struggled — in a 10-minute tug of war — against a new anti-stall flight-control system that relentlessly pushed the jet’s nose down 26 times before they lost control.\n\nThough the pilots responded to each nose-down movement by pulling the nose up again, mysteriously they didn’t do what the pilots on the previous day’s flight had done: simply switched off that flight-control system.\n\nThe detail is revealed in the data from the so-called “black box” flight recorder (it’s actually orange in color) from the fatal Oct. 29 flight that killed 189 people and the prior day’s flight of the same jet, presented last Thursday to the Indonesian Parliament by the country’s National Transportation Safety Committee (NTSC).\n\nThis data is the major basis for the preliminary crash-investigation report that was made public Wednesday in Indonesia, Tuesday evening in Seattle.\n\nThe flight-recorder data is presented as a series of line graphs that give a clear picture of what was going on with the aircraft systems as the plane taxied on the ground, took off and flew for just 11 minutes.\n\nThe data points to three factors that seem to have contributed to the disaster:\n\nA potential design flaw in Boeing’s new anti-stall addition to the MAX’s flight-control system and a lack of communication to airlines about the system.\n\nThe baffling failure of the Lion Air pilots to recognize what was happening and execute a standard procedure to shut off the faulty system.\n\nAnd a Lion Air maintenance shortfall that allowed the plane to fly repeatedly without fixing the key sensor that was feeding false information to the flight computer on previous flights.\n\nAnti-stall system triggered\n\nPeter Lemme, a former Boeing flight-controls engineer who is now an avionics and satellite-communications consultant, analyzed the graphs minute by minute.\n\nHe said the data shows Boeing’s new system — called MCAS (Maneuvering Characteristics Augmentation System) — “was triggered persistently” as soon as the wing flaps retracted.\n\nThe data confirms that a sensor that measures the plane’s angle of attack, the angle between the wings and the air flow, was feeding a faulty reading to the flight computer. The two angle-of-attack sensors on either side of the jet’s nose differed by about 20 degrees in their measurements even during the ground taxi phase when the plane’s pitch was level. One of those readings was clearly completely wrong.\n\nOn any given flight, the flight computer takes data from only one of the angle-of-attack (AOA) sensors, apparently for simplicity of design. In this case, the computer interpreted the AOA reading as much too high an angle, suggesting an imminent stall that required MCAS to kick in and save the airplane.\n\nWhen the MCAS system pushed the nose down, the captain repeatedly pulled it back up, probably by using thumb switches on the control column. But each time, the MCAS system, as designed, kicked in to swivel the horizontal tail and push the nose back down again.\n\nThe data shows that after this cycle repeated 21 times, the captain ceded control to the first officer and MCAS then pushed the nose down twice more, this time without a pilot response.\n\nAfter a few more cycles of this struggle, with the horizontal tail now close to the limit of its movement, the captain resumed control and pulled back on the control column with high force.\n\nIt was too late. The plane dived into the sea at more than 500 miles per hour.\n\nPrevious crew handled similar situation\n\nRemarkably, the corresponding black-box-data charts from the same plane’s flight the previous day show that the pilots on that earlier flight encountered more or less exactly the same situation.\n\nAgain the AOA sensors were out of sync from the start. Again, the captain’s control column began shaking, a stall warning, at the moment of takeoff. Again, MCAS kicked in to push the nose down as soon as the flaps retracted.\n\nInitially that crew reacted like the pilots of JT610, but after a dozen cycles of the nose going down and pushing it back up, they turned off MCAS using two standard cutoff switches on the control pedestal “within minutes of experiencing the automatic nose down” movements, according to the NTSC preliminary investigation report.\n\nThere were no further uncommanded nose-down movements. For the rest of the flight, they controlled the jet’s pitch manually and everything was normal. The jet continued to its destination and landed safely.\n\nBecause the cockpit voice recorder has not yet been recovered from the sea bed, it’s a myste", - "title": "Pilots struggled against Boeing’s 737 MAX control system on doomed Lion Air flight", - "url": "https://www.seattletimes.com/business/boeing-aerospace/black-box-data-reveals-lion-air-pilots-struggle-against-boeings-737-max-flight-control-system/" - }, - { - "authors": ["Bloomberg"], - "cloudinary_id": "reports/www.businesstimes.com.sg/sites/default/files/styles/article_img_retina/public/image/2018/11/28/gn-2711-lionair_0.jpg?itok=4QuG887p", - "date_published": "2018-11-28", - "description": "[JAKARTA] A malfunctioning sensor at the centre of the investigation into the Oct 29 crash of a Lion Air jetliner into the Java Sea wasn't repaired before the fatal flight even though it had failed on the plane's previous trip, according to a preliminary investigative report. . Read more at The Business Times.", - "epoch_date_submitted": 1559347200, - "image_url": "https://www.businesstimes.com.sg/sites/default/files/styles/article_img_retina/public/image/2018/11/28/gn-2711-lionair_0.jpg?itok=4QuG887p", - "inputs_outputs": null, - "language": "en", - "report_number": 373, - "source_domain": "businesstimes.com.sg", - "submitters": ["Catherine Olsson"], - "tags": [], - "text": "A malfunctioning sensor at the centre of the investigation into the Oct 29 crash of a Lion Air jetliner into the Java Sea wasn't repaired before the fatal flight even though it had failed on the plane's previous trip, according to a preliminary investigative report.\n\n[JAKARTA] A malfunctioning sensor at the centre of the investigation into the Oct 29 crash of a Lion Air jetliner into the Java Sea wasn't repaired before the fatal flight even though it had failed on the plane's previous trip, according to a preliminary investigative report.\n\nA mechanic worked on other sensors and equipment during a night shift before the early morning departure, but not on the so-called angle-of-attack vane, according to Indonesia's National Transportation Safety Committee. Portions of the committee's report were viewed by Bloomberg News before a scheduled release on Wednesday.\n\nThe report doesn't offer a cause for the accident but provides the most detailed look so far into the chaotic minutes before the crash and into the steps that were taken to address malfunctions that occurred on the plane the previous night. On both flights, pilots reported that they had difficulty figuring out basic information such as speed and altitude.\n\nThe pilots on Flight 610, which plunged into the Java Sea more than 11 minutes after it took off from Jakarta, appeared not to understand what was happening to them as they radioed air traffic controllers asking for their altitude and speed. They said they had an unspecified \"flight control problem,\" according to the report. Lion Air spokesman Danang Prihantoro didn't answer calls seeking comments when tried on his mobile phone on Wednesday.\n\nsentifi.com Market voices on:\n\nEveryone aboard died in the crash, although there are conflicting counts for the death toll. A weight and balance calculation lists 188 people - two pilots, five flight attendants and 181 passengers. Another listing known as a voyage report lists an extra flight attendant for a total of 189 people.\n\nThe Boeing Co 737 Max 8's angle-of-attack sensor, which measures how high or low the plane's nose was pointed relative to the oncoming air, had malfunctioned on the previous flight as well as in the minutes before the crash, according to the report. The sensor erroneously concluded the nose was pointed too high and the aircraft was in danger of losing lift, prompting a stall warning in the cockpit and triggering safety software that attempted to put them into a dive.\n\nThe two sets of pilots reacted differently to the multiple errors messages and malfunctions. On the previous flight, the pilots were able to shut off the motor that was trying to push down the nose relatively soon after taking off.\n\nFor reasons that haven't been explained, the pilots on Flight 610 didn't take that step - which is part of a long-standing emergency procedure. The plane's crash-proof cockpit recorder hasn't been recovered, so investigators don't have much insight into what they were thinking as they responded to the emergency.\n\nBoeing declined to comment on the preliminary report. It is working as a technical adviser to the Indonesian investigation.\n\n\"We will analyze any additional information as it becomes available,\" the aircraft maker said in a statement on Tuesday. \"We are taking every measure to fully understand all aspects of this accident, working closely with the investigation team and the relevant government authorities.\"\n\nEarlier this month the manufacturer issued a bulletin to operators of the Max reminding them that such a cascade of failures could be addressed by an existing emergency procedure. The manufacturer has said it's confident in the safety of the latest version of its 737 model.\n\nEARLIER FLIGHT\n\nOn the Oct 28 flight that landed safely in Jakarta, the captain told investigators that he scanned cockpit instruments and determined that the copilot's readings matched a third standby system and were accurate. He turned over control of the plane to the copilot.\n\nBy contrast, the captain on the flight that crashed radioed a controller about a minute before the plane disappeared from radar to say that all of the plane's altitude gauges were different and they couldn't determine how high they were.\n\nInvestigators will focus on how the airline performed maintenance on the plane.\n\nThe captain on the Oct 28 flight reported problems with speed and altitude readings and with a system called Feel Differential Pressure, which controls the force pilots need to push or pull the control column that raises and lowers the nose, according to the report. There was no indication that the angle of attack sensor was malfunctioning.\n\nAn angle of attack sensor had been replaced and tested just before the Oct 28 flight. According to flight data from the plane, the sensor on the left was reading about 20 degrees differently from the one on the right.\n\nOn both flights, a device known as a stick shaker - a warning that the wings are about to lose lift, which vibrates the control column", - "title": "Faulty Lion Air sensor wasn’t fixed before crash, preliminary report finds", - "url": "https://www.businesstimes.com.sg/transport/faulty-lion-air-sensor-wasn%E2%80%99t-fixed-before-crash-preliminary-report-finds" - }, - { - "authors": ["James Glanz", "Muktita Suhartono", "Hannah Beech"], - "cloudinary_id": "reports/static01.nyt.com/images/2018/11/28/world/28lionair1-promo/28lionair1-promo-facebookJumbo-v2.jpg", - "date_published": "2018-11-28", - "description": "Information from Lion Air Flight 610, which crashed off Indonesia last month, supports the theory that a new Boeing anti-stall system played a role.", - "epoch_date_submitted": 1559347200, - "image_url": "https://static01.nyt.com/images/2018/11/28/world/28lionair1-promo/28lionair1-promo-facebookJumbo-v2.jpg", - "inputs_outputs": null, - "language": "en", - "report_number": 372, - "source_domain": "nytimes.com", - "submitters": ["Catherine Olsson"], - "tags": [], - "text": "Investigators have yet to recover the cockpit voice recorder, which could provide further insight into the steps taken by the pilots and whether they followed the correct procedures.\n\nDespite Boeing’s insistence that the proper procedures were in the handbook, also called the emergency checklist, pilots have said since the accident that Boeing had not been clear about one potentially vital difference between the system on the new 737s and the older models. In the older versions, pilots could help address the problem of the nose being forced down improperly — a situation known as “runaway stabilizer trim” — by pulling back on the control column in front of them, the pilots say.\n\nIn the latest 737 generation, called the Max, that measure does not work, they said, citing information they have received since the crash. The pilots on Lion Air Flight 610 appear to have forcefully pulled back on their control columns to no avail, before the final dive, according to the information from the flight data recorder.\n\nCapt. Dennis Tajer, spokesman for the American Airlines pilot union and a 737 pilot, said he could not comment on any aspect of the investigation. But, he said, “in the previous model of the 737, pulling back on the control column, Boeing says will stop a stabilizer runaway.”\n\nInformation provided to American Airlines from Boeing since the crash, Captain Tajer said, “specifically says that pulling back on the control column in the Max will not stop the runaway if M.C.A.S. is triggered. That is an important difference to know.”\n\nBoeing said in its statement on Tuesday that the existing procedures covered the latest 737 model.\n\nBulletins from Boeing and the Federal Aviation Administration of the United States since the crash indicate that pilots could overcome an incorrectly activated M.C.A.S. with a series of steps. First, they would have had to activate switches on the outside of the control columns in front of both the pilot and co-pilot. Those switches are for electrically controlling the trim — the angle of the stabilizers on the plane’s tail. The pilot of Flight 610 appears to have done that repeatedly to bring the nose up, but the M.C.A.S. reactivated each time, as it was designed to do, forcing the nose back down, and the pilot had to repeat the process again and again.", - "title": "In Indonesia Lion Air Crash, Black Box Data Reveal Pilots’ Struggle to Regain Control", - "url": "https://www.nytimes.com/2018/11/27/world/asia/indonesia-lion-air-crash-.html" - } - ], - "title": "Crashes with Maneuvering Characteristics Augmentation System (MCAS)", - "tsne": { - "x": 0.11154986524731458, - "y": 0.6203396517870035 - } - } - } -} diff --git a/site/gatsby-site/playwright/fixtures/incidents/fullIncident50.json b/site/gatsby-site/playwright/fixtures/incidents/fullIncident50.json deleted file mode 100644 index 0587fba0fc..0000000000 --- a/site/gatsby-site/playwright/fixtures/incidents/fullIncident50.json +++ /dev/null @@ -1,958 +0,0 @@ -{ - "data": { - "incident": { - "AllegedDeployerOfAISystem": [ - { - "__typename": "Entity", - "entity_id": "the-dao", - "name": "The DAO" - } - ], - "AllegedDeveloperOfAISystem": [ - { - "__typename": "Entity", - "entity_id": "the-dao", - "name": "The DAO" - } - ], - "AllegedHarmedOrNearlyHarmedParties": [ - { - "__typename": "Entity", - "entity_id": "dao-token-holders", - "name": "DAO Token Holders" - } - ], - "__typename": "Incident", - "date": "2016-06-17", - "description": "On June 18, 2016, an attacker successfully exploited a vulnerability in The Decentralized Autonomous Organization (The DAO) on the Ethereum blockchain to steal 3.7M Ether valued at $70M.", - "editor_dissimilar_incidents": [], - "editor_notes": null, - "editor_similar_incidents": [], - "editors": [ - { - "__typename": "User", - "first_name": "Sean", - "last_name": "McGregor", - "userId": "619b47ea5eed5334edfa3bbc" - } - ], - "embedding": { - "__typename": "IncidentEmbedding", - "from_reports": [ - 905, - 903, - 902, - 901, - 900, - 899, - 898, - 897, - 896, - 893, - 892, - 889, - 888, - 887, - 886, - 885, - 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} - ], - "title": "The DAO Hack" - } - } -} diff --git a/site/gatsby-site/playwright/fixtures/incidents/incident.json b/site/gatsby-site/playwright/fixtures/incidents/incident.json deleted file mode 100644 index 19868022fe..0000000000 --- a/site/gatsby-site/playwright/fixtures/incidents/incident.json +++ /dev/null @@ -1,294 +0,0 @@ -{ - "data": { - "incident": { - "AllegedDeployerOfAISystem": [ - { - "entity_id": "youtube", - "name": "YouTube" - } - ], - "AllegedDeveloperOfAISystem": [ - { - "entity_id": "youtube", - "name": "YouTube" - } - ], - "AllegedHarmedOrNearlyHarmedParties": [ - { - "entity_id": "children", - "name": "Children" - } - ], - "__typename": "Incident", - "date": "2015-05-19", - "description": "YouTube’s content filtering and recommendation algorithms exposed children to disturbing and inappropriate videos.", - "editor_notes": "This incident was originally submitted by Sean McGregor.", - "editor_dissimilar_incidents": [], - "editor_similar_incidents": [], - "flagged_dissimilar_incidents": [], - 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}, - { - "entity_id": "google", - "name": "Google" - } - ], - "date": "2014-08-14", - "description": "Kronos’s scheduling algorithm and its use by Starbucks managers allegedly negatively impacted financial and scheduling stability for Starbucks employees, which disadvantaged wage workers. V2", - "editor_dissimilar_incidents": [], - "editor_similar_incidents": [], - "editors": [ {"userId": 1, "first_name": "Sean", "last_name": "McGregor"}, {"userId": "2", "first_name": "Pablo", "last_name": "Costa"}] , - "embedding": { - "from_reports": [25, 24, 23, 22, 21, 20, 19, 18, 17, 16], - "vector": [ - -0.06841292232275009, 0.08255906403064728, 0.017737803980708122, -0.10710948705673218, - 0.12266533076763153, -0.04649468511343002, -0.015452317893505096, 0.017702659592032433, - 0.07051698118448257, -0.10235148668289185, -0.021081121638417244, 0.017820026725530624, - 0.032566819339990616, -0.03947213292121887, 0.02732796035706997, -0.05834178999066353, - -0.09229441732168198, -0.03068310022354126, 0.0005864544655196369, -0.11755073070526123, - -0.08433695137500763, 0.005715564824640751, 0.006991466972976923, 0.11313071101903915, - -0.035511113703250885, 0.022447967901825905, 0.11154558509588242, 0.11783023923635483, - 0.004598249681293964, 0.05499538779258728, -0.015789125114679337, -0.08348552882671356, - 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"date_published": "2015-06-03", - "description": "Kronos, a workforce management company, thinks giving managers more data can make scheduling practices more fair. But ultimately, the underpinnings of the problem may go deeper than the algorit", - "epoch_date_submitted": 1559347200, - "image_url": "https://img.buzzfeed.com/buzzfeed-static/static/2015-06/2/17/campaign_images/webdr02/kronos-makes-changes-to-software-accused-of-produ-2-14421-1433279634-2_dblbig.jpg", - "language": "en", - "report_number": 25, - "source_domain": "buzzfeednews.com", - "submitters": ["Catherine Olsson"], - "tags": [], - "text": "In April, the New York attorney general's office launched an [investigation](http://www.reuters.com/article/2015/04/13/us-retail-workers-nyag-idUSKBN0N40G420150413) into the scheduling practices of 13 national retail chains, distributing a letter to the Gap, Target, J.C. Penney, and 10 other companies. The letter asked, among other things, whether these companies' store managers use software manufactured by a company called Kronos to algorithmically generate schedules.\n\nA few months later, Kronos was also featured prominently in an [article](http://www.nytimes.com/interactive/2014/08/13/us/starbucks-workers-scheduling-hours.html) published by the _New York Times_ about the ill effects of erratic scheduling on Starbucks employees, especially one particular family. In a [follow-up piece](http://www.nytimes.com/times-insider/2014/08/22/times-article-changes-a-policy-fast/), the author, Jodi Kantor, points directly to Kronos' scheduling software as the root of the problem. \"I saw that her life was coming apart and that the Starbucks software had contributed to the crisis,\" Kantor wrote of one of the story's subjects.\n\nThe piece's argument centered around the financial and scheduling unpredictability engendered by platforms like Kronos. When you don't know if your shift might be canceled, if or when you'll be called in, or what your hours will look like next week or the week after, it becomes [very difficult](http://www.buzzfeed.com/sapna/victorias-secret-keeps-workers-on-call-and-unpaid#.wspVMPngB) to make even the most\n\nbasic plans for your future. This can have devastating long-term financial and emotional impacts on workers. According to a [recent study](http://www.epi.org/publication/irregular-work-scheduling-and-its-consequences/) by the Economic Policy Institute, a left-leaning think tank in Washington, D.C., 17 percent of the American workforce is negatively affected by unstable schedules.\n\nFor their part, Kronos representatives argue that the algorithm is far from the root of the problem. \"The populist view is that scheduling is evil, in that it's causing erratic schedules for employees, and so forth,\" Charlie DeWitt, vice president of business development for Kronos, told BuzzFeed News. \"The fact of the matter is it's an algorithm. It does whatever you want it to do.\"\n\nAnd you don't necessarily need to work for Kronos to believe that in a competitive retail climate, the problem is more complicated than technology alone. [Lonnie Golden](http://www.abington.psu.edu/academics/faculty/dr-lonnie-golden), a Penn State economist who has extensively studied the impact of erratic scheduling, acknowledges that Kronos' product itself is less to blame than the managers who make staffing decisions based on the data it provides. \"It's not necessarily the technology that's responsible for minimum to no advance notice,\" he said. \"It's the way in which it's applied.\"\n\nBut, he added, \"where there's a technology problem, there's usually a technology solution.\" And while Kronos maintains that managers, and not the software, are responsible for early dismissals and last-minute shift cancellations, the company is nonetheless pursuing some technological solutions.\n\nKronos wants to help managers better understand how scheduling adjustments affect workers and, ultimately, the bottom line. Though the company maintains that its software doesn't produce the kind of erratic schedules that hurt wage workers, DeWitt said there was nonetheless an interest in figuring out why that perception existed — and, if possible, fixing it.\n\nTo that end, earlier this month at a [retail conference in Philadelphia](http://www.kronos.com/microsites/RetailExecSummitSpring15/), the company announced that it's working on a new plug-in that will give managers better insight into workers' schedule stability, equity of hours worked among employees, and the consistency of schedules from week to week. In addition, Kronos is improving a feature meant to help give employees more control over their schedules: Though the software already incorporates employee availability and preferences into its scheduling calculations, improvements to a shift-swapping feature on its employee-facing web and mobile apps will theoretically allow employees to work around conflicts among themselves.\n\nGolden said increased employee input and control would be a good thing. But some retailers, DeWitt pointed out, are uncomfortable making workers use an app outside of work hours; indeed, the practice could be seen as a shift of management responsibilities onto lower-paid individuals.\n\nPart of the idea behind the new Kronos plug-in is to help companies tie fairer scheduling practices to reduction in absenteeism and turnover, which can be enormously costly. In other words, if Kronos can help executives see the connection between treating workers fairly and a store's ability to increase revenue, DeWitt said, managers will have an impetus to create more predictable, stable schedules.\n\nAnd just because companies are looking at this kind of data doesn't mean they have to use it. \"Companies like Kronos and Workplace Systems are starting to integrate some of these principles into their software,\" said [Carrie Gleason](http://populardemocracy.org/carrie-gleason), director of the Fair Workweek Initiative at the Center for Popular Democracy, \"but it's all optional, so companies can decide not to do it.\" While [12 states](http://populardemocracy.org/campaign/restoring-fair-workweek) are currently considering legislation that would create new labor standards around the workweek, Gleason said the technology alone lacks a mechanism for enforcement.\n\nGiven market pressures and standard management practices, it's unlikely that any change to Kronos' technology would give workers more power — especially because, given the [competitive retail climate](http://www.buzzfeed.com/sapna/retail-winter-of-death#.krGlE3qDm) at the moment, the bottom line tends to be the priority. \"It's not just bad managers. They have extreme pressure to increase productivity on an ever-shrinking labor budget,\" Gleason said.\n\nWith these changes, Kronos has taken logical steps toward both repairing its reputation and making sure its software creates sustainable work environments. But while the company cannot control exactly how the algorithm that forecasts schedules and optimizes workforces is deployed inside different workplaces, the Kronos engineers who designed the product are nonetheless the partial architects of work environments that have been proven to be untenable for low-wage workers. The Kronos scheduling algorithm isn't designed to serve those people; it's designed to be sold to their bosses, and as such, will ultimately be shaped to serve the needs of management — until regulations exist that compel them to change how it's used.", - "title": "After A Wave Of Bad Press, This Controversial Software Company Is Making Changes", - "url": "https://www.buzzfeednews.com/article/carolineodonovan/kronos-makes-changes-to-software-accused-of-producing-bad-sc" - }, - { - "authors": ["Alain Sherter"], - "cloudinary_id": "reports/cbsnews2.cbsistatic.com/hub/i/r/2014/09/25/aa2708c1-7e84-4573-8f79-add177a690cd/thumbnail/1200x630/2505bd91ab74f8e2ce3e7c6f8a5d77f3/starbucks.jpg", - "date_published": "2014-09-26", - "description": "Baristas at the coffee chain worry the company won't fulfill its promise to improve working conditions", - "epoch_date_submitted": 1559347200, - "image_url": "https://cbsnews2.cbsistatic.com/hub/i/r/2014/09/25/aa2708c1-7e84-4573-8f79-add177a690cd/thumbnail/1200x630/2505bd91ab74f8e2ce3e7c6f8a5d77f3/starbucks.jpg", - "language": "en", - "report_number": 24, - "source_domain": "cbsnews.com", - "submitters": ["Catherine Olsson"], - "tags": [], - "text": "Liberte Locke, a 32-year-old \"barista\" at a Starbucks (SBUX) in New York City, is fed up.\n\n\"Starbucks' attitude is that there's always someone else who can do the job,\" she said in running through her complaints about life at the java giant.\n\nIf that isn't necessarily the consensus among Starbucks workers, interviews with nine current and former baristas at the company make clear it's not an isolated opinion, either. Even those who say they like their job paint a picture of a business that underpays front-line workers, enforces work rules arbitrarily, and too often fails to strike a balance between corporate goals and employee needs.\n\nOf course, such complaints are nothing new in retail, where low pay and erratic schedules are the norm. But by its own account, Starbucks is no ordinary company and is ostensibly a far cry from the fast-food outlets now facing a nationwide uprising by employees tired of working for peanuts.\n\nThat's evident in the company's recruitment pitch. Starbucks invites job-seekers to \"become a part of something bigger and inspire positive change in the world,\" describing it as a chance to discover a \"deep sense of purpose.\"\n\nDamage control\n\nThat image suffered a serious blow last month after The New York Times vividly chronicled a Starbucks worker struggling with the company's scheduling practices. The story, which centered on a 22-year-old barista and single mother, amounted to a public relations nightmare for Starbucks. Perhaps not coincidentally, within days of the story's publication top executives were promising reform.\n\nIn a memo to employees earlier this month, for instance, Chief Operating Officer Troy Alstead vowed to \"transform the U.S. partner experience,\" referring to Starbucks' more than 130,000 baristas. Inviting worker feedback, he said Starbucks will examine its approach to employee pay, revisit its dress code, make it easier for people to ask for time off, and consider other changes aimed at helping baristas balance work and their personal lives.\n\nAmong other changes, the company said it would end the practice of \"clopening,\" when an employee responsible for closing a store late at night is also assigned to open it early in the morning.\n\n\"We recognize that we can do more for our partners who wear the apron every day,\" he wrote.\n\nSome baristas did not feel this August memo from Starbucks went far enough in proposing ways to improve work conditions, so they marked it up with their own ideas. CBS\n\nAlthough Starbucks workers welcome this pledge to respect the apron, they fear the company is more intent on dousing the PR flames than on genuinely improving employees' experience. After the retailer last month sent an email to workers outlining possible solutions to the kind of scheduling problems and related issues detailed by the Times, a group of baristas gave the proposal a C- and posted online a marked-up version of the memo listing their own demands (see image above).\n\n\"We hope you're ready for a commitment to give us schedules that don't mess with taking care of kids, going to school or holding onto that second job we need because Sbux wages don't make ends meet,\" wrote the baristas, who are working with a union-backed labor group, the Center for Popular Democracy.\n\nRetail jungle\n\nDespite the recent media focus on Starbucks, the company's labor practices are generally no worse than those of many large retailers. In some ways they're better, with the company offering health care to part-time, as well as full-time, workers; unusually generous 401(k) matching contributions; annual stock grants to employees; and tuition reimbursement.\n\nStarbucks highlights such benefits as an example of its commitment to employees. \"Sharing success with one another has been core to the company's heritage for more than 40 years,\" Alstead said in the September memo.\n\nMeanwhile, some baristas say they enjoy their work and feel valued by Starbucks. \"It's a decent place to work, and my manager and co-workers are great,\" said one employee who asked not to be identified.\n\nBut other current and former workers claim Starbucks has changed in recent years, saying that corporate leaders' intense focus on slashing costs has short-circuited its professed commitment to workers. Mostly, they say Starbucks doesn't listen to employees and even punishes those who identify problems.\n\n\"The biggest problem is that baristas don't have a voice,\" said Sarah Madden, a former Starbucks barista who left the company this spring after two years with the coffee vendor. \"They can't speak to issues that they know exist. Workers know how to fix them, but when [they] speak up there are serious repercussions -- your hours get cut, you're transferred to another store or isolated from other people.\"\n\nEmployees interviewed for this article said one result of Starbucks' cost-containment push is that stores are frequently understaffed, hurting customer service and forcing managers to scramble to find staff. That problem is common across the big-box s", - "title": "For some Starbucks workers, job leaves bitter taste", - "url": "https://www.cbsnews.com/news/for-some-starbucks-workers-job-leaves-bitter-taste/" - }, - { - "authors": ["Aimee Picchi"], - "cloudinary_id": "reports/cbsnews1.cbsistatic.com/hub/i/r/2015/03/17/01a38576-5108-40f7-8df8-5416164ed878/thumbnail/1200x630/ca8d35fe6bc065b5c9a747d92bc6d94c/154211248.jpg", - "date_published": "2015-09-24", - "description": "Some employees at the coffee chain say it isn't living up to promises to improve the company's labor practices", - "epoch_date_submitted": 1559347200, - "image_url": "https://cbsnews1.cbsistatic.com/hub/i/r/2015/03/17/01a38576-5108-40f7-8df8-5416164ed878/thumbnail/1200x630/ca8d35fe6bc065b5c9a747d92bc6d94c/154211248.jpg", - "language": "en", - "report_number": 23, - "source_domain": "cbsnews.com", - "submitters": ["Catherine Olsson"], - "tags": [], - "text": "For Starbucks (SBUX) barista Kylei Weisse, working at the coffee chain helps him secure health insurance and some extra money while he studies at Georgia Perimeter College. What it doesn't provide is the kind of stable schedule that the company promised its workers last year.\n\n\"It's the wild inconsistency\" of the hours that's a problem, Weisse, 32, said. \"We're supposed to get them 10 days in advance, which often happens, but there's no guarantee. If our manager doesn't get it to us on time, we just have to deal with it.\"\n\nThat became a problem recently when Weisse's manager gave him only a few days notice on his work hours, which ended up conflicting with an anatomy and physiology exam at his college. Weisse ended up paying another worker $20 to take his shift so he could take the exam.\n\nThe short notice is especially frustrating because of Starbucks' vow last year to post employees' schedules at least 10 days in advance, as well as the company's insistence that workers provide at least one-month notice when they need to take a day off.\n\nWhat's behind Starbucks price increases?\n\nWeisse isn't alone in complaining that Starbucks isn't living up to its promises to overhaul its labor practices for its roughly 130,000 baristas. That vow followed an article last year by The New York Times that detailed how workers were struggling to manage childcare and other obligations when the company provided only a few days notice about their schedules.\n\nAbout half of roughly 200 Starbucks baristas in a recent survey said they are still receiving their schedule with less than one week's notice. Others also reported being asked to handle \"clopens,\" split shifts in which employees work a closing shift late into the evening and then an early opening shift the following morning. The company last year promised to end the practice.\n\nOf course, Starbucks isn't alone in using \"just-in-time\" scheduling, with the retail and restaurant industry increasingly turning to software that allows them to change work schedules at the last minute, depending on whether business picks up or slows down. But it is Starbucks that has become a lightning rod on the issue given its vows to improve how it treats employees and its own emphatic claims to valuing workers, whom it labels \"partners.\"\n\n\"Starbucks has the values and wants to do right by their employees,\" said Carrie Gleason, director of the Fair Workweek Initiative at the Center for Popular Democracy, an advocacy group focused on workers'rights, and a co-author of the group's new report on the company's labor practices. \"However, since last year when the company recognized there was a serious problem with the way it scheduled workers and pledged to reform, still so many of the same issues persist.\"\n\nStarbucks didn't respond to requests for comment on the study or on baristas' reports of labor practices that are failing to meet the company's stated goals.\n\nIn an internal memo this week published by Time, Starbucks executive Cliff Burrows wrote that the company couldn't validate the survey, but added that \"the findings suggest, contrary to the expectations we have in place, that some partners are receiving their schedules less than one week in advance and that there is a continuing issue with some partners working a close and then an opening shift the following morning.\" He asks store managers \"to go the extra mile to ensure partners have a consistent schedule.\"\n\nStarbucks ends \"race together\" campaign amid public backlash\n\nTo be sure, some Starbucks workers are receiving at least 10 days notice on their work hours, with the survey finding that about one-third receive two weeks notice and another 18 percent get their schedules three weeks in advance. But that leaves almost half of workers who only receive one week's notice, making it more difficult from them manage other obligations, such as school, family commitments or other jobs.\n\nClopens remain a problem, as well. About 60 percent of workers who have to handle a clopen receive seven or fewer hours of rest between a closing and an opening shift, the study found.\n\nThat's prompted one former Starbucks employee to start a petition to end the practice of scheduling clopens. Ciara Moran noted in her petition that she sometimes was only able to get four or five hours of sleep on the days she was scheduled for clopens. She said she quit her job because she doubted whether it was possible to get ahead given the demands on workers.\n\nEven if Starbucks stuck with its policy of providing eight hours between shifts, that's not enough time, especially given that many workers in the service sector have long commutes, the study said.\n\nAnother issue singled out by the report is Starbucks' practices on sick time. Since paid time off is only available to workers with at least a year on the job, about 40 percent of employees in the survey said they had dealt with barriers in taking sick days.\n\nIn a perfect world, Weisse said he'd like to receive his schedule either a month or a", - "title": "​Is Starbucks shortchanging its baristas?", - "url": "https://www.cbsnews.com/news/is-starbucks-shortchanging-its-baristas/" - }, - { - "authors": ["Tommy Tonkins"], - "cloudinary_id": "reports/www.quinyx.com/hubfs/Blog/Images/starbucks-scheduling.png#keepProtocol", - "date_published": "2016-01-01", - "description": "Workforce management software helps companies take the guesswork out of scheduling. Through analysis of different data streams, and by using historical data and forecast modules, it can optimise schedules for each day of the year to ensure companies are working efficiently and productively in order to maximise profits.", - "epoch_date_submitted": 1559347200, - "image_url": "http://www.quinyx.com/hubfs/Blog/Images/starbucks-scheduling.png#keepProtocol", - "language": "en", - "report_number": 22, - "source_domain": "quinyx.com", - "submitters": ["Catherine Olsson"], - "tags": [], - "text": "The best schedules deliver two things; they ensure a business maximises profit while at the same time keeping the workforce happy and motivated.\n\nAt first glance, these two outcomes seem to be coming from opposite sides of the spectrum. To maximise profits businesses have to adopt schedules that are dynamic, flexible and efficient. This type of scheduling doesn’t always work in harmony with employees who crave stability and regular working hours.\n\nWorkforce management software helps companies take the guesswork out of scheduling. Through analysis of different data streams, and by using historical data and forecast modules, it can optimise schedules for each day of the year to ensure companies are working efficiently and productively in order to maximise profits.\n\nFor many businesses, especially retailers and hospitality businesses, the workforce is the biggest controllable cost and being able to optimise it through automated scheduling is a dream come true for managers. They are able to react to instant demands, seasonal changes and sales patterns with a few clicks of the mouse to create the perfect schedule.\n\nHowever, as Starbucks found in the USA, a constantly changing schedule dictated by the management and by software can make it miserable for workers. The sheer unpredictability of the scheduling, with hours ranging from 40 to 15 to zero for some employees, meant it was impossible for workers to plan their lives.\n\nStriking a balance\n\nA group of Starbucks employees set up an online petition last year calling on the company to change their scheduling practices. They said: ‘We often get our schedules less than a week in advance — that’s not nearly enough to plan for childcare, another job, or school… or enough to cover our bills.\n\n‘There’s no reason why our work schedules can’t be as consistent as our loyal customers who line up for their morning Venti soy latte.’\n\nAfter a feature in The New York Times, Starbucks agreed to revise its policy of the way it schedules its 130,000 workers, realising the importance in having a happy and motivated workforce.\n\nThey have two needs they need to fulfil; the need to have a schedule that delivers for the business and the need to take care of their employees. In order to do this, they have to use the same workforce management software to empower their staff to control their own schedules.\n\nAs noted in the Harvard Business Review: “Ultimately, the success of scheduling systems depends on whether they serve as tools for or against the workers.\n\n“In many ways, data-driven scheduling software is attractive to retailers because it gives them unprecedented transparency. But the ultimate success of these systems depends on this same transparency being available to employees as well.”\n\nA new approach\n\nThis is why Quinyx will tell all employees when there are available shifts, allowing them to claim the shifts that fit in best for them and choose the hours they work. Quinyx will optimise the schedule and, through integrated and effective communication, employees are then able to claim or swap available shifts with their co-workers.\n\nWorkers are enabled. They have control over their own destiny and because they are cared about and looked after they deliver more and work harder. The results we’ve seen from companies using Quinyx speak for themselves.\n\nThis approach to scheduling is revolutionising the way businesses around the world are operating. But don’t just take our word for it. Get in touch today and see how we can help you do the same.", - "title": "How Starbucks started taking their schedules seriously…and why you should too", - "url": "https://www.quinyx.com/blog/how-starbucks-started-taking-their-schedules-seriously-and-why-you-should-too" - }, - { - "authors": ["Aditi Sen", "Carrie Gleason"], - "cloudinary_id": "reports/placeholder.svg", - "date_published": "2015-09-01", - "description": "A 2015 nationwide survey of Starbucks workers reveals that the company is not living up to its commitment to provide predictable, sustainable schedules to its workforce. Starbucks’ frontline employees bear the brunt of the management imperative to minimize store labor costs, which takes precedence over attempts to stabilize work hours, provide healthy schedules, and to ensure employees have real input into their working conditions. In 2014, a New York Times investigation into Starbucks’ scheduling practices revealed a troubling disconnect between a company whose mission is “to inspire and nurture the human spirit”...", - "epoch_date_submitted": 1559347200, - "image_url": "placeholder.svg", - "language": "en", - "report_number": 21, - "source_domain": "populardemocracy.org", - "submitters": ["Catherine Olsson"], - "tags": [], - "text": "A 2015 nationwide survey of Starbucks workers reveals that the company is not living up to its commitment to provide predictable, sustainable schedules to its workforce. Starbucks’ frontline employees bear the brunt of the management imperative to minimize store labor costs, which takes precedence over attempts to stabilize work hours, provide healthy schedules, and to ensure employees have real input into their working conditions. In 2014, a New York Times investigation into Starbucks’ scheduling practices revealed a troubling disconnect between a company whose mission is “to inspire and nurture the human spirit” and work schedules that left its employees exhausted, stressed, and struggling to care for their families and get ahead. Starbucks responded quickly, committing to honor employee availability, deliver weekly schedules with greater notice, and ensure adequete rest between shifts. One year later, Starbucks employees across the country report that little has changed for them. Many Starbucks scheduling policies fail to reflect the company’s human-focused values, while other policies designed to promote sustainable schedules have been implemented inconsistently", - "title": "THE GRIND: Striving for Scheduling Fairness at Starbucks", - "url": "http://populardemocracy.org/sites/default/files/The%2BGrind.pdf" - }, - { - "authors": ["Aaron Smith"], - "cloudinary_id": "reports/i2.cdn.turner.com/money/dam/assets/150924110035-starbucks-barista-780x439.jpg", - "date_published": "2015-09-24", - "description": "Starbucks exec addressed managers ahead of New York Times story about scheduling issues.", - "epoch_date_submitted": 1559347200, - "image_url": "https://i2.cdn.turner.com/money/dam/assets/150924110035-starbucks-barista-780x439.jpg", - "language": "en", - "report_number": 20, - "source_domain": "money.cnn.com", - "submitters": ["Catherine Olsson"], - "tags": [], - "text": "An internal memo from a Starbucks executive this week urged store managers to \"go the extra mile\" to improve workers' schedules.\n\nThe letter was distributed on Tuesday and refers to a New York Times story that was set to be published the following day titled, \"Starbucks falls short after pledging better labor practices.\"\n\nThe Times story referred to a survey by the nonprofit advocacy group Center for Popular Democracy.\n\nBased on interviews with 200 baristas in 37 states, the survey says Starbucks \"is not living up to its commitment to provide predictable, sustainable schedules to its workforce.\"\n\nIn 2014, Starbucks said it was changing its policies telling managers to post schedules at least a week in advance and not make store employees work an opening and closing shift back-to-back.\n\nIn this week's memo, Cliff Burrows, Starbucks (SBUX) group president of the U.S. and Americas, said the findings of the new survey \"suggest\" that neither commitment was being met -- \"contrary to the expectations we have in place.\"\n\nIn his letter, Burrows urges managers to improve scheduling for coffee baristas, who the company calls partners.\n\n\"To our store managers, I want to stress that as we continue to evolve and improve the usability of our system, we have to go the extra mile to ensure partners have a consistent schedule -- free of back-to-back close and open shifts that are less than 8 hours apart -- that is posted 2 weeks in advance,\" he wrote.", - "title": "Starbucks vows to do more to ease barista schedules", - "url": "https://money.cnn.com/2015/09/24/news/companies/starbucks-schedules/index.html" - }, - { - "authors": ["Fair Workweek Initiative"], - "cloudinary_id": "reports/static1.squarespace.com/static/556496efe4b02c9d26fdf26a/5564cf12e4b0e2aacd5c90bf/575d6f189f7266835093d178/1465776979746/5f134984-0e51-11e6-9acf-68c45ca472eb-1020x655.jpg?format=1500w", - "date_published": "2016-06-04", - "description": "Originally published on Seattle Times on June 4, 2016 at 8:00 am", - "epoch_date_submitted": 1559347200, - "image_url": "http://static1.squarespace.com/static/556496efe4b02c9d26fdf26a/5564cf12e4b0e2aacd5c90bf/575d6f189f7266835093d178/1465776979746/5f134984-0e51-11e6-9acf-68c45ca472eb-1020x655.jpg?format=1500w", - "language": "en", - "report_number": 19, - "source_domain": "fairworkweek.org", - "submitters": ["Catherine Olsson"], - "tags": [], - "text": "Originally published on Seattle Times on June 4, 2016 at 8:00 am\n\nThe company took a lot of heat in 2014 when The New York Times described scheduling practices that made some employees miserable. But the coffee giant says its policies and software have changed in important ways since then.\n\nStarbucks says it gives employees more advance notice and makes sure of more rest between shifts than it did when a 2014 New York Times storydescribed scheduling practices that made some workers miserable.\n\nThe coffee company now requires U.S. store managers to post workers’ schedules 14 days in advance, up from 10 previously. Its scheduling software now prevents managers from booking employees to work shifts with less than an eight-hour break in between, Starbucks says.\n\nThe company says it has never used on-call scheduling and has always tried to give available hours to part-time employees who request them. It offers full benefits to employees who work 20 hours a week or more.\n\nThe company’s contact center, where employees call in with concerns, now has a special team to deal with scheduling issues. Fewer than 3 percent of those calls concern scheduling, said spokeswoman Jaime Riley.\n\nStill, implementation appears to be uneven.\n\nIlana Greenberg, a barista at the Starbucks drive-through on Elliott Avenue West in Seattle, says that at her store the manager is good about getting schedules out to employees two weeks in advance.\n\nBut she’s heard from baristas at other stores who’ve gotten their schedules only three to four days in advance, and employees who’ve worked “clopening” shifts or “doubles” — working two shifts a day at different stores.\n\nGreenberg, who volunteers as an organizer with the union-backed Working Washington, acknowledges that some of those instances are likely due to the workers’ own choices. But sometimes, she says, “The manager will come up to a partner [employee] and say, ‘Can you work this shift?’ and it’s clear from phrasing and tone of voice that you don’t really have the option of saying no.”\n\nStarbucks acknowledges that sometimes, practice doesn’t always meet company policy. “We’re not perfect,” said spokeswoman Riley. “We know there’s still work to be done.”", - "title": "The Seattle Times: Starbucks says its scheduling practices have improved", - "url": "http://www.fairworkweek.org/news-bedford/starbucks-schedule" - }, - { - "authors": ["Heidi Groover"], - "cloudinary_id": "reports/placeholder.svg", - "date_published": "2015-11-12", - "description": "On Tuesday, I wrote about Starbucks baristas and other food service workers who marched downtown at sunrise to call for fair scheduling from their employers. Workers said they often get little notice of when they're scheduled to work and their hours can vary dramatically from week to week. At Starbucks, 19-year-old barista Darrion Sjoquist told me, understaffing makes it nearly impossible to call in sick without finding someone to fill in for you, worsening the unpredictability for everyone.\n\nIn a new essay up on Medium, Sjoquist describes more about his own experience and it's worth a read.\n\nSjoquist writes about how, growing up, his mom worked at Starbucks and the two also took care of Sjoquist's niece Khaliah. Because of his mom's erratic scheduling—including the infamous \"clopening\" shifts—he ended up \"picking up a lot of the childcare responsibilities.\"\n\nAt 16, I was waking up at 4:30 a.m. to get Khaliah to her preschool by 7 a.m. I would then rush back to the bus stop to get to my high school by 8 a.m. We never knew what the next week would be like more than a 7 days in advance, sometimes less. There was no consistency. As a result it was difficult to be an involved student. I was late as often as the buses were and couldn’t regularly attend any clubs or groups. Without structure in your life, it’s difficult to form healthy habits and it’s easy to feel lost or overwhelmed.\n\nNow that Sjoquist himself is working at Starbucks, he says he frequently fills in for coworkers when they're sick because he knows how impossible it can be to find someone to cover a shift. That sometimes means closing the store one night and then getting a call around 4 a.m. to fill in for a sick coworker the next morning, effectively working the same \"clopenings\" his mom did. That whole process of struggling to find a coworker to fill in for you if you're sick, Sjoquist writes, \"breeds the mindset that being sick is your fault.\"\n\nStarbucks expects us to find staff coverage when we call in sick. You are expected to show up for work if your son has been missing for 24 hours or your grandfather has died. If you are so sick that it hurts to speak, you are expected to call and text and beg every available person and ask them to sacrifice their day off, their precious hours before work or after school to help you solve a problem neither of you had any control over.\n\nIt’s a strange practice, one that breeds the mindset that being sick is your fault. When this outlook is coupled with incredibly lean staffing, there’s an enormous amount of guilt that comes with calling in sick. When losing just one person means disaster, calling in sick feels like abandoning your friends and peers. The bottom line is that no one chooses to be sick, but Starbucks seems to punish partners for calling out. The policy is to force baristas to ask their co-workers with no notice to work at stores where they’ve never been and to work with people they do not know. For a company as innovative and considerate as Starbucks this seems like it would be a last resort, not standard procedure.\n\nToday, Sjoquist spoke at a conference about \"the future of work\" in San Francisco. \"It’s time,\" he writes, \"for Starbucks to become an industry leader on a new front: fair schedules.\"", - "epoch_date_submitted": 1559347200, - "image_url": "placeholder.svg", - "language": "en", - "report_number": 18, - "source_domain": "thestranger.com", - "submitters": ["Catherine Olsson"], - "tags": [], - "text": "On Tuesday, I wrote about Starbucks baristas and other food service workers who marched downtown at sunrise to call for fair scheduling from their employers. Workers said they often get little notice of when they're scheduled to work and their hours can vary dramatically from week to week. At Starbucks, 19-year-old barista Darrion Sjoquist told me, understaffing makes it nearly impossible to call in sick without finding someone to fill in for you, worsening the unpredictability for everyone.\n\nIn a new essay up on Medium, Sjoquist describes more about his own experience and it's worth a read.\n\nSjoquist writes about how, growing up, his mom worked at Starbucks and the two also took care of Sjoquist's niece Khaliah. Because of his mom's erratic scheduling—including the infamous \"clopening\" shifts—he ended up \"picking up a lot of the childcare responsibilities.\"\n\nAt 16, I was waking up at 4:30 a.m. to get Khaliah to her preschool by 7 a.m. I would then rush back to the bus stop to get to my high school by 8 a.m. We never knew what the next week would be like more than a 7 days in advance, sometimes less. There was no consistency. As a result it was difficult to be an involved student. I was late as often as the buses were and couldn’t regularly attend any clubs or groups. Without structure in your life, it’s difficult to form healthy habits and it’s easy to feel lost or overwhelmed.\n\nNow that Sjoquist himself is working at Starbucks, he says he frequently fills in for coworkers when they're sick because he knows how impossible it can be to find someone to cover a shift. That sometimes means closing the store one night and then getting a call around 4 a.m. to fill in for a sick coworker the next morning, effectively working the same \"clopenings\" his mom did. That whole process of struggling to find a coworker to fill in for you if you're sick, Sjoquist writes, \"breeds the mindset that being sick is your fault.\"\n\nStarbucks expects us to find staff coverage when we call in sick. You are expected to show up for work if your son has been missing for 24 hours or your grandfather has died. If you are so sick that it hurts to speak, you are expected to call and text and beg every available person and ask them to sacrifice their day off, their precious hours before work or after school to help you solve a problem neither of you had any control over.\n\nIt’s a strange practice, one that breeds the mindset that being sick is your fault. When this outlook is coupled with incredibly lean staffing, there’s an enormous amount of guilt that comes with calling in sick. When losing just one person means disaster, calling in sick feels like abandoning your friends and peers. The bottom line is that no one chooses to be sick, but Starbucks seems to punish partners for calling out. The policy is to force baristas to ask their co-workers with no notice to work at stores where they’ve never been and to work with people they do not know. For a company as innovative and considerate as Starbucks this seems like it would be a last resort, not standard procedure.\n\nToday, Sjoquist spoke at a conference about \"the future of work\" in San Francisco. \"It’s time,\" he writes, \"for Starbucks to become an industry leader on a new front: fair schedules.\"", - "title": "\"I Hope That Howard Schultz Hears My Story\": A Starbucks Barista on Why He's Fighting for Fair Scheduling", - "url": "https://www.thestranger.com/blogs/slog/2015/11/12/23142113/i-hope-that-howard-schultz-hears-my-story-a-starbucks-barista-on-why-hes-fighting-for-fair-scheduling" - }, - { - "authors": ["News Writer"], - "cloudinary_id": "reports/cdn.ttgtmedia.com/visuals/searchFinancialApplications/hr_technology/financialapplications_article_004.jpg", - "date_published": "2015-08-27", - "description": "Retail and restaurant workers are the focus of a raging national debate over fair hours, pay and the use of shift scheduling software from big vendors such as ADP, SAP and Kronos.", - "epoch_date_submitted": 1559347200, - "image_url": "https://cdn.ttgtmedia.com/visuals/searchFinancialApplications/hr_technology/financialapplications_article_004.jpg", - "language": "en", - "report_number": 17, - "source_domain": "searchhrsoftware.techtarget.com", - "submitters": ["Catherine Olsson"], - "tags": [], - "text": "Caitlin O'Reilly-Green, a barista at an Atlanta Starbucks, says a manager blames her erratic hours on the staff scheduling software used by the giant coffee chain.\n\n\"When you try to bring up issues with it, they just kind of blame it on the software,\" she said. \"'That is what the computer does.' They can't do anything about it or change the schedule. It is very frustrating.\"\n\nShe said the shift scheduling software often turns out hours that make no sense.\n\nO'Reilly-Green is among millions of retail and restaurant workers at the focus of a raging national debate over fair hours, pay and the use of shift scheduling software -- sometimes called staff scheduling software -- from big vendors like ADP, SAP and Kronos to set worker hours based on factors such as store traffic and weather.\n\nStarbucks uses Kronos to set schedules at her store, O'Reilly-Green said, but the company would neither confirm nor deny that it uses Kronos.\n\n\"I work about 25 hours a week right now,\" she said of the Starbucks barista job she has held for a little more than a year. The schedule is \"totally 100% different every week,\" she said. \"Monday is 4 to 8. On Tuesday, it can be 9 to 4. It is different every single day, every single week. There has never been any sort of consistency.\"\n\nShe said a year-old Starbucks policy requiring at least a 10-day advance notice for a weekly schedule is helpful, but workers, at least at her store, need more advance notice and more consistency, though they do sometimes get two weeks' notice. Starbucks provides Blue Cross Blue Shield health insurance for workers, but it can be difficult to use because doctor's appointments are tough to schedule, she said.\n\nO'Reilly-Green said the erratic scheduling is her biggest concern after the pay: She gets $8.10 an hour in a state where the minimum wage is $7.25.\n\nLaurel Harper, a spokeswoman for Starbucks, said the company is very concerned about the experience of one of its employees and welcomes the opportunity to look into the situation and fix it.\n\nStore managers are allowed to schedule employees up to three weeks in advance, and must post schedules at least 10 days in advance, she said.\n\nA year ago, the company updated its automated shift scheduling software to enable more stability and consistency of weekly hours. Only managers have access to the software. Lower-level employees can't use it to propose shift swaps, for example, or leave notes, she said.\n\nEmployees do have the option to change shifts with other employees if a personal conflict arises. Each employee has unique scheduling needs, and store managers do their best to accommodate all requests for time off for any appointment or personal reason, Harper said.\n\nActivist cites software abuse Software companies, not just retailers or other users, have a big role in helping bring about change to improve schedules and protect workers in the retail and service industries, said Erin Hurley, worker organizer at Rise Up Georgia, a grassroots organization. Software vendors should set guidelines on how their shift scheduling software is used, she said. Shift scheduling software enables certain retailers and restaurant chains to use on-call shifts and create erratic schedules for workers, according to Hurley. \"They should hold the corporations accountable if their product is being abused,\" she said. \"I would not want to have a client that is abusing my product, and that is what retailers are doing with software -- abusing it.\" Hurley received $8 an hour when she worked on and off for five years at Bath & Body Works. In her most recent stint from August 2014 to February, she generally had to be available for two to three on-call shifts a week, which forced her to keep her schedule open, even though 90% of the time she wasn't needed. Kronos Vice President Charlie DeWitt said Kronos is continuing to work with retailers, policy makers, nonprofits, and academics to address issues surrounding schedule stability, equity and employee engagement. \"We have a policy to not comment on how our customers use Kronos,\" DeWitt said by email when asked if he agreed with the Starbucks worker who said the schedules produced by Kronos make no sense and that managers need to be more involved in setting the schedule. But in a move that could benefit workers, Kronos is planning to make available in the next few months a plug-in to help companies track how often the time worked by an employee differs from the initial schedule, if they are receiving adequate hours and how often their schedule changes from week to week, DeWitt said. \"We recently met with 10 major retailers to explore enhancement opportunities for the prototype,\" DeWitt said in the email. \"The meeting was successful and most showed interest in pursuing further.\" Another vendor, Oracle, has discontinued selling shift scheduling software, a spokesman said. Congress and about 10 states are considering bills to require premium pay for \"on-call\" scheduling, or in at least some cases, for failu", - "title": "Kronos shift scheduling software a grind for Starbucks worker", - "url": "https://searchhrsoftware.techtarget.com/news/4500252451/Kronos-shift-scheduling-software-a-grind-for-Starbucks-worker" - }, - { - "authors": ["Jodi Kantor"], - "cloudinary_id": "reports/static01.nyt.com/images/2014/08/13/us/worker-hours-1407960684740/worker-hours-1407960684740-articleLarge.jpg", - "date_published": "2014-08-14", - "description": "Increasing numbers of low-income mothers and fathers are at the center of a new collision that pits workplace scheduling technology against the routines of parenting.", - "epoch_date_submitted": 1559347200, - "image_url": "https://static01.nyt.com/images/2014/08/13/us/worker-hours-1407960684740/worker-hours-1407960684740-articleLarge.jpg", - "language": "en", - "report_number": 16, - "source_domain": "nytimes.com", - "submitters": ["Catherine Olsson"], - "tags": [], - "text": "SAN DIEGO — In a typical last-minute scramble, Jannette Navarro, a 22-year-old Starbucks barista and single mother, scraped together a plan for surviving the month of July without setting off family or financial disaster.\n\nIn contrast to the joyless work she had done at a Dollar Tree store and a KFC franchise, the $9-an-hour Starbucks job gave Ms. Navarro, the daughter of a drug addict and an absentee father, the hope of forward motion. She had been hired because she showed up so many times, cheerful and persistent, asking for work, and she had a way of flicking away setbacks — such as a missed bus on her three-hour commute — with the phrase, “I’m over it.”\n\nJannette Navarro at Starbucks.\n\nNewly off public assistance, she was just a few credits shy of an associate degree in business and talked of getting a master’s degree as some of her co-workers were. Her take-home pay rarely topped $400 to $500 every two weeks; since starting in November, she had set aside $900 toward a car — her next step toward stability and independence for herself and her 4-year-old son, Gavin.\n\nBut Ms. Navarro’s fluctuating hours, combined with her limited resources, had also turned their lives into a chronic crisis over the clock. She rarely learned her schedule more than three days before the start of a workweek, plunging her into urgent logistical puzzles over who would watch the boy. Months after starting the job she moved out of her aunt’s home, in part because of mounting friction over the erratic schedule, which the aunt felt was also holding her family captive. Ms. Navarro’s degree was on indefinite pause because her shifting hours left her unable to commit to classes. She needed to work all she could, sometimes counting on dimes from the tip jar to make the bus fare home. If she dared ask for more stable hours, she feared, she would get fewer work hours over all.\n\n“You’re waiting on your job to control your life,” she said, with the scheduling software used by her employer dictating everything from “how much sleep Gavin will get to what groceries I’ll be able to buy this month.”\n\nLast month, she was scheduled to work until 11 p.m. on Friday, July 4; report again just hours later, at 4 a.m. on Saturday; and start again at 5 a.m. on Sunday. She braced herself to ask her aunt, Karina Rivera, to watch Gavin, hoping she would not explode in annoyance, or worse, refuse. She vowed to somehow practice for the driving test that she had promised her boyfriend she would pass by the previous month. To stay awake, she would formulate her own behind-the-counter coffee concoctions, pumping in extra shots of espresso.\n\nA sign with a missed deadline hanging in the kitchen.\n\nScheduling Chaos\n\nLike increasing numbers of low-income mothers and fathers, Ms. Navarro is at the center of a new collision that pits sophisticated workplace technology against some fundamental requirements of parenting, with particularly harsh consequences for poor single mothers. Along with virtually every major retail and restaurant chain, Starbucks relies on software that choreographs workers in precise, intricate ballets, using sales patterns and other data to determine which of its 130,000 baristas are needed in its thousands of locations and exactly when. Big-box retailers or mall clothing chains are now capable of bringing in more hands in anticipation of a delivery truck pulling in or the weather changing, and sending workers home when real-time analyses show sales are slowing. Managers are often compensated based on the efficiency of their staffing.\n\nScheduling is now a powerful tool to bolster profits, allowing businesses to cut labor costs with a few keystrokes. “It’s like magic,” said Charles DeWitt, vice president for business development at Kronos, which supplies the software for Starbucks and many other chains.\n\nYet those advances are injecting turbulence into parents’ routines and personal relationships, undermining efforts to expand preschool access, driving some mothers out of the work force and redistributing some of the uncertainty of doing business from corporations to families, say parents, child care providers and policy experts.\n\nIn Brooklyn, Sandianna Irvine often works “on call” hours at Ashley Stewart, a plus-size clothing store, rushing to make arrangements for her 5-year-old daughter if the store needs her. Before Martha Cadenas was promoted to manager at a Walmart in Apple Valley, Minn., she had to work any time the store needed; her mother “ended up having to move in with me,” she said, because of the unpredictable hours. Maria Trisler is often dismissed early from her shifts at a McDonald’s in Peoria, Ill., when the computers say sales are slow. The same sometimes happens to Ms. Navarro at Starbucks.\n\nBy Saturday afternoon of the Fourth of July weekend, Ms. Navarro had made it through “clopening,” closing late at night and opening again just a few hours later. But she had not yet worked up the courage to ask Ms. Rivera and Ms. Rivera’s boyfriend, Osc", - "title": "Working Anything but 9 to 5", - "url": "https://www.nytimes.com/interactive/2014/08/13/us/starbucks-workers-scheduling-hours.html" - }, - { - "authors": ["Pablo Costa"], - "cloudinary_id": "reports/static01.nyt.com/images/2014/08/13/us/worker-hours-1407960684740/worker-hours-1407960684740-articleLarge.jpg", - "date_published": "2022-08-14", - "description": "New Report description", - "epoch_date_submitted": 1559347200, - "image_url": "https://static01.nyt.com/images/2014/08/13/us/worker-hours-1407960684740/worker-hours-1407960684740-articleLarge.jpg", - "language": "en", - "report_number": 17, - "source_domain": "nytimes.com", - "submitters": ["Catherine Olsson"], - "tags": [], - "text": "New Report SAN DIEGO — In a typical last-minute scramble, Jannette Navarro, a 22-year-old Starbucks barista and single mother, scraped together a plan for surviving the month of July without setting off family or financial disaster.\n\nIn contrast to the joyless work she had done at a Dollar Tree store and a KFC franchise, the $9-an-hour Starbucks job gave Ms. Navarro, the daughter of a drug addict and an absentee father, the hope of forward motion. She had been hired because she showed up so many times, cheerful and persistent, asking for work, and she had a way of flicking away setbacks — such as a missed bus on her three-hour commute — with the phrase, “I’m over it.”\n\nJannette Navarro at Starbucks.\n\nNewly off public assistance, she was just a few credits shy of an associate degree in business and talked of getting a master’s degree as some of her co-workers were. Her take-home pay rarely topped $400 to $500 every two weeks; since starting in November, she had set aside $900 toward a car — her next step toward stability and independence for herself and her 4-year-old son, Gavin.\n\nBut Ms. Navarro’s fluctuating hours, combined with her limited resources, had also turned their lives into a chronic crisis over the clock. She rarely learned her schedule more than three days before the start of a workweek, plunging her into urgent logistical puzzles over who would watch the boy. Months after starting the job she moved out of her aunt’s home, in part because of mounting friction over the erratic schedule, which the aunt felt was also holding her family captive. Ms. Navarro’s degree was on indefinite pause because her shifting hours left her unable to commit to classes. She needed to work all she could, sometimes counting on dimes from the tip jar to make the bus fare home. If she dared ask for more stable hours, she feared, she would get fewer work hours over all.\n\n“You’re waiting on your job to control your life,” she said, with the scheduling software used by her employer dictating everything from “how much sleep Gavin will get to what groceries I’ll be able to buy this month.”\n\nLast month, she was scheduled to work until 11 p.m. on Friday, July 4; report again just hours later, at 4 a.m. on Saturday; and start again at 5 a.m. on Sunday. She braced herself to ask her aunt, Karina Rivera, to watch Gavin, hoping she would not explode in annoyance, or worse, refuse. She vowed to somehow practice for the driving test that she had promised her boyfriend she would pass by the previous month. To stay awake, she would formulate her own behind-the-counter coffee concoctions, pumping in extra shots of espresso.\n\nA sign with a missed deadline hanging in the kitchen.\n\nScheduling Chaos\n\nLike increasing numbers of low-income mothers and fathers, Ms. Navarro is at the center of a new collision that pits sophisticated workplace technology against some fundamental requirements of parenting, with particularly harsh consequences for poor single mothers. Along with virtually every major retail and restaurant chain, Starbucks relies on software that choreographs workers in precise, intricate ballets, using sales patterns and other data to determine which of its 130,000 baristas are needed in its thousands of locations and exactly when. Big-box retailers or mall clothing chains are now capable of bringing in more hands in anticipation of a delivery truck pulling in or the weather changing, and sending workers home when real-time analyses show sales are slowing. Managers are often compensated based on the efficiency of their staffing.\n\nScheduling is now a powerful tool to bolster profits, allowing businesses to cut labor costs with a few keystrokes. “It’s like magic,” said Charles DeWitt, vice president for business development at Kronos, which supplies the software for Starbucks and many other chains.\n\nYet those advances are injecting turbulence into parents’ routines and personal relationships, undermining efforts to expand preschool access, driving some mothers out of the work force and redistributing some of the uncertainty of doing business from corporations to families, say parents, child care providers and policy experts.\n\nIn Brooklyn, Sandianna Irvine often works “on call” hours at Ashley Stewart, a plus-size clothing store, rushing to make arrangements for her 5-year-old daughter if the store needs her. Before Martha Cadenas was promoted to manager at a Walmart in Apple Valley, Minn., she had to work any time the store needed; her mother “ended up having to move in with me,” she said, because of the unpredictable hours. Maria Trisler is often dismissed early from her shifts at a McDonald’s in Peoria, Ill., when the computers say sales are slow. The same sometimes happens to Ms. Navarro at Starbucks.\n\nBy Saturday afternoon of the Fourth of July weekend, Ms. Navarro had made it through “clopening,” closing late at night and opening again just a few hours later. But she had not yet worked up the courage to ask Ms. Rivera and Ms. Rivera’s boyfriend, Osc", - "title": "New Report", - "url": "https://www.nytimes.com/interactive/2014/08/13/us/starbucks-workers-scheduling-hours.html" - }, - { - "authors": ["Pablo Costa"], - "date_published": "2023-04-18", - "epoch_date_submitted": 1559347200, - "report_number": 18, - "tags": ["variant:unreviewed"], - "inputs_outputs": ["Input 1", "Output 1"] - } - ], - "title": "Kronos Scheduling Algorithm Allegedly Caused Financial Issues for Starbucks Employees V2" - } - } -} diff --git a/site/gatsby-site/playwright/fixtures/incidents/incidents.json b/site/gatsby-site/playwright/fixtures/incidents/incidents.json deleted file mode 100644 index 75ac408f4b..0000000000 --- a/site/gatsby-site/playwright/fixtures/incidents/incidents.json +++ /dev/null @@ -1,272 +0,0 @@ -{ - "data": { - "incidents": [ - { - "AllegedDeployerOfAISystem": [ - { - "entity_id": "youtube", - "name": "YouTube" - } - ], - "AllegedDeveloperOfAISystem": [ - 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"title": "Test Incident", - "description": "This is a test description", - "date": "2015-05-19", - "nlp_similar_incidents": [], - "incident_id": 1, - "editors": [], - "editor_notes": "This is a test", - "embedding": { - "from_reports": [1, 2, 3], - "vector": [-0.023964308202266693, 0.05607481673359871, -0.00004077623088960536] - }, - "reports": [ - { - "__typename": "Report", - "report_number": 1 - }, - { - "__typename": "Report", - "report_number": 2 - }, - { - "__typename": "Report", - "report_number": 3 - }, - { - "__typename": "Report", - "report_number": 4 - }, - { - "__typename": "Report", - "report_number": 5 - }, - { - "__typename": "Report", - "report_number": 6 - }, - { - "__typename": "Report", - "report_number": 7 - }, - { - "__typename": "Report", - "report_number": 8 - }, - { - "__typename": "Report", - "report_number": 9 - }, - { - "__typename": "Report", - "report_number": 10 - }, - { - "__typename": "Report", - "report_number": 11 - }, - { - "__typename": "Report", - "report_number": 12 - }, - { - "__typename": "Report", - "report_number": 14 - }, - { - "__typename": "Report", - "report_number": 15 - } - ], - "AllegedDeployerOfAISystem": [], - "AllegedDeveloperOfAISystem": [], - "AllegedHarmedOrNearlyHarmedParties": [], - "editor_similar_incidents": [], - "editor_dissimilar_incidents": [], - "flagged_dissimilar_incidents": [] - } - } -} diff --git a/site/gatsby-site/playwright/fixtures/incidents/updateOneIncidentFlagged.json b/site/gatsby-site/playwright/fixtures/incidents/updateOneIncidentFlagged.json deleted file mode 100644 index 96d29ac87e..0000000000 --- a/site/gatsby-site/playwright/fixtures/incidents/updateOneIncidentFlagged.json +++ /dev/null @@ -1,898 +0,0 @@ -{ - "data": { - "updateOneIncident": { - "AllegedDeployerOfAISystem": [ - { - "entity_id": "youtube", - "name": "YouTube" - } - ], - "AllegedDeveloperOfAISystem": [ - { - "entity_id": "youtube", - "name": "YouTube" - } - ], - "AllegedHarmedOrNearlyHarmedParties": [ - { - "entity_id": "children", - "name": "Children" - } - ], - "__typename": "Incident", - "date": "2015-05-19", - "description": "YouTube’s content filtering and recommendation algorithms exposed children to disturbing and inappropriate videos.", - "editor_dissimilar_incidents": [], - "editor_similar_incidents": [], - "editors": [{"userId": "1", "first_name": "Sean", "last_name": "McGregor"}], - "editor_notes": "Notes", - "embedding": { - "__typename": "IncidentEmbedding", - "from_reports": [ - 8, - 4, - 2, - 6, - 3, - 1, - 15, - 14, - 7, - 9, - 11, - 5, - 10, - 12 - ], - "vector": [ - -0.06751111149787903, - 0.06833232194185257, - 0.010899675078690052, - -0.1141943484544754, - 0.10080315172672272, - 0.01041362900286913, - 0.004443523474037647, - 0.06500855833292007, - 0.06018650531768799, - -0.12926925718784332, - 0.032988499850034714, - 0.08060640841722488, - 0.016019538044929504, - -0.08831886947154999, - 0.03878483548760414, - -0.056356046348810196, - -0.11832374334335327, - -0.061912901699543, - 0.00044723687460646033, - -0.13217340409755707, - 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}, - { - "__typename": "IncidentNlp_similar_incident", - "incident_id": 34, - "similarity": 0.998900830745697 - } - ], - "reports": [ - { - "__typename": "Report", - "report_number": 8 - }, - { - "__typename": "Report", - "report_number": 4 - }, - { - "__typename": "Report", - "report_number": 2 - }, - { - "__typename": "Report", - "report_number": 6 - }, - { - "__typename": "Report", - "report_number": 3 - }, - { - "__typename": "Report", - "report_number": 1 - }, - { - "__typename": "Report", - "report_number": 15 - }, - { - "__typename": "Report", - "report_number": 14 - }, - { - "__typename": "Report", - "report_number": 7 - }, - { - "__typename": "Report", - "report_number": 9 - }, - { - "__typename": "Report", - "report_number": 11 - }, - { - "__typename": "Report", - "report_number": 5 - }, - { - "__typename": "Report", - "report_number": 10 - }, - { - "__typename": "Report", - "report_number": 12 - } - ], - "title": "Google’s YouTube Kids App Presents Inappropriate Content" - } - } -} \ No newline at end of file diff --git a/site/gatsby-site/playwright/fixtures/reports/issueWithTranslations.json b/site/gatsby-site/playwright/fixtures/reports/issueWithTranslations.json deleted file mode 100644 index a304a6f318..0000000000 --- a/site/gatsby-site/playwright/fixtures/reports/issueWithTranslations.json +++ /dev/null @@ -1,50 +0,0 @@ -{ - "data": { - "report": { - "authors": [ - "Marco Acevedo" - ], - "date_downloaded": "2019-04-13", - "date_published": "2015-07-11", - "flag": null, - "image_url": "https://assets.change.org/photos/0/yb/id/eYyBIdJOMHpqcty-1600x900-noPad.jpg?1523726975", - "report_number": 10, - "submitters": [ - "Roman Yampolskiy" - ], - "tags": [ - "Test Tag" - ], - "text": "## Video still of a reproduced version of Minnie Mouse\n\nWhich appeared on the now-suspended Simple Fun channel Simple Fun.", - "plain_text": "Video still of a reproduced version of Minnie Mouse\n\nWhich appeared on the now-suspended Simple Fun channel Simple Fun.", - "title": "Remove YouTube Kids app until it eliminates its inappropriate content", - "description": "Description Remove YouTube Kids app until it eliminates its inappropriate content", - "url": "https://www.change.org/p/remove-youtube-kids-app-until-it-eliminates-its-inappropriate-content", - "editor_notes": "", - "language": "en", - "translations_es": { - "__typename": "ReportTranslation", - "title": "Este es el Título en español", - "text": "Este es un texto de prueba que tiene un largo mayor a ochenta caracteres (en español)" - }, - "translations_en": { - "__typename": "ReportTranslation", - "title": "", - "text": "" - }, - "translations_fr": { - "__typename": "ReportTranslation", - "title": "C'est le Titre en français", - "text": "Il s'agit d'un texte de test de plus de quatre-vingts caractères - lorem ipsum (en français)" - }, - "translations_ja": { - "__typename": "ReportTranslation", - "title": "これは日本語でのタイトルです", - "text": "解サオライ協立なーづ民手ぶみドに即記朝ぐ奥置ぱで地更トるあて栄厚ぜづを祭屋ん来派どてゃ読速ヘ誌約カタシネ原39業理る。外ヒヱフ社第むせゆ由更混ソエ夕野しりすよ顔飛リの兆基う公言や置17謝后嘘5供フキヌア星集ヘラ辞勘壇崇さびわ。（日本語で）" - }, - "date_modified": "2023-01-01", - "epoch_date_modified": 1672531200, - "is_incident_report": false - } - } -} \ No newline at end of file diff --git a/site/gatsby-site/playwright/fixtures/reports/probablyRelatedReports.json b/site/gatsby-site/playwright/fixtures/reports/probablyRelatedReports.json deleted file mode 100644 index 8ffaaccfce..0000000000 --- a/site/gatsby-site/playwright/fixtures/reports/probablyRelatedReports.json +++ /dev/null @@ -1,24 +0,0 @@ -{ - "data": { - "reports": [ - { - "__typename": "Report", - "report_number": 1, - "title": "Report 1 test title", - "url": "https://www.bbc.com/news/world-us-canada-46468719" - }, - { - "__typename": "Report", - "report_number": 2, - "title": "Report 2 test title", - "url": "https://www.bbc.com/news/world-us-canada-46468719" - }, - { - "__typename": "Report", - "report_number": 3, - "title": "Report 3 test title", - "url": "https://www.bbc.com/news/world-us-canada-46468719" - } - ] - } -} \ No newline at end of file diff --git a/site/gatsby-site/playwright/fixtures/reports/report.json b/site/gatsby-site/playwright/fixtures/reports/report.json deleted file mode 100644 index 1ae223f554..0000000000 --- a/site/gatsby-site/playwright/fixtures/reports/report.json +++ /dev/null @@ -1,37 +0,0 @@ -{ - "data": { - "report": { - "authors": ["Marco Acevedo"], - "cloudinary_id": "reports/assets.change.org/photos/0/yb/id/eYyBIdJOMHpqcty-1600x900-noPad.jpg?1523726975", - "date_downloaded": "2019-04-13", - "date_modified": "2020-06-14", - "date_published": "2015-07-11", - "date_submitted": "2019-06-01", - "editor_notes": "", - "embedding": { - "from_text_hash": "12256b4f3816b968a2224017eac1bf7d5c6b98e5", - "vector": [-0.09368586540222168, 0.02987837791442871, 0.03945714980363846] - }, - "epoch_date_downloaded": 1555113600, - "epoch_date_modified": 1592092800, - "epoch_date_published": 1436572800, - "epoch_date_submitted": 1559347200, - "flag": null, - "image_url": "https://assets.change.org/photos/0/yb/id/eYyBIdJOMHpqcty-1600x900-noPad.jpg?1523726975", - "is_incident_report": true, - "language": "en", - "plain_text": "Videos filled with profanity, sexually explicit material, alcohol, smoking, and drug references - this is what parents are finding on Google’s YouTube Kids app. That’s right - its kids app. Now, parents across the country are calling on Google to remove the app until it can guarantee the total elimination of this inappropriate content.\n\nWhen my neighbors told me about the horrible adult content popping up on the Youtube Kids app, I thought there must be a mistake. Why would Google market an app as “a family-friendly place to explore” and not have proper safeguards in place? Unfortunately, it turned out to be true. And I’ve since learned of the numerous complaints filed to the Federal Trade Commission about this very problem.\n\nEven worse, Google’s response has been laughable. They tell parents to simply flag inappropriate material or set new filters. As a father of two, it makes me angry when a large company like Google doesn’t take responsibility for its kids’ products. Parents are being sold on an app built for kids 5 and under that is supposed to keep them safe from adult content. Parents like myself are joining forces to hold Google accountable.\n\nTell Google to remove the YouTube Kids app until it can live up to its marketing.\n\nThe solution is simple: only allow content pre-approved for ages 5 and under to appear on the app, and don’t allow ads clearly meant for adults. Unless it can live up to expectations, the app should be removed.\n\nParents are not the only ones outraged. The media has blasted Google’s app, calling it “the most anti-family idea ever to come out of Silicon Valley,\" and reporting that it “ignores basic protections for children.”\n\nWith your support, we can get Google to remove YouTube Kids until the proper protections are in place.\n\nThese are examples of videos encountered on YouTube Kids:\n\nA graphic lecture discussing hardcore pornography by Cindy Gallop:\n\nhttps://www.youtube.com/watch?v=EgtcEq7jpAk\n\nHow to make chlorine gas with household products (chemical weapon used in Syria):\n\nhttps://www.youtube.com/watch?v=DF2CXHvh8uI\n\nHow to tie a noose:\n\nhttps://www.youtube.com/watch?v=TpAA2itjI34\n\nHow to throw knives:\n\nhttps://www.youtube.com/watch?v=NGgzn1haQ-E\n\nA guy tasting battery acid:\n\nhttps://www.youtube.com/watch?v=gif-OWNjJSw\n\nHow to use a chainsaw:\n\nhttps://www.youtube.com/watch?v=Kk28thdgCEU\n\nA “Sesame Street” episode dubbed with long strings of expletives:\n\nhttps://www.youtube.com/watch?v=kVkqzE-iiEY\n\nReferences to pedophilia in a homemade video reviewing a “My Little Pony” episode:\n\nhttps://www.youtube.com/watch?v=7K9uH4d-HnU\n\nA DIY video on conducting illegal piracy, featuring pictures of marijuana leaves:\n\nhttps://www.youtube.com/watch?v=dZDF5uqORA0", - "report_number": 10, - "source_domain": "change.org", - "submitters": ["Roman Yampolskiy"], - "tags": [], - "text": "Videos filled with profanity, sexually explicit material, alcohol, smoking, and drug references - this is what parents are finding on Google’s YouTube Kids app. That’s right - its kids app. Now, parents across the country are calling on Google to remove the app until it can guarantee the total elimination of this inappropriate content.\n\nWhen my neighbors told me about the horrible adult content popping up on the Youtube Kids app, I thought there must be a mistake. Why would Google market an app as “a family-friendly place to explore” and not have proper safeguards in place? Unfortunately, it turned out to be true. And I’ve since learned of the numerous complaints filed to the Federal Trade Commission about this very problem.\n\nEven worse, Google’s response has been laughable. They tell parents to simply flag inappropriate material or set new filters. As a father of two, it makes me angry when a large company like Google doesn’t take responsibility for its kids’ products. Parents are being sold on an app built for kids 5 and under that is supposed to keep them safe from adult content. Parents like myself are joining forces to hold Google accountable.\n\nTell Google to remove the YouTube Kids app until it can live up to its marketing.\n\nThe solution is simple: only allow content pre-approved for ages 5 and under to appear on the app, and don’t allow ads clearly meant for adults. Unless it can live up to expectations, the app should be removed.\n\nParents are not the only ones outraged. The media has blasted Google’s app, calling it “the most anti-family idea ever to come out of Silicon Valley,\" and reporting that it “ignores basic protections for children.”\n\nWith your support, we can get Google to remove YouTube Kids until the proper protections are in place.\n\nThese are examples of videos encountered on YouTube Kids:\n\nA graphic lecture discussing hardcore pornography by Cindy Gallop:\n\nhttps://www.youtube.com/watch?v=EgtcEq7jpAk\n\nHow to make chlorine gas with household products (chemical weapon used in Syria):\n\nhttps://www.youtube.com/watch?v=DF2CXHvh8uI\n\nHow to tie a noose:\n\nhttps://www.youtube.com/watch?v=TpAA2itjI34\n\nHow to throw knives:\n\nhttps://www.youtube.com/watch?v=NGgzn1haQ-E\n\nA guy tasting battery acid:\n\nhttps://www.youtube.com/watch?v=gif-OWNjJSw\n\nHow to use a chainsaw:\n\nhttps://www.youtube.com/watch?v=Kk28thdgCEU\n\nA “Sesame Street” episode dubbed with long strings of expletives:\n\nhttps://www.youtube.com/watch?v=kVkqzE-iiEY\n\nReferences to pedophilia in a homemade video reviewing a “My Little Pony” episode:\n\nhttps://www.youtube.com/watch?v=7K9uH4d-HnU\n\nA DIY video on conducting illegal piracy, featuring pictures of marijuana leaves:\n\nhttps://www.youtube.com/watch?v=dZDF5uqORA0", - "title": "Remove YouTube Kids app until it eliminates its inappropriate content", - "description": "Description Remove YouTube Kids app until it eliminates its inappropriate content", - "url": "https://www.change.org/p/remove-youtube-kids-app-until-it-eliminates-its-inappropriate-content", - "user": { - "userId": "63320ce63ec803072c9f529c" - } - } - } -} diff --git a/site/gatsby-site/playwright/fixtures/reports/reportWithTranslations.json b/site/gatsby-site/playwright/fixtures/reports/reportWithTranslations.json deleted file mode 100644 index 9cdcfb4a55..0000000000 --- a/site/gatsby-site/playwright/fixtures/reports/reportWithTranslations.json +++ /dev/null @@ -1,44 +0,0 @@ -{ - "data": { - "report": { - "authors": ["Marco Acevedo"], - "date_downloaded": "2019-04-13", - "date_published": "2015-07-11", - "flag": null, - "image_url": "https://assets.change.org/photos/0/yb/id/eYyBIdJOMHpqcty-1600x900-noPad.jpg?1523726975", - "report_number": 10, - "submitters": ["Roman Yampolskiy"], - "tags": ["Test Tag"], - "text": "## Video still of a reproduced version of Minnie Mouse\n\nWhich appeared on the now-suspended Simple Fun channel Simple Fun.", - "plain_text": "Video still of a reproduced version of Minnie Mouse\n\nWhich appeared on the now-suspended Simple Fun channel Simple Fun.", - "title": "Remove YouTube Kids app until it eliminates its inappropriate content", - "description": "Description YouTube Kids app until it eliminates its inappropriate content", - "url": "https://www.change.org/p/remove-youtube-kids-app-until-it-eliminates-its-inappropriate-content", - "editor_notes": "", - "language": "en", - "translations_es": { - "__typename": "ReportTranslation", - "text": "Este es un texto de prueba que tiene un largo mayor a ochenta caracteres (en español)", - "title": "Este es el Título en español" - }, - "translations_en": { - "__typename": "ReportTranslation", - "text": "", - "title": "" - }, - "translations_fr": { - "__typename": "ReportTranslation", - "text": "Il s'agit d'un texte de test de plus de quatre-vingts caractères - lorem ipsum (en français)", - "title": "C'est le Titre en français" - }, - "translations_ja": { - "__typename": "ReportTranslation", - "text": "解サオライ協立なーづ民手ぶみドに即記朝ぐ奥置ぱで地更トるあて栄厚ぜづを祭屋ん来派どてゃ読速ヘ誌約カタシネ原39業理る。外ヒヱフ社第むせゆ由更混ソエ夕野しりすよ顔飛リの兆基う公言や置17謝后嘘5供フキヌア星集ヘラ辞勘壇崇さびわ。（日本語で）", - "title": "これは日本語でのタイトルです" - }, - "date_modified": "2023-01-01", - "epoch_date_modified": 1672531200, - "is_incident_report": true - } - } -} diff --git a/site/gatsby-site/playwright/fixtures/reports/updateOneReport.json b/site/gatsby-site/playwright/fixtures/reports/updateOneReport.json deleted file mode 100644 index f2d3d2328e..0000000000 --- a/site/gatsby-site/playwright/fixtures/reports/updateOneReport.json +++ /dev/null @@ -1,26 +0,0 @@ -{ - "data": { - "updateOneReport": { - "__typename": "Report", - "authors": ["Marco Acevedo"], - "date_downloaded": "2019-04-13", - "date_modified": "2022-08-29", - "date_published": "2015-07-11", - "editor_notes": "", - "epoch_date_downloaded": 1555113600, - "epoch_date_modified": 1661731200, - 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Why would Google market an app as “a family-friendly place to explore” and not have proper safeguards in place? Unfortunately, it turned out to be true. And I’ve since learned of the numerous complaints filed to the Federal Trade Commission about this very problem.\n\nEven worse, Google’s response has been laughable. They tell parents to simply flag inappropriate material or set new filters. As a father of two, it makes me angry when a large company like Google doesn’t take responsibility for its kids’ products. Parents are being sold on an app built for kids 5 and under that is supposed to keep them safe from adult content. Parents like myself are joining forces to hold Google accountable.\n\nTell Google to remove the YouTube Kids app until it can live up to its marketing.\n\nThe solution is simple: only allow content pre-approved for ages 5 and under to appear on the app, and don’t allow ads clearly meant for adults. Unless it can live up to expectations, the app should be removed.\n\nParents are not the only ones outraged. The media has blasted Google’s app, calling it “the most anti-family idea ever to come out of Silicon Valley,\" and reporting that it “ignores basic protections for children.”\n\nWith your support, we can get Google to remove YouTube Kids until the proper protections are in place.\n\nThese are examples of videos encountered on YouTube Kids:\n\nA graphic lecture discussing hardcore pornography by Cindy Gallop:\n\nhttps://www.youtube.com/watch?v=EgtcEq7jpAk\n\nHow to make chlorine gas with household products (chemical weapon used in Syria):\n\nhttps://www.youtube.com/watch?v=DF2CXHvh8uI\n\nHow to tie a noose:\n\nhttps://www.youtube.com/watch?v=TpAA2itjI34\n\nHow to throw knives:\n\nhttps://www.youtube.com/watch?v=NGgzn1haQ-E\n\nA guy tasting battery acid:\n\nhttps://www.youtube.com/watch?v=gif-OWNjJSw\n\nHow to use a chainsaw:\n\nhttps://www.youtube.com/watch?v=Kk28thdgCEU\n\nA “Sesame Street” episode dubbed with long strings of expletives:\n\nhttps://www.youtube.com/watch?v=kVkqzE-iiEY\n\nReferences to pedophilia in a homemade video reviewing a “My Little Pony” episode:\n\nhttps://www.youtube.com/watch?v=7K9uH4d-HnU\n\nA DIY video on conducting illegal piracy, featuring pictures of marijuana leaves:\n\nhttps://www.youtube.com/watch?v=dZDF5uqORA0\n", - "report_number": 10, - "submitters": ["Roman Yampolskiy"], - "tags": [], - "text": "Videos filled with profanity, sexually explicit material, alcohol, smoking, and drug references - this is what parents are finding on Google’s YouTube Kids app. That’s right - its kids app. Now, parents across the country are calling on Google to remove the app until it can guarantee the total elimination of this inappropriate content.\n\nWhen my neighbors told me about the horrible adult content popping up on the Youtube Kids app, I thought there must be a mistake. Why would Google market an app as “a family-friendly place to explore” and not have proper safeguards in place? Unfortunately, it turned out to be true. And I’ve since learned of the numerous complaints filed to the Federal Trade Commission about this very problem.\n\nEven worse, Google’s response has been laughable. They tell parents to simply flag inappropriate material or set new filters. As a father of two, it makes me angry when a large company like Google doesn’t take responsibility for its kids’ products. Parents are being sold on an app built for kids 5 and under that is supposed to keep them safe from adult content. Parents like myself are joining forces to hold Google accountable.\n\nTell Google to remove the YouTube Kids app until it can live up to its marketing.\n\nThe solution is simple: only allow content pre-approved for ages 5 and under to appear on the app, and don’t allow ads clearly meant for adults. Unless it can live up to expectations, the app should be removed.\n\nParents are not the only ones outraged. The media has blasted Google’s app, calling it “the most anti-family idea ever to come out of Silicon Valley,\" and reporting that it “ignores basic protections for children.”\n\nWith your support, we can get Google to remove YouTube Kids until the proper protections are in place.\n\nThese are examples of videos encountered on YouTube Kids:\n\nA graphic lecture discussing hardcore pornography by Cindy Gallop:\n\nhttps://www.youtube.com/watch?v=EgtcEq7jpAk\n\nHow to make chlorine gas with household products (chemical weapon used in Syria):\n\nhttps://www.youtube.com/watch?v=DF2CXHvh8uI\n\nHow to tie a noose:\n\nhttps://www.youtube.com/watch?v=TpAA2itjI34\n\nHow to throw knives:\n\nhttps://www.youtube.com/watch?v=NGgzn1haQ-E\n\nA guy tasting battery acid:\n\nhttps://www.youtube.com/watch?v=gif-OWNjJSw\n\nHow to use a chainsaw:\n\nhttps://www.youtube.com/watch?v=Kk28thdgCEU\n\nA “Sesame Street” episode dubbed with long strings of expletives:\n\nhttps://www.youtube.com/watch?v=kVkqzE-iiEY\n\nReferences to pedophilia in a homemade video reviewing a “My Little Pony” episode:\n\nhttps://www.youtube.com/watch?v=7K9uH4d-HnU\n\nA DIY video on conducting illegal piracy, featuring pictures of marijuana leaves:\n\nhttps://www.youtube.com/watch?v=dZDF5uqORA0", - "title": "Remove YouTube Kids app until it eliminates its inappropriate content", - "description": "Description Remove YouTube Kids app until it eliminates its inappropriate content", - "url": "https://www.change.org/p/remove-youtube-kids-app-until-it-eliminates-its-inappropriate-content" - } - } -} diff --git a/site/gatsby-site/playwright/fixtures/reports/updateOneReportTranslation.json b/site/gatsby-site/playwright/fixtures/reports/updateOneReportTranslation.json deleted file mode 100644 index 37906edb16..0000000000 --- a/site/gatsby-site/playwright/fixtures/reports/updateOneReportTranslation.json +++ /dev/null @@ -1,10 +0,0 @@ -{ - "data": { - "updateOneReportTranslation": { - "__typename": "Report", - "report_number": 10, - "text": "Videos filled with profanity, sexually explicit material, alcohol, smoking, and drug references - this is what parents are finding on Google’s YouTube Kids app. That’s right - its kids app. Now, parents across the country are calling on Google to remove the app until it can guarantee the total elimination of this inappropriate content.\n\nWhen my neighbors told me about the horrible adult content popping up on the Youtube Kids app, I thought there must be a mistake. Why would Google market an app as “a family-friendly place to explore” and not have proper safeguards in place? Unfortunately, it turned out to be true. And I’ve since learned of the numerous complaints filed to the Federal Trade Commission about this very problem.\n\nEven worse, Google’s response has been laughable. They tell parents to simply flag inappropriate material or set new filters. As a father of two, it makes me angry when a large company like Google doesn’t take responsibility for its kids’ products. Parents are being sold on an app built for kids 5 and under that is supposed to keep them safe from adult content. Parents like myself are joining forces to hold Google accountable.\n\nTell Google to remove the YouTube Kids app until it can live up to its marketing.\n\nThe solution is simple: only allow content pre-approved for ages 5 and under to appear on the app, and don’t allow ads clearly meant for adults. Unless it can live up to expectations, the app should be removed.\n\nParents are not the only ones outraged. The media has blasted Google’s app, calling it “the most anti-family idea ever to come out of Silicon Valley,\" and reporting that it “ignores basic protections for children.”\n\nWith your support, we can get Google to remove YouTube Kids until the proper protections are in place.\n\nThese are examples of videos encountered on YouTube Kids:\n\nA graphic lecture discussing hardcore pornography by Cindy Gallop:\n\nhttps://www.youtube.com/watch?v=EgtcEq7jpAk\n\nHow to make chlorine gas with household products (chemical weapon used in Syria):\n\nhttps://www.youtube.com/watch?v=DF2CXHvh8uI\n\nHow to tie a noose:\n\nhttps://www.youtube.com/watch?v=TpAA2itjI34\n\nHow to throw knives:\n\nhttps://www.youtube.com/watch?v=NGgzn1haQ-E\n\nA guy tasting battery acid:\n\nhttps://www.youtube.com/watch?v=gif-OWNjJSw\n\nHow to use a chainsaw:\n\nhttps://www.youtube.com/watch?v=Kk28thdgCEU\n\nA “Sesame Street” episode dubbed with long strings of expletives:\n\nhttps://www.youtube.com/watch?v=kVkqzE-iiEY\n\nReferences to pedophilia in a homemade video reviewing a “My Little Pony” episode:\n\nhttps://www.youtube.com/watch?v=7K9uH4d-HnU\n\nA DIY video on conducting illegal piracy, featuring pictures of marijuana leaves:\n\nhttps://www.youtube.com/watch?v=dZDF5uqORA0", - "title": "Remove YouTube Kids app until it eliminates its inappropriate content" - } - } -} \ No newline at end of file diff --git a/site/gatsby-site/playwright/fixtures/subscriptions/empty-subscriptions.json b/site/gatsby-site/playwright/fixtures/subscriptions/empty-subscriptions.json deleted file mode 100644 index 027e2860dc..0000000000 --- a/site/gatsby-site/playwright/fixtures/subscriptions/empty-subscriptions.json +++ /dev/null @@ -1,6 +0,0 @@ -{ - "data": { - "subscriptions": [ - ] - } -} diff --git a/site/gatsby-site/playwright/fixtures/subscriptions/subscriptions-to-deleted-incidents.json b/site/gatsby-site/playwright/fixtures/subscriptions/subscriptions-to-deleted-incidents.json deleted file mode 100644 index e9616d4d30..0000000000 --- a/site/gatsby-site/playwright/fixtures/subscriptions/subscriptions-to-deleted-incidents.json +++ /dev/null @@ -1,63 +0,0 @@ -{ - "data": { - "subscriptions": [ - { - "_id": "6335e4ce290efe73c71b2e50", - "incident_id": null, - "entityId": null, - "type": "new-incidents" - }, - { - "_id": "6335e4ce290efe73c71b2e55", - "incident_id": { - "incident_id": 11, - "title": "Northpointe Risk Models" - }, - "entityId": null, - "type": "incident" - }, - { - "_id": "6335fcd0290efe73c71b2e60", - "incident_id": { - "incident_id": 10, - "title": "Kronos’s Algorithm Allegedly Created Scheduling and Financial Issues for Starbucks Employees" - }, - "entityId": null, - "type": "incident" - }, - { - "_id": "63361ade290efe73c71b2e61", - "incident_id": { - "incident_id": 12, - "title": "Common Biases of Vector Embeddings" - }, - "entityId": null, - "type": "incident" - }, - { - "_id": "63361ade290efe73c71b2e61", - "incident_id": null, - "entityId": null, - "type": "incident" - }, - { - "_id": "63361ade290efe73c71b2e70", - "incident_id": null, - "entityId": { - "entity_id": "google", - "name": "Google" - }, - "type": "entity" - }, - { - "_id": "63361ade290efe73c71b2e71", - "incident_id": null, - "entityId": { - "entity_id": "uk-home-office", - "name": "UK Home Office" - }, - "type": "entity" - } - ] - } -} diff --git a/site/gatsby-site/playwright/fixtures/subscriptions/subscriptions.json b/site/gatsby-site/playwright/fixtures/subscriptions/subscriptions.json deleted file mode 100644 index 63bcd9bd69..0000000000 --- a/site/gatsby-site/playwright/fixtures/subscriptions/subscriptions.json +++ /dev/null @@ -1,57 +0,0 @@ -{ - "data": { - "subscriptions": [ - { - "_id": "6335e4ce290efe73c71b2e50", - "incident_id": null, - "entityId": null, - "type": "new-incidents" - }, - { - "_id": "6335e4ce290efe73c71b2e55", - "incident_id": { - "incident_id": 11, - "title": "Northpointe Risk Models" - }, - "entityId": null, - "type": "incident" - }, - { - "_id": "6335fcd0290efe73c71b2e60", - "incident_id": { - "incident_id": 10, - "title": "Kronos’s Algorithm Allegedly Created Scheduling and Financial Issues for Starbucks Employees" - }, - "entityId": null, - "type": "incident" - }, - { - "_id": "63361ade290efe73c71b2e61", - "incident_id": { - "incident_id": 12, - "title": "Common Biases of Vector Embeddings" - }, - "entityId": null, - "type": "incident" - }, - { - "_id": "63361ade290efe73c71b2e70", - "incident_id": null, - "entityId": { - "entity_id": "google", - "name": "Google" - }, - "type": "entity" - }, - { - "_id": "63361ade290efe73c71b2e71", - "incident_id": null, - "entityId": { - "entity_id": "uk-home-office", - "name": "UK Home Office" - }, - "type": "entity" - } - ] - } -} diff --git a/site/gatsby-site/playwright/fixtures/users/users.json b/site/gatsby-site/playwright/fixtures/users/users.json deleted file mode 100644 index 3c59427c3e..0000000000 --- a/site/gatsby-site/playwright/fixtures/users/users.json +++ /dev/null @@ -1,102 +0,0 @@ -{ - "data": { - "users": [ - { - "__typename": "User", - "adminData": { - "__typename": "UserAdminDatum", - "creationDate": "2022-09-26T20:34:46Z", - "disabled": false, - "email": "pablo@botsfactory.io", - "lastAuthenticationDate": "2023-06-06T00:07:30Z" - }, - "first_name": "Test", - "last_name": "User", - "roles": [ - "admin" - ], - "userId": "63320ce63ec803072c9f529c" - }, - { - "__typename": "User", - "adminData": { - "__typename": "UserAdminDatum", - "creationDate": "2022-09-27T00:07:21Z", - "disabled": false, - "email": "pablo+1@botsfactory.io", - "lastAuthenticationDate": "2022-09-27T17:34:37Z" - }, - "first_name": null, - "last_name": null, - "roles": [ - "subscriber" - ], - "userId": "63323eb94c44d44dee0887cd" - }, - { - "__typename": "User", - "adminData": { - "__typename": "UserAdminDatum", - "creationDate": "2022-09-27T17:00:26Z", - "disabled": false, - "email": "mail+1@cesarvarela.com", - "lastAuthenticationDate": "2022-09-27T17:00:36Z" - }, - "first_name": null, - "last_name": null, - "roles": [ - "subscriber" - ], - "userId": "63332c2ae42c6c12924ffc57" - }, - { - "__typename": "User", - "adminData": { - "__typename": "UserAdminDatum", - "creationDate": "2022-10-03T23:48:44Z", - "disabled": false, - "email": "pablo+2@botsfactory.io", - "lastAuthenticationDate": "2022-10-03T23:48:58Z" - }, - "first_name": "Cesar", - "last_name": "Varela", - "roles": [ - "subscriber" - ], - "userId": "633b74dc95edb45d34a95895" - }, - { - "__typename": "User", - "adminData": { - "__typename": "UserAdminDatum", - "creationDate": "2022-10-03T23:48:44Z", - "disabled": false, - "email": "sean@mcgregor.com", - "lastAuthenticationDate": "2022-10-03T23:48:58Z" - }, - "first_name": "Sean", - "last_name": "McGregor", - "roles": [ - "subscriber" - ], - "userId": "619b47ea5eed5334edfa3bbc" - }, - { - "__typename": "User", - "adminData": { - "__typename": "UserAdminDatum", - "creationDate": null, - "disabled": null, - "email": null, - "lastAuthenticationDate": null - }, - "first_name": null, - "last_name": null, - "roles": [ - "subscriber" - ], - "userId": "63e5d7a329e0d8b04a5092a6" - } - ] - } -} \ No newline at end of file diff --git a/site/gatsby-site/playwright/memory-mongo.ts b/site/gatsby-site/playwright/memory-mongo.ts index dd7b5fa0e7..fa3e640f5d 100644 --- a/site/gatsby-site/playwright/memory-mongo.ts +++ b/site/gatsby-site/playwright/memory-mongo.ts @@ -9,11 +9,14 @@ import entities from './seeds/aiidprod/entities'; import reports_es from './seeds/translations/reports_es'; import classifications from './seeds/aiidprod/classifications'; import taxa from './seeds/aiidprod/taxa'; +import candidates from './seeds/aiidprod/candidates'; import duplicates from './seeds/aiidprod/duplicates'; import users from './seeds/customData/users'; import subscriptions from './seeds/customData/subscriptions'; +import reportsHistory from './seeds/history/reportsHistory'; +import incidentsHistory from './seeds/history/incidentsHistory'; export const init = async (extra?: Record[]>>, { drop } = { drop: false }) => { @@ -25,6 +28,7 @@ export const init = async (extra?: Record { + const newDate = new Date( + new Date().getTime() - 86400000 * i // i days ago + ) + + const day = newDate.getDate(); + const month = newDate.getMonth(); + const year = newDate.getFullYear(); + + return new Date(year, month, day).toISOString().slice(0, 10); + }) + +const candidates: DBCandidate[] = [ + { + match: true, + title: "Candidate 1", + url: "https://www.candidate1.com", + date_published: dates[0], + dismissed: false, + similarity: 0.99, + matching_keywords: ["keyword1", "keyword2"], + matching_harm_keywords: ["harmkeyword1"], + matching_entities: ["entity1"], + text: "Candidate 1 Text", + plain_text: "Candidate 1 Plain Text" + }, + { + match: true, + title: "Candidate 2", + url: "https://www.candidate2.com", + date_published: dates[1], + dismissed: false, + similarity: 0.85, + matching_keywords: ["keyword3"], + matching_harm_keywords: ["harmkeyword2"], + matching_entities: ["entity2"], + text: "Candidate 2 Text", + plain_text: "Candidate 2 Plain Text" + }, + { + match: false, + title: "Candidate 3", + url: "https://www.candidate3.com", + date_published: dates[2], + dismissed: false, + similarity: 0.99, + matching_keywords: ["keyword4", "keyword5"], + matching_harm_keywords: ["harmkeyword3"], + matching_entities: [], + text: "Candidate 3 Text", + plain_text: "Candidate 3 Plain Text" + }, + { + match: true, + title: "Candidate 4", + url: "https://www.candidate4.com", + date_published: dates[3], + dismissed: false, + similarity: 0.99, + matching_keywords: ["keyword6", "keyword7"], + matching_harm_keywords: ["harmkeyword4"], + matching_entities: ["entity2"], + text: "Candidate 4 Text", + plain_text: "Candidate 4 Plain Text" + }, +]; + +export default candidates; \ No newline at end of file diff --git a/site/gatsby-site/playwright/seeds/aiidprod/incidents.ts b/site/gatsby-site/playwright/seeds/aiidprod/incidents.ts index dd360419ab..ecf90e2521 100644 --- a/site/gatsby-site/playwright/seeds/aiidprod/incidents.ts +++ b/site/gatsby-site/playwright/seeds/aiidprod/incidents.ts @@ -11,6 +11,7 @@ const incidents: DBIncident[] = [ "Alleged harmed or nearly harmed parties": ["entity-3"], editors: ["user1"], reports: [1], + implicated_systems: ["entity-1"], // TODO: this aren't required but break the build if missing editor_notes: "", @@ -28,6 +29,7 @@ const incidents: DBIncident[] = [ "Alleged harmed or nearly harmed parties": ["entity-3"], editors: ["user1"], reports: [2], + implicated_systems: ["entity-1"], // TODO: this aren't required but break the build if missing editor_notes: "", @@ -86,6 +88,7 @@ const incidents: DBIncident[] = [ }, // this field is currently present in the database but not mapped to any graphql fueld // "created_at": 1407974400000 + implicated_systems: ["entity-1"], }, ] diff --git a/site/gatsby-site/playwright/seeds/aiidprod/reports.ts b/site/gatsby-site/playwright/seeds/aiidprod/reports.ts index 3a1dc94eb0..de103d826c 100644 --- a/site/gatsby-site/playwright/seeds/aiidprod/reports.ts +++ b/site/gatsby-site/playwright/seeds/aiidprod/reports.ts @@ -6,24 +6,29 @@ const items: DBReport[] = [ report_number: 1, title: "Report 1", authors: ["author1"], - cloudinary_id: "cloudinary_id1", - date_downloaded: new Date("2020-01-01"), - date_modified: new Date("2020-01-01"), - date_published: new Date("2020-01-01"), - date_submitted: new Date("2020-01-01"), + cloudinary_id: "reports/upload.wikimedia.org/wikipedia/commons/e/e9/Felis_silvestris_silvestris_small_gradual_decrease_of_quality.png", + date_downloaded: new Date("2020-01-01T00:00:00.000Z"), + date_modified: new Date("2024-10-29T00:49:45.572Z"), + date_published: new Date("2020-01-01T00:00:00.000Z"), + date_submitted: new Date("2020-01-01T00:00:00.000Z"), epoch_date_downloaded: 1577836800, - epoch_date_modified: 1577836800, + epoch_date_modified: 1730162985, epoch_date_published: 1577836800, epoch_date_submitted: 1577836800, - image_url: "image_url1", + image_url: "https://upload.wikimedia.org/wikipedia/commons/e/e9/Felis_silvestris_silvestris_small_gradual_decrease_of_quality.png", language: "en", - plain_text: "plain_text1", - text: "text1", - source_domain: "source_domain1", + plain_text: "A malfunction in the AI-driven traffic management system caused widespread traffic congestion and several accidents in the downtown area, leading to a review of the system’s safety protocols.\n", + text: "A malfunction in the AI-driven traffic management system caused widespread traffic congestion and several accidents in the downtown area, leading to a review of the system’s safety protocols.", + source_domain: "report1.com", submitters: ["submitter1"], tags: ["tag1"], url: "https://report1.com", user: "user1", + is_incident_report: true, + editor_notes: null, + flag: null, + inputs_outputs: null, + quiet: null, }, { title: "Report 2", diff --git a/site/gatsby-site/playwright/seeds/aiidprod/submissions.ts b/site/gatsby-site/playwright/seeds/aiidprod/submissions.ts index feaf3c03f9..2d2f5c20ca 100644 --- a/site/gatsby-site/playwright/seeds/aiidprod/submissions.ts +++ b/site/gatsby-site/playwright/seeds/aiidprod/submissions.ts @@ -24,6 +24,7 @@ const submissions: DBSubmission[] = [ title: "Sample title", url: "http://example.com", user: "user1", + implicated_systems: ["entity-1"], incident_title: "Incident title", incident_date: "2021-09-14", editor_notes: "This is an editor note", diff --git a/site/gatsby-site/playwright/seeds/history/incidentsHistory.ts b/site/gatsby-site/playwright/seeds/history/incidentsHistory.ts new file mode 100644 index 0000000000..f9cdd58c5c --- /dev/null +++ b/site/gatsby-site/playwright/seeds/history/incidentsHistory.ts @@ -0,0 +1,125 @@ +import { DBIncidentHistory } from '../../../server/interfaces'; + +const items: DBIncidentHistory[] = [ + { + "incident_id": 1, + "title": "Lorem ipsum dolor sit amet, consectetur adipiscing elit", + "description": "Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation", + "date": "2020-01-01", + "Alleged deployer of AI system": [ + "entity-1" + ], + "Alleged developer of AI system": [ + "entity-2" + ], + "Alleged harmed or nearly harmed parties": [ + "entity-3" + ], + "implicated_systems": ["entity-1"], + "editors": [ + "user1", + ], + "reports": [ + 1 + ], + "editor_notes": "", + "nlp_similar_incidents": [], + "editor_similar_incidents": [], + "editor_dissimilar_incidents": [], + "flagged_dissimilar_incidents": [], + "epoch_date_modified": 1730242189, + "modifiedBy": "user1" + }, + { + "incident_id": 1, + "title": "Incident 1", + "description": "Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation", + "date": "2020-01-01", + "Alleged deployer of AI system": [ + "entity-1" + ], + "Alleged developer of AI system": [ + "entity-2" + ], + "Alleged harmed or nearly harmed parties": [ + "entity-3" + ], + "implicated_systems": ["entity-1"], + "editors": [ + "user1", + "619b47ea5eed5334edfa3bbc" + ], + "reports": [ + 1 + ], + "editor_notes": "", + "nlp_similar_incidents": [], + "editor_similar_incidents": [], + "editor_dissimilar_incidents": [], + "flagged_dissimilar_incidents": [], + "epoch_date_modified": 1730242374, + "modifiedBy": "619b47ea5eed5334edfa3bbc" + }, + { + "incident_id": 1, + "title": "Incident 1", + "description": "Incident 1 Description", + "date": "2020-01-01", + "Alleged deployer of AI system": [ + "entity-1" + ], + "Alleged developer of AI system": [ + "entity-2" + ], + "Alleged harmed or nearly harmed parties": [ + "entity-3" + ], + "implicated_systems": ["entity-1"], + "editors": [ + "user1", + "619b47ea5eed5334edfa3bbc" + ], + "reports": [ + 1, 2 + ], + "editor_notes": "", + "nlp_similar_incidents": [], + "editor_similar_incidents": [], + "editor_dissimilar_incidents": [], + "flagged_dissimilar_incidents": [], + "epoch_date_modified": 1730242383, + "modifiedBy": "619b47ea5eed5334edfa3bbc" + }, + { + "incident_id": 1, + "title": "Incident 1", + "description": "Incident 1 Description", + "date": "2020-01-01", + "Alleged deployer of AI system": [ + "entity-1" + ], + "Alleged developer of AI system": [ + "entity-2" + ], + "Alleged harmed or nearly harmed parties": [ + "entity-3" + ], + "implicated_systems": ["entity-1"], + "editors": [ + "user1", + "619b47ea5eed5334edfa3bbc", + ], + "reports": [ + 1 + ], + "editor_notes": "", + "nlp_similar_incidents": [], + "editor_similar_incidents": [], + "editor_dissimilar_incidents": [], + "flagged_dissimilar_incidents": [], + "epoch_date_modified": 1730242399, + "modifiedBy": "user1" + } +] + +export default items; \ No newline at end of file diff --git a/site/gatsby-site/playwright/seeds/history/reportsHistory.ts b/site/gatsby-site/playwright/seeds/history/reportsHistory.ts new file mode 100644 index 0000000000..8cc90250aa --- /dev/null +++ b/site/gatsby-site/playwright/seeds/history/reportsHistory.ts @@ -0,0 +1,126 @@ +import { DBReportHistory } from '../../../server/interfaces'; + +const items: DBReportHistory[] = [ + { + _id: "6720309d1b3f74e536ba788f", + report_number: 1, + title: "Lorem ipsum dolor sit amet, consectetur", + authors: ["author1"], + cloudinary_id: "reports/invalidimage.com", + date_downloaded: new Date("2020-01-01T00:00:00.000Z"), + date_modified: new Date("2024-10-29T00:47:22.417Z"), + date_published: new Date("2020-01-01T00:00:00.000Z"), + date_submitted: new Date("2020-01-01T00:00:00.000Z"), + epoch_date_downloaded: 1577836800, + epoch_date_modified: 1730162842, + epoch_date_published: 1577836800, + epoch_date_submitted: 1577836800, + image_url: "https://invalidimage.com", + language: "en", + plain_text: "Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum\n", + text: "Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum", + source_domain: "report1.com", + submitters: ["submitter1"], + tags: ["tag1"], + url: "https://report1.com", + user: "user1", + is_incident_report: true, + editor_notes: null, + flag: null, + inputs_outputs: null, + quiet: null, + modifiedBy: "user1" + }, + { + _id: "672030c91b3f74e536ba7890", + report_number: 1, + title: "Report 1", + authors: ["author1"], + cloudinary_id: "reports/invalidimage.com", + date_downloaded: new Date("2020-01-01T00:00:00.000Z"), + date_modified: new Date("2024-10-29T00:48:06.197Z"), + date_published: new Date("2020-01-01T00:00:00.000Z"), + date_submitted: new Date("2020-01-01T00:00:00.000Z"), + epoch_date_downloaded: 1577836800, + epoch_date_modified: 1730162886, + epoch_date_published: 1577836800, + epoch_date_submitted: 1577836800, + image_url: "https://invalidimage.com", + language: "en", + plain_text: "Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum\n", + text: "Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum", + source_domain: "report1.com", + submitters: ["submitter1"], + tags: ["tag1"], + url: "https://report1.com", + user: "user1", + is_incident_report: true, + editor_notes: null, + flag: null, + inputs_outputs: null, + quiet: null, + modifiedBy: "619b47ea5eed5334edfa3bbc" + }, + { + _id: "672030e81b3f74e536ba7891", + report_number: 1, + title: "Report 1", + authors: ["author1"], + cloudinary_id: "reports/invalidimage.com", + date_downloaded: new Date("2020-01-01T00:00:00.000Z"), + date_modified: new Date("2024-10-29T00:48:37.349Z"), + date_published: new Date("2020-01-01T00:00:00.000Z"), + date_submitted: new Date("2020-01-01T00:00:00.000Z"), + epoch_date_downloaded: 1577836800, + epoch_date_modified: 1730162917, + epoch_date_published: 1577836800, + epoch_date_submitted: 1577836800, + image_url: "https://invalidimage.com", + language: "en", + plain_text: "A malfunction in the AI-driven traffic management system caused widespread traffic congestion and several accidents in the downtown area, leading to a review of the system’s safety protocols.\n", + text: "A malfunction in the AI-driven traffic management system caused widespread traffic congestion and several accidents in the downtown area, leading to a review of the system’s safety protocols.", + source_domain: "report1.com", + submitters: ["submitter1"], + tags: ["tag1"], + url: "https://report1.com", + user: "user1", + is_incident_report: true, + editor_notes: null, + flag: null, + inputs_outputs: null, + quiet: null, + modifiedBy: "619b47ea5eed5334edfa3bbc" + }, + { + _id: "6720312c1b3f74e536ba7892", + report_number: 1, + title: "Report 1", + authors: ["author1"], + cloudinary_id: "reports/upload.wikimedia.org/wikipedia/commons/e/e9/Felis_silvestris_silvestris_small_gradual_decrease_of_quality.png", + date_downloaded: new Date("2020-01-01T00:00:00.000Z"), + date_modified: new Date("2024-10-29T00:49:45.572Z"), + date_published: new Date("2020-01-01T00:00:00.000Z"), + date_submitted: new Date("2020-01-01T00:00:00.000Z"), + epoch_date_downloaded: 1577836800, + epoch_date_modified: 1730162985, + epoch_date_published: 1577836800, + epoch_date_submitted: 1577836800, + image_url: "https://upload.wikimedia.org/wikipedia/commons/e/e9/Felis_silvestris_silvestris_small_gradual_decrease_of_quality.png", + language: "en", + plain_text: "A malfunction in the AI-driven traffic management system caused widespread traffic congestion and several accidents in the downtown area, leading to a review of the system’s safety protocols.\n", + text: "A malfunction in the AI-driven traffic management system caused widespread traffic congestion and several accidents in the downtown area, leading to a review of the system’s safety protocols.", + source_domain: "report1.com", + submitters: ["submitter1"], + tags: ["tag1"], + url: "https://report1.com", + user: "user1", + is_incident_report: true, + editor_notes: null, + flag: null, + inputs_outputs: null, + quiet: null, + modifiedBy: "619b47ea5eed5334edfa3bbc" + }, +] + +export default items; \ No newline at end of file diff --git a/site/gatsby-site/playwright/utils.ts b/site/gatsby-site/playwright/utils.ts index 0ffb38328e..07bd25b97c 100644 --- a/site/gatsby-site/playwright/utils.ts +++ b/site/gatsby-site/playwright/utils.ts @@ -6,6 +6,8 @@ import assert from 'node:assert'; import fs from 'fs'; import path from 'path'; import * as memoryMongo from './memory-mongo'; +import { algoliaMock } from './fixtures/algoliaMock'; +import siteConfig from '../config'; declare module '@playwright/test' { interface Request { @@ -20,6 +22,8 @@ type TestFixtures = { runOnlyOnEmptyEnvironment: () => Promise, login: (username: string, password: string, options?: { customData?: Record }) => Promise, retryDelay?: [({ }: {}, use: () => Promise, testInfo: { retry: number }) => Promise, { auto: true }], + runOnlyInProduction: () => Promise, + runAnywhereExceptProduction: () => Promise, }; const getUserIdFromLocalStorage = async (page: Page) => { @@ -117,6 +121,22 @@ export const test = base.extend({ await use(); }, { auto: true }], + + runOnlyInProduction: async ({ }, use, testInfo) => { + if (config.SITE_URL !== siteConfig.gatsby.siteUrl) { + testInfo.skip(); + } + + await use(null); + }, + + runAnywhereExceptProduction: async ({ }, use, testInfo) => { + if (config.SITE_URL === siteConfig.gatsby.siteUrl) { + testInfo.skip(); + } + + await use(null); + } }); // SEE: https://playwright.dev/docs/api/class-page#page-wait-for-request @@ -282,3 +302,40 @@ export async function fillAutoComplete(page: Page, selector: string, sequence: s await page.getByText(target).first().click({ timeout: 1000 }); }).toPass(); } + +export function getLanguages() { + return [ + { code: 'en', hrefLang: 'en-US', name: 'English', localName: 'English', langDir: 'ltr', dateFormat: 'MM/DD/YYYY' }, + { code: 'es', hrefLang: 'es', name: 'Spanish', localName: 'Español', langDir: 'ltr', dateFormat: 'DD-MM-YYYY' }, + { code: 'fr', hrefLang: 'fr', name: 'French', localName: 'Français', langDir: 'ltr', dateFormat: 'DD-MM-YYYY' }, + { code: 'ja', hrefLang: 'ja', name: 'Japanese', localName: '日本語', langDir: 'ltr', dateFormat: 'YYYY/MM/DD' }, + ]; +} + +// TODO: this mock should pull from the database instead of being hardcoded +export async function mockAlgolia(page: Page) { + + await page.route('**/*.algolia.net/1/indexes/*/queries*', async route => { + const response = await route.fetch(); + + await route.fulfill({ + status: 200, + json: algoliaMock, + headers: { + ...response.headers(), + } + }); + }); + + await page.route('**/*.algolianet.com/1/indexes/*/queries*', async route => { + const response = await route.fetch(); + + await route.fulfill({ + status: 200, + json: algoliaMock, + headers: { + ...response.headers(), + } + }); + }); +} \ No newline at end of file diff --git a/site/gatsby-site/plugins/gatsby-theme-i18n/gatsby-node.js b/site/gatsby-site/plugins/gatsby-theme-i18n/gatsby-node.js index 2a3cf7eec6..bfc953315c 100644 --- a/site/gatsby-site/plugins/gatsby-theme-i18n/gatsby-node.js +++ b/site/gatsby-site/plugins/gatsby-theme-i18n/gatsby-node.js @@ -220,50 +220,40 @@ exports.onCreatePage = ({ page, actions }, themeOptions) => { const [template, mdxFile] = page.component.split(`?__contentFilePath=`); - // If the mdxFile path has a language, let's strip the language from it. - // ex: index.de.mdx ==> index.mdx + // Process the mdxFile if it exists if (mdxFile) { - let thePath = path.dirname(mdxFile); + const directory = path.dirname(mdxFile); // Get the directory path - let fileName = path.basename(mdxFile); + const fileName = path.basename(mdxFile, `.mdx`); // Extract the file name without `.mdx` - let ext = path.extname(mdxFile); + const ext = path.extname(mdxFile); // Get the file extension - if (ext !== '.mdx') { - throw new Error(`Unexpected file extension in mdx path parsing: ${mdxFile}`); + if (ext !== `.mdx`) { + throw new Error(`Unexpected file extension in MDX path parsing: ${mdxFile}`); } - // Split the filename in three parts split by the dot. We expect two or three components. - let fileNamePieces = fileName.split('.'); + // Extract the locale from the file name if present (e.g., "index.es" → "es") + const nameParts = fileName.split(`.`); - if (fileNamePieces.length == 2) { - // ex: index.mdx - theFilePath = mdxFile; - } else if (fileNamePieces.length == 3) { - // ex: index.es.mdx - // Keep everything except the language code. - theFilePath = `${path.join(thePath, fileNamePieces.at(0))}${ext}`; - } else { - throw new Error(`Unexpected file format in mdx path parsing: ${mdxFile}`); - } - - // If we use a non-default language, and the language file is on the disk, then use it. - if (ext === '.mdx' && locale.code !== defaultLang) { - // ex: /path/up/to/lang/code - let potentialPath = path.join(path.dirname(thePath), fileNamePieces.at(0)); + let baseName = nameParts[0]; // Default to the base name without locale - // ex: /path/up/to/lang/code.es.mdx - let potentialLangfile = `${potentialPath}.${locale.code}${ext}`; + // If it's a non-default language and the file exists, use the localized file + if (locale.code !== defaultLang) { + const localizedFile = path.join(directory, `${baseName}.${locale.code}${ext}`); - if (fs.existsSync(potentialLangfile)) { - theFilePath = potentialLangfile; + if (fs.existsSync(localizedFile)) { + theFilePath = `${template}?__contentFilePath=${localizedFile}`; } else { - // Nothing to render if file doen't exist. - theFilePath = ''; + // If no localized file exists, use the default file + theFilePath = `${template}?__contentFilePath=${path.join( + directory, + `${baseName}${ext}` + )}`; } + } else { + // For the default language, strip the locale from the path if present + theFilePath = `${template}?__contentFilePath=${path.join(directory, `${baseName}${ext}`)}`; } - - theFilePath = `${template}?__contentFilePath=${theFilePath}`; } const newPage = { @@ -293,7 +283,6 @@ exports.onCreatePage = ({ page, actions }, themeOptions) => { // Check if the page is a localized 404 if (newPage.path.match(/^\/[a-z]{2}\/404\/$/)) { - // Match all paths starting with this code (apart from other valid paths) newPage.matchPath = `/${locale.code}/*`; } diff --git a/site/gatsby-site/postBuild/processNotifications.js b/site/gatsby-site/postBuild/processNotifications.js deleted file mode 100644 index 2a7bc2949d..0000000000 --- a/site/gatsby-site/postBuild/processNotifications.js +++ /dev/null @@ -1,49 +0,0 @@ -const { gql, ApolloClient, HttpLink, InMemoryCache } = require('@apollo/client'); - -const config = require('../config'); - -const Realm = require('realm-web'); - -const { fetch } = require('cross-fetch'); - -const getValidAccessToken = async () => { - const realmApp = new Realm.App({ - id: config.realm.review_db.realm_app_id, - }); - - let credentials = Realm.Credentials.apiKey(config.realm.graphqlApiKey); - - await realmApp.logIn(credentials); - - return realmApp.currentUser.accessToken; -}; - -const client = new ApolloClient({ - link: new HttpLink({ - uri: `https://services.cloud.mongodb.com/api/client/v2.0/app/${config.realm.production_db.realm_app_id}/graphql`, - fetch: async (uri, options) => { - const accessToken = await getValidAccessToken(); - - options.headers.Authorization = `Bearer ${accessToken}`; - return fetch(uri, options); - }, - }), - cache: new InMemoryCache(), -}); - -const processNotifications = async () => { - const PROCESS_NOTIFICATIONS = gql` - mutation ProcessNotifications { - processNotifications - } - `; - - const processResult = await client.mutate({ - mutation: PROCESS_NOTIFICATIONS, - variables: {}, - }); - - return processResult; -}; - -module.exports = processNotifications; diff --git a/site/gatsby-site/server/config.ts b/site/gatsby-site/server/config.ts index 885c793f8f..e5a6bb35de 100644 --- a/site/gatsby-site/server/config.ts +++ b/site/gatsby-site/server/config.ts @@ -10,7 +10,6 @@ export interface Config { MAILERSEND_API_KEY: string; NOTIFICATIONS_SENDER_NAME: string; NOTIFICATIONS_SENDER: string; - PROCESS_NOTIFICATIONS_SECRET: string; SITE_URL: string; }; @@ -26,12 +25,11 @@ const config: Config = { MAILERSEND_API_KEY: process.env.MAILERSEND_API_KEY!, NOTIFICATIONS_SENDER_NAME: process.env.NOTIFICATIONS_SENDER_NAME!, NOTIFICATIONS_SENDER: process.env.NOTIFICATIONS_SENDER!, - PROCESS_NOTIFICATIONS_SECRET: process.env.PROCESS_NOTIFICATIONS_SECRET!, SITE_URL: process.env.SITE_URL! || process.env.URL!, } Object.keys(config).forEach((key) => { - if (config[key as keyof Config] === undefined) { + if (config[key as keyof Config] === undefined || config[key as keyof Config] === '') { throw new Error(`Config property ${key} is undefined`); } }); diff --git a/site/gatsby-site/server/emails/index.ts b/site/gatsby-site/server/emails/index.ts new file mode 100644 index 0000000000..2da64d2381 --- /dev/null +++ b/site/gatsby-site/server/emails/index.ts @@ -0,0 +1,97 @@ +import { EmailParams, MailerSend, Recipient } from "mailersend"; +import config from "../config"; +import templates from "./templates"; +import * as reporter from '../reporter'; +import assert from "assert"; + +interface SendEmailParams { + recipients: { + email: string; + userId: string; + }[]; + subject: string; + dynamicData: { + incidentId?: string; + incidentTitle?: string; + incidentUrl?: string; + incidentDescription?: string; + incidentDate?: string; + developers?: string; + deployers?: string; + entitiesHarmed?: string; + implicatedSystems?: string; + reportUrl?: string; + reportTitle?: string; + reportAuthor?: string; + entityName?: string; + entityUrl?: string; + }; + templateId: string; +} + +export const replacePlaceholdersWithAllowedKeys = (template: string, data: { [key: string]: string }, allowedKeys: string[]): string => { + return template.replace(/\{\{\s*(\w+)\s*\}\}/g, (match, key) => { + return allowedKeys.includes(key) && key in data ? data[key] : match; + }); +} + +export const mailersendBulkSend = async (emails: EmailParams[]) => { + + const mailersend = new MailerSend({ + apiKey: config.MAILERSEND_API_KEY, + }); + + assert(emails.every(email => email.to.length == 1), 'Emails must have exactly one recipient'); + assert(emails.every(email => !email.cc), 'Should not use the "cc" field'); + + await mailersend.email.sendBulk(emails); +} + +export const sendEmail = async ({ recipients, subject, dynamicData, templateId }: SendEmailParams) => { + + const emailTemplateBody = templates[templateId]; + + if (!emailTemplateBody) { + throw new Error(`Template not found: ${templateId}`); + } + + const bulk: EmailParams[] = []; + + for (const recipient of recipients) { + + const personalizations = [{ + email: recipient.email, + data: { + ...dynamicData, + email: recipient.email, + userId: recipient.userId, + siteUrl: config.SITE_URL, + } + }] + + // We have to do this because MailerSend is escaping the placeholders containing html tags + const html = replacePlaceholdersWithAllowedKeys(emailTemplateBody, dynamicData, ['developers', 'deployers', 'entitiesHarmed', 'implicatedSystems']) + + const emailParams = new EmailParams() + .setFrom({ email: config.NOTIFICATIONS_SENDER, name: config.NOTIFICATIONS_SENDER_NAME }) + .setTo([new Recipient(recipient.email, '')]) + .setPersonalization(personalizations) + .setSubject(subject) + .setHtml(html); + //TODO: add a text version of the email + // .setText("Greetings from the team, you got this message through MailerSend."); + + bulk.push(emailParams); + } + + try { + + await mailersendBulkSend(bulk); + + } catch (error: any) { + error.message = `[Send Email]: ${error.message}`; + reporter.error(error); + + throw error; + } +} diff --git a/site/gatsby-site/server/emails/templates/NewEntityIncident.ts b/site/gatsby-site/server/emails/templates/NewEntityIncident.ts index 45ed998500..8067ea2056 100644 --- a/site/gatsby-site/server/emails/templates/NewEntityIncident.ts +++ b/site/gatsby-site/server/emails/templates/NewEntityIncident.ts @@ -82,5 +82,3 @@ const getEmailTemplate = () => { }; export default getEmailTemplate(); - - diff --git a/site/gatsby-site/server/fields/candidates.ts b/site/gatsby-site/server/fields/candidates.ts new file mode 100644 index 0000000000..11f0add44a --- /dev/null +++ b/site/gatsby-site/server/fields/candidates.ts @@ -0,0 +1,91 @@ +import { GraphQLBoolean, GraphQLFieldConfigMap, GraphQLFloat, GraphQLInt, GraphQLList, GraphQLNonNull, GraphQLObjectType, GraphQLString } from "graphql"; +import { allow } from "graphql-shield"; +import { isAdmin } from "../rules"; +import { ObjectIdScalar } from "../scalars"; +import { generateMutationFields, generateQueryFields } from "../utils"; +import { GraphQLDateTime } from "graphql-scalars"; + +const CandidateType = new GraphQLObjectType({ + name: 'Candidate', + fields: { + _id: { + type: ObjectIdScalar, + }, + title: { + type: new GraphQLNonNull(GraphQLString), + }, + date_published: { + type: new GraphQLNonNull(GraphQLString), + }, + matching_keywords: { + type: new GraphQLList(GraphQLString), + }, + matching_harm_keywords: { + type: new GraphQLList(GraphQLString), + }, + matching_entities: { + type: new GraphQLList(GraphQLString), + }, + + embedding: { + type: new GraphQLObjectType({ + name: 'CandidateEmbedding', + fields: { + vector: { + type: new GraphQLList(GraphQLInt) + }, + source: { + type: GraphQLString + } + } + }) + }, + similarity: { + type: GraphQLFloat, + }, + match: { + type: GraphQLBoolean, + }, + created_at: { + type: GraphQLDateTime + }, + url: { + type: new GraphQLNonNull(GraphQLString), + }, + dismissed: { + type: GraphQLBoolean, + }, + text: { + type: GraphQLString, + }, + plain_text: { + type: GraphQLString, + }, + }, +}); + +export const queryFields: GraphQLFieldConfigMap = { + + ...generateQueryFields({ collectionName: 'candidates', Type: CandidateType }) +} + +export const mutationFields: GraphQLFieldConfigMap = { + + ...generateMutationFields({ collectionName: 'candidates', Type: CandidateType }), +} + +export const permissions = { + Query: { + candidate: allow, + candidates: allow, + }, + Mutation: { + deleteOneCandidate: isAdmin, + deleteManyCandidates: isAdmin, + insertOneCandidate: allow, + insertManyCandidates: isAdmin, + updateOneCandidate: isAdmin, + updateManyCandidates: isAdmin, + upsertOneCandidate: isAdmin, + } +} \ No newline at end of file diff --git a/site/gatsby-site/server/fields/common.ts b/site/gatsby-site/server/fields/common.ts index fef3ac9668..ec5f70bfc5 100644 --- a/site/gatsby-site/server/fields/common.ts +++ b/site/gatsby-site/server/fields/common.ts @@ -1,10 +1,8 @@ import { MongoClient } from "mongodb"; -import templates from "../emails/templates"; import config from "../config"; -import * as reporter from '../reporter'; -import { Context, DBIncident, DBNotification, DBReport } from "../interfaces"; +import { Context, DBIncident, DBIncidentHistory, DBNotification, DBReport, DBReportHistory } from "../interfaces"; import _ from "lodash"; -import { Recipient, EmailParams, MailerSend } from "mailersend"; +import jwt from 'jsonwebtoken'; export const incidentEmbedding = (reports: Record[]) => { reports = reports.filter((report) => report.embedding); @@ -94,136 +92,76 @@ export const getUserAdminData = async (userId: string) => { const userApiResponse = await apiRequest({ path: `/users/${userId}` }); - const response: UserAdminData = {}; + let user: UserAdminData | null = null; if (userApiResponse.data) { - response.email = userApiResponse.data.email; - response.creationDate = new Date(userApiResponse.creation_date * 1000); - response.lastAuthenticationDate = new Date(userApiResponse.last_authentication_date * 1000); - response.disabled = userApiResponse.disabled; + user = { + email: userApiResponse.data.email, + creationDate: new Date(userApiResponse.creation_date * 1000), + lastAuthenticationDate: new Date(userApiResponse.last_authentication_date * 1000), + disabled: userApiResponse.disabled, + userId, + } } - return response; + return user; } -export const getUsersAdminData = async () => { - - const response = await apiRequest({ path: `/users` }); - const result: UserAdminData[] = []; +let cachedToken: string | null = null; +let tokenExpiration: number | null = null; - for (const userData of response) { +/** + * Fetches the access token for the MongoDB Atlas Admin API. + * + * Note: The token is cached to speed up subsequent requests. Tokens expire after 30 minutes. + * + * @returns {Promise} A promise that resolves to the access token. + */ +export const getAccessToken = async () => { - if (userData.data?.email) { + const refreshDate = tokenExpiration ? tokenExpiration * 1000 - 5 * 60 * 1000 : null; - const user: UserAdminData = {}; + // Refresh the authentication token well before expiration to avoid interruptions. - user.email = userData.data.email; - user.creationDate = new Date(userData.creation_date * 1000); - user.lastAuthenticationDate = new Date(userData.last_authentication_date * 1000); - user.disabled = userData.disabled; - user.userId = userData._id; + const now = Date.now(); - result.push(user); - } + if ((cachedToken && refreshDate && now < refreshDate)) { + return cachedToken; } - return result; -} - -interface SendEmailParams { - recipients: { - email: string; - userId: string; - }[]; - subject: string; - dynamicData: { - incidentId?: string; - incidentTitle?: string; - incidentUrl?: string; - incidentDescription?: string; - incidentDate?: string; - developers?: string; // HTML string of developers - deployers?: string; // HTML string of deployers - entitiesHarmed?: string; // HTML string of harmed entities - reportUrl?: string; // URL for a specific report (optional) - reportTitle?: string; // Title of the report (optional) - reportAuthor?: string; // Author of the report (optional) - entityName?: string; // Entity name (optional) - entityUrl?: string; // Entity URL (optional) - }; - templateId: string; // Email template ID -} - -export const replacePlaceholdersWithAllowedKeys = (template: string, data: { [key: string]: string }, allowedKeys: string[]): string => { - return template.replace(/\{\{\s*(\w+)\s*\}\}/g, (match, key) => { - return allowedKeys.includes(key) && key in data ? data[key] : match; - }); -} - -export const mailersendBulkSend = async (emails: EmailParams[]) => { - - const mailersend = new MailerSend({ - apiKey: config.MAILERSEND_API_KEY, + const loginResponse = await fetch('https://services.cloud.mongodb.com/api/admin/v3.0/auth/providers/mongodb-cloud/login', { + method: 'POST', + headers: { + 'Content-Type': 'application/json', + }, + body: JSON.stringify({ + username: config.REALM_API_PUBLIC_KEY, + apiKey: config.REALM_API_PRIVATE_KEY, + }), }); - await mailersend.email.sendBulk(emails); -} - -export const sendEmail = async ({ recipients, subject, dynamicData, templateId }: SendEmailParams) => { - - const emailTemplateBody = templates[templateId]; + const data = await loginResponse.json(); - if (!emailTemplateBody) { - throw new Error(`Template not found: ${templateId}`); + if (loginResponse.status != 200) { + throw new Error(`Login failed: ${data.error}`); } - try { + const decoded = jwt.decode(data.access_token) as { exp: number }; - const bulk: EmailParams[] = []; + cachedToken = data.access_token; + tokenExpiration = decoded.exp; - for (const recipient of recipients) { - - const personalizations = [{ - email: recipient.email, - data: { - ...dynamicData, - email: recipient.email, - userId: recipient.userId, - siteUrl: config.SITE_URL, - } - }] - - // We have to do this because MailerSend is escaping the placeholders containing html tags - const html = replacePlaceholdersWithAllowedKeys(emailTemplateBody, dynamicData, ['developers', 'deployers', 'entitiesHarmed']) - - const emailParams = new EmailParams() - .setFrom({ email: config.NOTIFICATIONS_SENDER, name: config.NOTIFICATIONS_SENDER_NAME }) - .setTo([new Recipient(recipient.email, '')]) - .setPersonalization(personalizations) - .setSubject(subject) - .setHtml(html); - //TODO: add a text version of the email - // .setText("Greetings from the team, you got this message through MailerSend."); - - bulk.push(emailParams); - } - - await mailersendBulkSend(bulk); - - } catch (error: any) { - error.message = `[Send Email]: ${error.message}`; - reporter.error(error); - - throw error; - } -} + return cachedToken; +}; /** * Makes an API request to the MongoDB Atlas Admin API, supporting only GET methods. * This function handles authentication using a public/private API key pair and returns the response from the API. * + * Rate limited to 100 calls / minute + * * **Note:** Use with caution as this function has admin privileges. * * @param {Object} params - The parameters for the API request. @@ -233,36 +171,24 @@ export const sendEmail = async ({ recipients, subject, dynamicData, templateId } * * @throws {Error} Throws an error if an unsupported HTTP method is provided. */ -export const apiRequest = async ({ path, method = "GET" }: { method?: string, path: string }) => { +export const apiRequest = async ({ path, params = {}, method = "GET" }: { method?: string, params?: Record, path: string }) => { - const loginResponse = await fetch('https://services.cloud.mongodb.com/api/admin/v3.0/auth/providers/mongodb-cloud/login', { - method: 'POST', - headers: { - 'Content-Type': 'application/json', - }, - body: JSON.stringify({ - username: config.REALM_API_PUBLIC_KEY, - apiKey: config.REALM_API_PRIVATE_KEY, - }), - }); + const accessToken = await getAccessToken(); - const data = await loginResponse.json(); + const url = new URL(`https://services.cloud.mongodb.com/api/admin/v3.0/groups/${config.REALM_API_GROUP_ID}/apps/${config.REALM_API_APP_ID}${path}`); - if (loginResponse.status != 200) { - return { - status: loginResponse.status, - error: data.error - } + if (Object.keys(params).length > 0) { + const searchParams = new URLSearchParams(params); + url.search = searchParams.toString(); } - let response = null; + const headers = { "Authorization": `Bearer ${accessToken}` }; - const url = `https://services.cloud.mongodb.com/api/admin/v3.0/groups/${config.REALM_API_GROUP_ID}/apps/${config.REALM_API_APP_ID}${path}`; - const headers = { "Authorization": `Bearer ${data.access_token}` }; + let response = null; if (method == 'GET') { - const result = await fetch(url, { headers }); + const result = await fetch(url.toString(), { headers }); response = await result.json(); } @@ -292,6 +218,7 @@ export const createNotificationsOnNewIncident = async (fullDocument: DBIncident, 'Alleged deployer of AI system', 'Alleged developer of AI system', 'Alleged harmed or nearly harmed parties', + 'implicated_systems' ]; const entities: string[] = []; @@ -347,6 +274,7 @@ export function hasRelevantUpdates(before: DBIncident, after: DBIncident): boole "editor_similar_incidents", "flagged_dissimilar_incidents", "nlp_similar_incidents", + "implicated_systems" ]; const hasMonitoredUpdates = monitoredFields.some((field) => { @@ -402,6 +330,7 @@ export const createNotificationsOnUpdatedIncident = async (fullDocument: DBIncid 'Alleged deployer of AI system', 'Alleged developer of AI system', 'Alleged harmed or nearly harmed parties', + 'implicated_systems' ]; const entities: string[] = []; @@ -427,4 +356,30 @@ export const createNotificationsOnUpdatedIncident = async (fullDocument: DBIncid }; await notificationsCollection.updateOne(notification, { $set: notification }, { upsert: true }); } -} \ No newline at end of file +} + +export const logReportHistory = async (updated: DBReport, context: Context) => { + + const reportHistory: DBReportHistory = { + ...updated, + modifiedBy: context.user?.id ?? '', + _id: undefined, + } + + const reportHistoryCollection = context.client.db('history').collection("reports"); + + await reportHistoryCollection.insertOne(reportHistory); +}; + +export const logIncidentHistory = async (updated: DBIncident, context: Context) => { + + const incidentHistory: DBIncidentHistory = { + ...updated, + modifiedBy: context.user?.id ?? '', + _id: undefined, + } + + const incidentHistoryCollection = context.client.db('history').collection("incidents"); + + await incidentHistoryCollection.insertOne(incidentHistory); +} diff --git a/site/gatsby-site/server/fields/incidents.ts b/site/gatsby-site/server/fields/incidents.ts index a6148defb1..624507073d 100644 --- a/site/gatsby-site/server/fields/incidents.ts +++ b/site/gatsby-site/server/fields/incidents.ts @@ -1,9 +1,9 @@ import { GraphQLFieldConfigMap, GraphQLInputObjectType, GraphQLInt, GraphQLList, GraphQLNonNull } from "graphql"; import { allow } from "graphql-shield"; import { generateMutationFields, generateQueryFields, getQueryResolver } from "../utils"; -import { Context } from "../interfaces"; +import { Context, DBIncident } from "../interfaces"; import { isRole } from "../rules"; -import { createNotificationsOnNewIncident, createNotificationsOnUpdatedIncident, hasRelevantUpdates, linkReportsToIncidents } from "./common"; +import { createNotificationsOnNewIncident, createNotificationsOnUpdatedIncident, hasRelevantUpdates, linkReportsToIncidents, logIncidentHistory } from "./common"; import { IncidentType } from "../types/incidents"; export const queryFields: GraphQLFieldConfigMap = { @@ -32,11 +32,15 @@ export const mutationFields: GraphQLFieldConfigMap = { if (operation === 'insertOne') { + await logIncidentHistory(result, context); + await createNotificationsOnNewIncident(result, context); } if (operation === 'updateOne') { + await logIncidentHistory(result, context); + if (hasRelevantUpdates(initial, result)) { await createNotificationsOnUpdatedIncident(result, initial, context); @@ -58,7 +62,15 @@ export const mutationFields: GraphQLFieldConfigMap = { await linkReportsToIncidents(context.client, args.input.report_numbers, args.input.incident_ids); - const incidentsCollection = context.client.db('aiidprod').collection("incidents"); + const incidentsCollection = context.client.db('aiidprod').collection("incidents"); + + + const updates = await incidentsCollection.find({ incident_id: { $in: args.input.incident_ids } }).toArray(); + + for (const updated of updates) { + + await logIncidentHistory(updated, context); + } return incidentsCollection.find({ reports: { $in: args.input.report_numbers } }, options).toArray(); }) @@ -72,7 +84,7 @@ export const mutationFields: GraphQLFieldConfigMap = { }, resolve: getQueryResolver(IncidentType, async (filter, projection, options, obj, args, context) => { - const incidentsCollection = context.client.db('aiidprod').collection<{ editors: string[] }>("incidents"); + const incidentsCollection = context.client.db('aiidprod').collection("incidents"); await incidentsCollection.updateOne({ incident_id: args.incidentId }, { $set: { flagged_dissimilar_incidents: args.dissimilarIds } }); @@ -81,7 +93,14 @@ export const mutationFields: GraphQLFieldConfigMap = { await incidentsCollection.updateOne({ incident_id: args.incidentId }, { $addToSet: { editors: context.user.id } }); } - return incidentsCollection.findOne({ incident_id: args.incidentId }, options); + const updated = await incidentsCollection.findOne({ incident_id: args.incidentId }, options); + + if (updated) { + + await logIncidentHistory(updated, context); + } + + return updated; }) }, } diff --git a/site/gatsby-site/server/fields/incidentsHistory.ts b/site/gatsby-site/server/fields/incidentsHistory.ts new file mode 100644 index 0000000000..1e8c60a17b --- /dev/null +++ b/site/gatsby-site/server/fields/incidentsHistory.ts @@ -0,0 +1,17 @@ +import { GraphQLFieldConfigMap } from "graphql"; +import { allow } from "graphql-shield"; +import { generateQueryFields } from "../utils"; +import { IncidentHistoryType } from "../types/incidentHistory"; + + +export const queryFields: GraphQLFieldConfigMap = { + + ...generateQueryFields({ databaseName: 'history', collectionName: 'incidents', fieldName: 'history_incident', Type: IncidentHistoryType }) +} + +export const permissions = { + Query: { + history_incident: allow, + history_incidents: allow, + } +} \ No newline at end of file diff --git a/site/gatsby-site/server/fields/notifications.ts b/site/gatsby-site/server/fields/notifications.ts index b271acdc6f..c09143d0ed 100644 --- a/site/gatsby-site/server/fields/notifications.ts +++ b/site/gatsby-site/server/fields/notifications.ts @@ -1,457 +1,16 @@ -import { GraphQLFieldConfigMap, GraphQLInt } from 'graphql'; +import { GraphQLFieldConfigMap } from 'graphql'; import { generateQueryFields } from '../utils'; -import { Context, DBEntity, DBIncident, DBReport, DBSubscription, DBNotification } from '../interfaces'; +import { Context } from '../interfaces'; import { NotificationType } from '../types/notification'; -import { getUserAdminData, getUsersAdminData, sendEmail } from './common'; -import * as reporter from '../reporter'; -import { hasHeaderSecret, isRole } from '../rules'; -import config from '../config'; +import { isRole } from '../rules'; export const queryFields: GraphQLFieldConfigMap = { ...generateQueryFields({ databaseName: 'customData', collectionName: 'notifications', Type: NotificationType }) } - - -const getRecipients = async (userIds: string[]) => { - const users = await getUsersAdminData(); - - const recipients = users - .filter((user) => userIds.includes(user.userId!)) - .map((user) => ({ email: user.email!, userId: user.userId! })); - - return recipients; -} - -const markNotifications = async (notificationsCollection: any, notifications: any, isProcessed: any) => { - for (const pendingNotification of notifications) { - await notificationsCollection.updateOne( - { _id: pendingNotification._id }, - { $set: { processed: isProcessed, sentDate: new Date() } } - ); - } -} - -const markNotificationsAsProcessed = async (notificationsCollection: any, notifications: any) => { - await markNotifications(notificationsCollection, notifications, true); -} - -const markNotificationsAsNotProcessed = async (notificationsCollection: any, notifications: any) => { - await markNotifications(notificationsCollection, notifications, false); -} - -const buildEntityList = (allEntities: any, entityIds: any) => { - const entityNames = entityIds.map((entityId: string) => { - const entity = allEntities.find((entity: any) => entity.entity_id === entityId); - return entity ? `${entity.name}` : ''; - }); - - if (entityNames.length < 3) { return entityNames.join(' and '); } - - return `${entityNames.slice(0, - 1).join(', ')}, and ${entityNames[entityNames.length - 1]}`; -} - -async function notificationsToNewIncidents(context: Context) { - - const notificationsCollection = context.client.db('customData').collection("notifications"); - const subscriptionsCollection = context.client.db('customData').collection("subscriptions"); - const incidentsCollection = context.client.db('aiidprod').collection("incidents"); - const entitiesCollection = context.client.db('aiidprod').collection("entities"); - - const pendingNotificationsToNewIncidents: any[] = await notificationsCollection.find({ processed: false, type: 'new-incidents' }).toArray(); - - let result = 0; - - if (pendingNotificationsToNewIncidents.length > 0) { - - result += pendingNotificationsToNewIncidents.length; - - const subscriptionsToNewIncidents = await subscriptionsCollection.find({ type: 'new-incidents' }).toArray(); - - // Process subscriptions to New Incidents - if (subscriptionsToNewIncidents.length > 0) { - - const allEntities = await entitiesCollection.find({}).toArray(); - - const userIds = subscriptionsToNewIncidents.map((subscription) => subscription.userId); - - const uniqueUserIds: string[] = [...new Set(userIds)]!; - - const recipients = await getRecipients(uniqueUserIds); - - const uniqueNotifications: number[] = []; - - for (const pendingNotification of pendingNotificationsToNewIncidents) { - - // Mark the notification as processed before sending the email - await markNotificationsAsProcessed(notificationsCollection, [pendingNotification]); - - // Send only one email per Incident - if (!uniqueNotifications.includes(pendingNotification.incident_id)) { - uniqueNotifications.push(pendingNotification.incident_id); - - try { - const incident = await incidentsCollection.findOne({ incident_id: pendingNotification.incident_id }); - - if (incident) { - //Send email notification - const sendEmailParams = { - recipients, - subject: 'New Incident {{incidentId}} was created', - dynamicData: { - incidentId: `${incident.incident_id}`, - incidentTitle: incident.title, - incidentUrl: `${config.SITE_URL}/cite/${incident.incident_id}`, - incidentDescription: incident.description ?? undefined, - incidentDate: incident.date, - developers: buildEntityList(allEntities, incident['Alleged developer of AI system']), - deployers: buildEntityList(allEntities, incident['Alleged deployer of AI system']), - entitiesHarmed: buildEntityList(allEntities, incident['Alleged harmed or nearly harmed parties']), - }, - templateId: 'NewIncident' // Template value from function name sufix from "site/realm/functions/config.json" - }; - - await sendEmail(sendEmailParams); - } - - - } catch (error: any) { - // If there is an error sending the email > Mark the notification as not processed - await markNotificationsAsNotProcessed(notificationsCollection, [pendingNotification]); - - error.message = `[Process Pending Notifications: New Incidents]: ${error.message}`; - - reporter.error(error); - } - } - } - - console.log(`New Incidents: ${pendingNotificationsToNewIncidents.length} pending notifications were processed.`); - } - else { - // If there are no subscribers to New Incidents (edge case) > Mark all pending notifications as processed - await markNotificationsAsProcessed(notificationsCollection, pendingNotificationsToNewIncidents); - } - } - else { - console.log('New Incidents: No pending notifications to process.'); - } - - return result; -} - -async function notificationsToIncidentUpdates(context: Context) { - - const notificationsCollection = context.client.db('customData').collection("notifications"); - const subscriptionsCollection = context.client.db('customData').collection("subscriptions"); - const incidentsCollection = context.client.db('aiidprod').collection("incidents"); - const reportsCollection = context.client.db('aiidprod').collection("reports"); - - let result = 0; - - const pendingNotificationsToIncidentUpdates = await notificationsCollection.find({ - processed: false, - type: { $in: ['new-report-incident', 'incident-updated'] } - }).toArray(); - - if (pendingNotificationsToIncidentUpdates.length > 0) { - - result += pendingNotificationsToIncidentUpdates.length; - - let uniqueNotifications: DBNotification[] = []; - - for (const pendingNotification of pendingNotificationsToIncidentUpdates) { - - // Mark the notification as processed before sending the email - await markNotificationsAsProcessed(notificationsCollection, [pendingNotification]); - - // Process each Incident only once - if (!uniqueNotifications.some(n => n.incident_id === pendingNotification.incident_id && n.type === pendingNotification.type)) { - uniqueNotifications.push(pendingNotification); - - try { - const subscriptionsToIncidentUpdates = await subscriptionsCollection.find({ - type: 'incident', - incident_id: pendingNotification.incident_id! - }).toArray(); - - // Process subscriptions to Incident updates - if (subscriptionsToIncidentUpdates.length > 0) { - - const userIds = subscriptionsToIncidentUpdates.map((subscription) => subscription.userId); - - const uniqueUserIds = [...new Set(userIds)]; - - const recipients = await getRecipients(uniqueUserIds); - - const incident = await incidentsCollection.findOne({ incident_id: pendingNotification.incident_id! }); - - const newReportNumber = pendingNotification.report_number; - - const newReport = newReportNumber ? await reportsCollection.findOne({ report_number: newReportNumber }) : null; - - if (incident) { - - const sendEmailParams = { - recipients, - subject: 'Incident {{incidentId}} was updated', - dynamicData: { - incidentId: `${incident.incident_id}`, - incidentTitle: incident.title, - incidentUrl: `${config.SITE_URL}/cite/${incident.incident_id}`, - reportUrl: `${config.SITE_URL}/cite/${incident.incident_id}#r${newReportNumber}`, - reportTitle: newReport ? newReport.title : '', - reportAuthor: (newReport && newReport.authors[0]) ? newReport.authors[0] : '', - }, - // Template value from function name sufix from "site/realm/functions/config.json" - templateId: newReportNumber ? 'NewReportAddedToAnIncident' : 'IncidentUpdate', - }; - - await sendEmail(sendEmailParams); - - console.log(`Incident ${incident.incident_id} updates: Pending notification was processed.`); - } - - } - } catch (error: any) { - // If there is an error sending the email > Mark the notification as not processed - await markNotificationsAsNotProcessed(notificationsCollection, [pendingNotification]); - - error.message = `[Process Pending Notifications: Incidents Updates]: ${error.message}`; - - reporter.error(error); - } - } - } - } - else { - console.log('New Updated Incidents: No pending notifications to process.'); - } - - return result; -} - -async function notificationsToNewEntityIncidents(context: Context) { - - const notificationsCollection = context.client.db('customData').collection("notifications"); - const subscriptionsCollection = context.client.db('customData').collection("subscriptions"); - const incidentsCollection = context.client.db('aiidprod').collection("incidents"); - const entitiesCollection = context.client.db('aiidprod').collection("entities"); - - let result = 0; - - const pendingNotificationsToNewEntityIncidents = await notificationsCollection.find({ processed: false, type: 'entity' }).toArray(); - - if (pendingNotificationsToNewEntityIncidents.length > 0) { - - result += pendingNotificationsToNewEntityIncidents.length; - - const allEntities = await entitiesCollection.find({}).toArray(); - - let uniqueNotifications: string[] = []; - - for (const pendingNotification of pendingNotificationsToNewEntityIncidents) { - - // Mark the notification as processed before sending the email - await markNotificationsAsProcessed(notificationsCollection, [pendingNotification]); - - // Process each entity only once - if (pendingNotification.entity_id && !uniqueNotifications.includes(pendingNotification.entity_id)) { - uniqueNotifications.push(pendingNotification.entity_id); - - try { - - const subscriptionsToNewEntityIncidents = await subscriptionsCollection.find({ - type: 'entity', - entityId: pendingNotification.entity_id - }).toArray(); - - // Process subscriptions to New Entity Incidents - if (subscriptionsToNewEntityIncidents.length > 0) { - - const userIds = subscriptionsToNewEntityIncidents.map((subscription) => subscription.userId); - - const uniqueUserIds = [...new Set(userIds)]; - - const recipients = await getRecipients(uniqueUserIds); - - const incident = await incidentsCollection.findOne({ incident_id: pendingNotification.incident_id! }); - - const entity = allEntities.find(entity => entity.entity_id === pendingNotification.entity_id); - - const isIncidentUpdate = pendingNotification.isUpdate; - - if (incident && entity) { - - const sendEmailParams = { - recipients, - subject: isIncidentUpdate ? 'Update Incident for {{entityName}}' : 'New Incident for {{entityName}}', - dynamicData: { - incidentId: `${incident.incident_id}`, - incidentTitle: incident.title, - incidentUrl: `${config.SITE_URL}/cite/${incident.incident_id}`, - incidentDescription: incident.description ?? undefined, - incidentDate: incident.date, - entityName: entity.name, - entityUrl: `${config.SITE_URL}/entities/${entity.entity_id}`, - developers: buildEntityList(allEntities, incident['Alleged developer of AI system']), - deployers: buildEntityList(allEntities, incident['Alleged deployer of AI system']), - entitiesHarmed: buildEntityList(allEntities, incident['Alleged harmed or nearly harmed parties']), - }, - // Template value from function name sufix from "site/realm/functions/config.json" - templateId: isIncidentUpdate ? 'EntityIncidentUpdated' : 'NewEntityIncident' - }; - - await sendEmail(sendEmailParams); - - console.log(`New "${entity.name}" Entity Incidents: pending notification was processed.`); - } - } - } catch (error: any) { - // If there is an error sending the email > Mark the notification as not processed - await markNotificationsAsNotProcessed(notificationsCollection, [pendingNotification]); - - error.message = `[Process Pending Notifications: New Entity Incidents]: ${error.message}`; - - reporter.error(error); - } - } - } - } - else { - console.log('New Entity Incidents: No pending notifications to process.'); - } - - return result; -} - -async function notificationsToNewPromotions(context: Context) { - - const notificationsCollection = context.client.db('customData').collection("notifications"); - const incidentsCollection = context.client.db('aiidprod').collection("incidents"); - - let result = 0; - - const pendingNotificationsToNewPromotions = await notificationsCollection.find({ processed: false, type: 'submission-promoted' }).toArray(); - - if (pendingNotificationsToNewPromotions.length > 0) { - - result += pendingNotificationsToNewPromotions.length; - - const userIds = pendingNotificationsToNewPromotions - .filter(s => s.userId) - .map(s => s.userId) as string[]; - - const uniqueUserIds = [...new Set(userIds)]; - - const recipients = await getRecipients(uniqueUserIds); - - let uniqueNotifications: number[] = []; - - for (const pendingNotification of pendingNotificationsToNewPromotions) { - - // Mark the notification as processed before sending the email - await markNotificationsAsProcessed(notificationsCollection, [pendingNotification]); - - if (pendingNotification.incident_id && !uniqueNotifications.includes(pendingNotification.incident_id)) { - uniqueNotifications.push(pendingNotification.incident_id); - - try { - const incident = await incidentsCollection.findOne({ incident_id: pendingNotification.incident_id }); - - if (incident) { - - //Send email notification - const sendEmailParams = { - recipients, - subject: 'Your submission has been approved!', - dynamicData: { - incidentId: `${incident.incident_id}`, - incidentTitle: incident.title, - incidentUrl: `${config.SITE_URL}/cite/${incident.incident_id}`, - incidentDescription: incident.description ?? undefined, - incidentDate: incident.date, - }, - templateId: 'SubmissionApproved' // Template value from function name sufix from "site/realm/functions/config.json" - }; - - await sendEmail(sendEmailParams); - } - - } catch (error: any) { - // If there is an error sending the email > Mark the notification as not processed - await markNotificationsAsNotProcessed(notificationsCollection, [pendingNotification]); - - error.message = `[Process Pending Notifications: Submission Promoted]: ${error.message}`; - - reporter.error(error); - } - } - } - - console.log(`New Promotions: ${pendingNotificationsToNewPromotions.length} pending notifications were processed.`); - } - else { - console.log('Submission Promoted: No pending notifications to process.'); - } - - return result; -} - -export const mutationFields: GraphQLFieldConfigMap = { - - processNotifications: { - type: GraphQLInt, - resolve: async (source, args, context, info) => { - let result = 0; // Pending notifications processed count - - try { - result += await notificationsToNewIncidents(context); - } - catch (error: any) { - - error.message = `[Process Pending Notifications: New Incidents]: ${error.message}`; - reporter.error(error); - } - - try { - result += await notificationsToNewEntityIncidents(context); - } - catch (error: any) { - - error.message = `[Process Pending Notifications: New Entity Incidents]: ${error.message}`; - reporter.error(error); - } - - try { - result += await notificationsToIncidentUpdates(context); - } - catch (error: any) { - - error.message = `[Process Pending Notifications: Incidents Updates]: ${error.message}`; - reporter.error(error); - } - - try { - result += await notificationsToNewPromotions(context); - } - catch (error: any) { - - error.message = `[Process Pending Notifications: Submission Promoted]: ${error.message}`; - reporter.error(error); - } - - return result; - } - } -} - export const permissions = { Query: { notification: isRole('admin'), notifications: isRole('admin'), }, - Mutation: { - processNotifications: hasHeaderSecret("PROCESS_NOTIFICATIONS_SECRET"), - } } \ No newline at end of file diff --git a/site/gatsby-site/server/fields/reports.ts b/site/gatsby-site/server/fields/reports.ts index f34b5cb287..1d97b4376c 100644 --- a/site/gatsby-site/server/fields/reports.ts +++ b/site/gatsby-site/server/fields/reports.ts @@ -2,7 +2,7 @@ import { GraphQLBoolean, GraphQLFieldConfigMap, GraphQLInputObjectType, GraphQLI import { allow } from "graphql-shield"; import { generateMutationFields, generateQueryFields, getQueryResolver } from "../utils"; import { isRole } from "../rules"; -import { linkReportsToIncidents } from "./common"; +import { linkReportsToIncidents, logReportHistory } from "./common"; import { ReportType } from "../types/report"; @@ -84,7 +84,22 @@ const CreateVariantInput = new GraphQLInputObjectType({ export const mutationFields: GraphQLFieldConfigMap = { - ...generateMutationFields({ collectionName: 'reports', Type: ReportType, generateFields: ['updateOne', 'deleteOne', 'insertOne'] }), + ...generateMutationFields({ + collectionName: 'reports', + Type: ReportType, + generateFields: ['updateOne', 'deleteOne', 'insertOne'], + onResolve: async (operation, context, params) => { + + const { result } = params!; + + if (operation === 'updateOne' || operation === 'insertOne') { + + await logReportHistory(result, context) + } + + return result; + }, + }), flagReport: { type: ReportType, diff --git a/site/gatsby-site/server/fields/reportsHistory.ts b/site/gatsby-site/server/fields/reportsHistory.ts new file mode 100644 index 0000000000..864efee916 --- /dev/null +++ b/site/gatsby-site/server/fields/reportsHistory.ts @@ -0,0 +1,17 @@ +import { GraphQLFieldConfigMap } from "graphql"; +import { allow } from "graphql-shield"; +import { generateQueryFields } from "../utils"; +import { ReportHistoryType } from "../types/reportHistory"; + + +export const queryFields: GraphQLFieldConfigMap = { + + ...generateQueryFields({ databaseName: 'history', collectionName: 'reports', fieldName: 'history_report', Type: ReportHistoryType }) +} + +export const permissions = { + Query: { + history_report: allow, + history_reports: allow, + } +} \ No newline at end of file diff --git a/site/gatsby-site/server/fields/submissions.ts b/site/gatsby-site/server/fields/submissions.ts index e81cde431f..d9a2f253ce 100644 --- a/site/gatsby-site/server/fields/submissions.ts +++ b/site/gatsby-site/server/fields/submissions.ts @@ -105,6 +105,7 @@ export const mutationFields: GraphQLFieldConfigMap = { nlp_similar_incidents: submission.nlp_similar_incidents || [], editor_similar_incidents: submission.editor_similar_incidents || [], editor_dissimilar_incidents: submission.editor_dissimilar_incidents || [], + implicated_systems: submission.implicated_systems || [], editor_notes: submission.editor_notes ?? '', flagged_dissimilar_incidents: [], } diff --git a/site/gatsby-site/server/fields/users.ts b/site/gatsby-site/server/fields/users.ts index b9a3178dd6..1f8330934c 100644 --- a/site/gatsby-site/server/fields/users.ts +++ b/site/gatsby-site/server/fields/users.ts @@ -1,8 +1,9 @@ import { GraphQLFieldConfigMap } from "graphql"; import { generateMutationFields, generateQueryFields } from "../utils"; import { Context } from "../interfaces"; -import { isRole, isSelf } from "../rules"; +import { notQueriesAdminData, isRole, isSelf } from "../rules"; import { UserType } from "../types/user"; +import { or } from "graphql-shield"; export const queryFields: GraphQLFieldConfigMap = { @@ -17,8 +18,8 @@ export const mutationFields: GraphQLFieldConfigMap = { export const permissions = { Query: { - user: isSelf(), - users: isRole('incident_editor'), //TODO: this needs more work + user: or(isSelf(), notQueriesAdminData()), + users: or(isRole('admin'), notQueriesAdminData()), }, Mutation: { updateOneUser: isSelf(), diff --git a/site/gatsby-site/server/generated/gql.ts b/site/gatsby-site/server/generated/gql.ts index 2fbd7d25f3..c0028f6cad 100644 --- a/site/gatsby-site/server/generated/gql.ts +++ b/site/gatsby-site/server/generated/gql.ts @@ -20,18 +20,17 @@ const documents = { "\n query FindEntities {\n entities {\n entity_id\n name\n }\n }\n": types.FindEntitiesDocument, "\n query FindEntity($filter: EntityFilterType) {\n entity(filter: $filter) {\n entity_id\n name\n created_at\n date_modified\n }\n }\n": types.FindEntityDocument, "\n mutation UpdateEntity($filter: EntityFilterType!, $update: EntityUpdateType!) {\n updateOneEntity(filter: $filter, update: $update) {\n entity_id\n }\n }\n": types.UpdateEntityDocument, - "\n query FindIncident($filter: IncidentFilterType) {\n incident(filter: $filter) {\n incident_id\n title\n description\n editors {\n userId\n first_name\n last_name\n }\n date\n AllegedDeployerOfAISystem {\n entity_id\n name\n }\n AllegedDeveloperOfAISystem {\n entity_id\n name\n }\n AllegedHarmedOrNearlyHarmedParties {\n entity_id\n name\n }\n nlp_similar_incidents {\n incident_id\n similarity\n }\n editor_similar_incidents\n editor_dissimilar_incidents\n flagged_dissimilar_incidents\n reports {\n report_number\n }\n embedding {\n from_reports\n vector\n }\n editor_notes\n }\n }\n": types.FindIncidentDocument, - "\n query FindIncidentsTable($filter: IncidentFilterType) {\n incidents(filter: $filter) {\n incident_id\n title\n description\n editors {\n userId\n first_name\n last_name\n }\n date\n AllegedDeployerOfAISystem {\n entity_id\n name\n }\n AllegedDeveloperOfAISystem {\n entity_id\n name\n }\n AllegedHarmedOrNearlyHarmedParties {\n entity_id\n name\n }\n reports {\n report_number\n }\n }\n }\n": types.FindIncidentsTableDocument, - "\n query FindIncidentEntities($filter: IncidentFilterType) {\n incident(filter: $filter) {\n incident_id\n AllegedDeployerOfAISystem {\n entity_id\n name\n }\n AllegedDeveloperOfAISystem {\n entity_id\n name\n }\n AllegedHarmedOrNearlyHarmedParties {\n entity_id\n name\n }\n }\n }\n": types.FindIncidentEntitiesDocument, - "\n query FindIncidents($filter: IncidentFilterType) {\n incidents(filter: $filter) {\n incident_id\n title\n description\n editors {\n userId\n first_name\n last_name\n }\n date\n AllegedDeployerOfAISystem {\n entity_id\n name\n }\n AllegedDeveloperOfAISystem {\n entity_id\n name\n }\n AllegedHarmedOrNearlyHarmedParties {\n entity_id\n name\n }\n nlp_similar_incidents {\n incident_id\n similarity\n }\n editor_similar_incidents\n editor_dissimilar_incidents\n flagged_dissimilar_incidents\n reports {\n report_number\n }\n embedding {\n from_reports\n vector\n }\n }\n }\n": types.FindIncidentsDocument, + "\n query FindIncident($filter: IncidentFilterType) {\n incident(filter: $filter) {\n incident_id\n title\n description\n editors {\n userId\n first_name\n last_name\n }\n date\n AllegedDeployerOfAISystem {\n entity_id\n name\n }\n AllegedDeveloperOfAISystem {\n entity_id\n name\n }\n AllegedHarmedOrNearlyHarmedParties {\n entity_id\n name\n }\n implicated_systems {\n entity_id\n name\n }\n nlp_similar_incidents {\n incident_id\n similarity\n }\n editor_similar_incidents\n editor_dissimilar_incidents\n flagged_dissimilar_incidents\n reports {\n report_number\n }\n embedding {\n from_reports\n vector\n }\n editor_notes\n }\n }\n": types.FindIncidentDocument, + "\n query FindIncidentsTable($filter: IncidentFilterType) {\n incidents(filter: $filter) {\n incident_id\n title\n description\n editors {\n userId\n first_name\n last_name\n }\n date\n AllegedDeployerOfAISystem {\n entity_id\n name\n }\n AllegedDeveloperOfAISystem {\n entity_id\n name\n }\n AllegedHarmedOrNearlyHarmedParties {\n entity_id\n name\n }\n implicated_systems {\n entity_id\n name\n }\n reports {\n report_number\n }\n }\n }\n": types.FindIncidentsTableDocument, + "\n query FindIncidentEntities($filter: IncidentFilterType) {\n incident(filter: $filter) {\n incident_id\n AllegedDeployerOfAISystem {\n entity_id\n name\n }\n AllegedDeveloperOfAISystem {\n entity_id\n name\n }\n AllegedHarmedOrNearlyHarmedParties {\n entity_id\n name\n }\n implicated_systems {\n entity_id\n name\n }\n }\n }\n": types.FindIncidentEntitiesDocument, + "\n query FindIncidents($filter: IncidentFilterType) {\n incidents(filter: $filter) {\n incident_id\n title\n description\n editors {\n userId\n first_name\n last_name\n }\n date\n AllegedDeployerOfAISystem {\n entity_id\n name\n }\n AllegedDeveloperOfAISystem {\n entity_id\n name\n }\n AllegedHarmedOrNearlyHarmedParties {\n entity_id\n name\n }\n implicated_systems {\n entity_id\n name\n }\n nlp_similar_incidents {\n incident_id\n similarity\n }\n editor_similar_incidents\n editor_dissimilar_incidents\n flagged_dissimilar_incidents\n reports {\n report_number\n }\n embedding {\n from_reports\n vector\n }\n }\n }\n": types.FindIncidentsDocument, "\n query FindIncidentsTitles($filter: IncidentFilterType) {\n incidents(filter: $filter) {\n incident_id\n title\n }\n }\n": types.FindIncidentsTitlesDocument, - "\n mutation UpdateIncident($filter: IncidentFilterType!, $update: IncidentUpdateType!) {\n updateOneIncident(filter: $filter, update: $update) {\n incident_id\n title\n description\n editors {\n userId\n first_name\n last_name\n }\n date\n AllegedDeployerOfAISystem {\n entity_id\n name\n }\n AllegedDeveloperOfAISystem {\n entity_id\n name\n }\n AllegedHarmedOrNearlyHarmedParties {\n entity_id\n name\n }\n nlp_similar_incidents {\n incident_id\n similarity\n }\n editor_similar_incidents\n editor_dissimilar_incidents\n flagged_dissimilar_incidents\n reports {\n report_number\n }\n embedding {\n from_reports\n vector\n }\n editor_notes\n }\n }\n": types.UpdateIncidentDocument, + "\n mutation UpdateIncident($filter: IncidentFilterType!, $update: IncidentUpdateType!) {\n updateOneIncident(filter: $filter, update: $update) {\n incident_id\n title\n description\n editors {\n userId\n first_name\n last_name\n }\n date\n AllegedDeployerOfAISystem {\n entity_id\n name\n }\n AllegedDeveloperOfAISystem {\n entity_id\n name\n }\n AllegedHarmedOrNearlyHarmedParties {\n entity_id\n name\n }\n implicated_systems {\n entity_id\n name\n }\n nlp_similar_incidents {\n incident_id\n similarity\n }\n editor_similar_incidents\n editor_dissimilar_incidents\n flagged_dissimilar_incidents\n reports {\n report_number\n }\n embedding {\n from_reports\n vector\n }\n editor_notes\n }\n }\n": types.UpdateIncidentDocument, "\n mutation UpdateIncidents($filter: IncidentFilterType!, $update: IncidentUpdateType!) {\n updateManyIncidents(filter: $filter, update: $update) {\n matchedCount\n modifiedCount\n }\n }\n": types.UpdateIncidentsDocument, "\n mutation InsertIncident($data: IncidentInsertType!) {\n insertOneIncident(data: $data) {\n incident_id\n }\n }\n": types.InsertIncidentDocument, "\n query FindLastIncident {\n incidents(sort: { incident_id: DESC }, pagination: { limit: 1, skip: 0 }) {\n incident_id\n }\n }\n": types.FindLastIncidentDocument, - "\n query FindIncidentFull($filter: IncidentFilterType) {\n incident(filter: $filter) {\n incident_id\n title\n description\n editors {\n userId\n first_name\n last_name\n }\n date\n AllegedDeployerOfAISystem {\n entity_id\n name\n }\n AllegedDeveloperOfAISystem {\n entity_id\n name\n }\n AllegedHarmedOrNearlyHarmedParties {\n entity_id\n name\n }\n nlp_similar_incidents {\n incident_id\n similarity\n }\n editor_similar_incidents\n editor_dissimilar_incidents\n flagged_dissimilar_incidents\n reports {\n submitters\n date_published\n report_number\n title\n description\n url\n image_url\n cloudinary_id\n source_domain\n text\n authors\n epoch_date_submitted\n language\n tags\n inputs_outputs\n }\n embedding {\n from_reports\n vector\n }\n editor_notes\n epoch_date_modified\n tsne {\n x\n y\n }\n }\n }\n": types.FindIncidentFullDocument, - "\n mutation logIncidentHistory($input: History_incidentInsertInput!) {\n logIncidentHistory(input: $input) {\n incident_id\n }\n }\n": types.LogIncidentHistoryDocument, - "\n query FindIncidentHistory($query: History_incidentQueryInput) {\n history_incidents(query: $query, sortBy: EPOCH_DATE_MODIFIED_DESC) {\n incident_id\n AllegedDeployerOfAISystem\n AllegedDeveloperOfAISystem\n AllegedHarmedOrNearlyHarmedParties\n _id\n date\n description\n modifiedBy\n editor_dissimilar_incidents\n editor_notes\n editor_similar_incidents\n editors\n embedding {\n from_reports\n vector\n }\n epoch_date_modified\n flagged_dissimilar_incidents\n nlp_similar_incidents {\n incident_id\n similarity\n }\n reports\n title\n tsne {\n x\n y\n }\n }\n }\n": types.FindIncidentHistoryDocument, + "\n query FindIncidentFull($filter: IncidentFilterType) {\n incident(filter: $filter) {\n incident_id\n title\n description\n editors {\n userId\n first_name\n last_name\n }\n date\n AllegedDeployerOfAISystem {\n entity_id\n name\n }\n AllegedDeveloperOfAISystem {\n entity_id\n name\n }\n AllegedHarmedOrNearlyHarmedParties {\n entity_id\n name\n }\n implicated_systems {\n entity_id\n name\n }\n nlp_similar_incidents {\n incident_id\n similarity\n }\n editor_similar_incidents\n editor_dissimilar_incidents\n flagged_dissimilar_incidents\n reports {\n submitters\n date_published\n report_number\n title\n description\n url\n image_url\n cloudinary_id\n source_domain\n text\n authors\n epoch_date_submitted\n language\n tags\n inputs_outputs\n }\n embedding {\n from_reports\n vector\n }\n editor_notes\n epoch_date_modified\n tsne {\n x\n y\n }\n }\n }\n": types.FindIncidentFullDocument, + "\n query FindIncidentHistory($filter: History_incidentFilterType) {\n history_incidents(filter: $filter, sort: {epoch_date_modified: DESC}) {\n incident_id\n AllegedDeployerOfAISystem\n AllegedDeveloperOfAISystem\n AllegedHarmedOrNearlyHarmedParties\n implicated_systems\n _id\n date\n description\n modifiedBy\n editor_dissimilar_incidents\n editor_notes\n editor_similar_incidents\n editors\n embedding {\n from_reports\n vector\n }\n epoch_date_modified\n flagged_dissimilar_incidents\n nlp_similar_incidents {\n incident_id\n similarity\n }\n reports\n title\n tsne {\n x\n y\n }\n }\n }\n": types.FindIncidentHistoryDocument, "\n mutation FlagIncidentSimilarity($incidentId: Int!, $dissimilarIds: [Int!]) {\n flagIncidentSimilarity(incidentId: $incidentId, dissimilarIds: $dissimilarIds) {\n incident_id\n flagged_dissimilar_incidents\n editors {\n userId\n }\n }\n }\n": types.FlagIncidentSimilarityDocument, "\n query AllQuickAdd($filter: QuickaddFilterType!) {\n quickadds(filter: $filter) {\n _id\n date_submitted\n url\n source_domain\n }\n }\n": types.AllQuickAddDocument, "\n mutation DeleteOneQuickAdd($filter: QuickaddFilterType) {\n deleteManyQuickadds(filter: $filter) {\n deletedCount\n }\n }\n": types.DeleteOneQuickAddDocument, @@ -41,15 +40,14 @@ const documents = { "\n mutation UpdateReport($filter: ReportFilterType!, $update: ReportUpdateType!) {\n updateOneReport(filter: $filter, update: $update) {\n url\n title\n authors\n submitters\n date_published\n date_downloaded\n date_modified\n epoch_date_published\n epoch_date_downloaded\n epoch_date_modified\n image_url\n text\n plain_text\n tags\n flag\n report_number\n editor_notes\n language\n quiet\n }\n }\n": types.UpdateReportDocument, "\n mutation DeleteOneReport($filter: ReportFilterType!) {\n deleteOneReport(filter: $filter) {\n report_number\n }\n }\n": types.DeleteOneReportDocument, "\n mutation LinkReportsToIncidents($input: LinkReportsToIncidentsInput!) {\n linkReportsToIncidents(input: $input) {\n incident_id\n reports {\n report_number\n }\n }\n }\n": types.LinkReportsToIncidentsDocument, - "\n mutation logReportHistory($input: History_reportInsertInput!) {\n logReportHistory(input: $input) {\n report_number\n }\n }\n": types.LogReportHistoryDocument, - "\n query FindReportHistory($query: History_reportQueryInput) {\n history_reports(query: $query, sortBy: EPOCH_DATE_MODIFIED_DESC) {\n _id\n authors\n cloudinary_id\n date_downloaded\n date_modified\n date_published\n date_submitted\n description\n editor_notes\n embedding {\n from_text_hash\n vector\n }\n epoch_date_downloaded\n epoch_date_modified\n epoch_date_published\n epoch_date_submitted\n flag\n image_url\n inputs_outputs\n is_incident_report\n language\n modifiedBy\n plain_text\n report_number\n submitters\n tags\n text\n title\n url\n source_domain\n user\n quiet\n }\n }\n": types.FindReportHistoryDocument, + "\n query FindReportHistory($filter: History_reportFilterType) {\n history_reports(filter: $filter, sort: { date_modified: DESC }) {\n _id\n authors\n cloudinary_id\n date_downloaded\n date_modified\n date_published\n date_submitted\n description\n editor_notes\n embedding {\n from_text_hash\n vector\n }\n epoch_date_downloaded\n epoch_date_modified\n epoch_date_published\n epoch_date_submitted\n flag\n image_url\n inputs_outputs\n is_incident_report\n language\n modifiedBy\n plain_text\n report_number\n submitters\n tags\n text\n title\n url\n source_domain\n user\n quiet\n }\n }\n": types.FindReportHistoryDocument, "\n query FindReports($filter: ReportFilterType!) {\n reports(filter: $filter) {\n _id\n submitters\n date_published\n report_number\n title\n description\n url\n image_url\n cloudinary_id\n source_domain\n text\n authors\n epoch_date_submitted\n language\n tags\n inputs_outputs\n }\n }\n": types.FindReportsDocument, "\n query FindReportsTable($filter: ReportFilterType!) {\n reports(filter: $filter, sort: { report_number: DESC }) {\n _id\n submitters\n date_published\n date_downloaded\n date_submitted\n date_modified\n report_number\n title\n description\n url\n image_url\n cloudinary_id\n source_domain\n text\n authors\n epoch_date_submitted\n language\n tags\n inputs_outputs\n editor_notes\n is_incident_report\n }\n }\n": types.FindReportsTableDocument, "\n mutation FlagReport($report_number: Int!, $input: Boolean!) {\n flagReport(report_number: $report_number, input: $input) {\n report_number\n flag\n date_modified\n epoch_date_modified\n }\n }\n": types.FlagReportDocument, "\n mutation DeleteSubmission($_id: ObjectId!) {\n deleteOneSubmission(filter: { _id: { EQ: $_id } }) {\n _id\n }\n }\n": types.DeleteSubmissionDocument, - "\n query FindSubmissions {\n submissions {\n _id\n cloudinary_id\n date_downloaded\n date_modified\n date_published\n date_submitted\n description\n image_url\n incident_date\n incident_ids\n incident_editors {\n first_name\n last_name\n userId\n }\n incident_title\n language\n source_domain\n text\n title\n authors\n submitters\n url\n editor_notes\n tags\n nlp_similar_incidents {\n similarity\n incident_id\n }\n editor_similar_incidents\n editor_dissimilar_incidents\n plain_text\n developers {\n entity_id\n name\n }\n deployers {\n entity_id\n name\n }\n harmed_parties {\n entity_id\n name\n }\n status\n user {\n userId\n }\n quiet\n }\n }\n": types.FindSubmissionsDocument, - "\n query FindSubmission($filter: SubmissionFilterType!) {\n submission(filter: $filter) {\n _id\n cloudinary_id\n date_downloaded\n date_modified\n date_published\n date_submitted\n description\n image_url\n incident_date\n incident_ids\n incident_editors {\n first_name\n last_name\n userId\n }\n incident_title\n language\n source_domain\n text\n title\n authors\n submitters\n url\n editor_notes\n tags\n developers {\n entity_id\n name\n }\n deployers {\n entity_id\n name\n }\n harmed_parties {\n entity_id\n name\n }\n nlp_similar_incidents {\n similarity\n incident_id\n }\n editor_similar_incidents\n editor_dissimilar_incidents\n status\n quiet\n }\n }\n": types.FindSubmissionDocument, - "\n mutation UpdateSubmission($filter: SubmissionFilterType!, $update: SubmissionUpdateType!) {\n updateOneSubmission(filter: $filter, update: $update) {\n _id\n cloudinary_id\n date_downloaded\n date_modified\n date_published\n date_submitted\n description\n image_url\n incident_date\n incident_ids\n incident_editors {\n first_name\n last_name\n userId\n }\n incident_title\n language\n source_domain\n text\n title\n authors\n submitters\n url\n editor_notes\n tags\n developers {\n entity_id\n name\n }\n deployers {\n entity_id\n name\n }\n harmed_parties {\n entity_id\n name\n }\n nlp_similar_incidents {\n similarity\n incident_id\n }\n editor_similar_incidents\n editor_dissimilar_incidents\n }\n }\n": types.UpdateSubmissionDocument, + "\n query FindSubmissions {\n submissions {\n _id\n cloudinary_id\n date_downloaded\n date_modified\n date_published\n date_submitted\n description\n image_url\n incident_date\n incident_ids\n incident_editors {\n first_name\n last_name\n userId\n }\n incident_title\n language\n source_domain\n text\n title\n authors\n submitters\n url\n editor_notes\n tags\n nlp_similar_incidents {\n similarity\n incident_id\n }\n editor_similar_incidents\n editor_dissimilar_incidents\n plain_text\n developers {\n entity_id\n name\n }\n deployers {\n entity_id\n name\n }\n harmed_parties {\n entity_id\n name\n }\n status\n user {\n userId\n }\n quiet\n implicated_systems {\n entity_id\n name\n }\n }\n }\n": types.FindSubmissionsDocument, + "\n query FindSubmission($filter: SubmissionFilterType!) {\n submission(filter: $filter) {\n _id\n cloudinary_id\n date_downloaded\n date_modified\n date_published\n date_submitted\n description\n image_url\n incident_date\n incident_ids\n incident_editors {\n first_name\n last_name\n userId\n }\n incident_title\n language\n source_domain\n text\n title\n authors\n submitters\n url\n editor_notes\n tags\n developers {\n entity_id\n name\n }\n deployers {\n entity_id\n name\n }\n harmed_parties {\n entity_id\n name\n }\n nlp_similar_incidents {\n similarity\n incident_id\n }\n editor_similar_incidents\n editor_dissimilar_incidents\n status\n quiet\n implicated_systems {\n entity_id\n name\n }\n }\n }\n": types.FindSubmissionDocument, + "\n mutation UpdateSubmission($filter: SubmissionFilterType!, $update: SubmissionUpdateType!) {\n updateOneSubmission(filter: $filter, update: $update) {\n _id\n cloudinary_id\n date_downloaded\n date_modified\n date_published\n date_submitted\n description\n image_url\n incident_date\n incident_ids\n incident_editors {\n first_name\n last_name\n userId\n }\n incident_title\n language\n source_domain\n text\n title\n authors\n submitters\n url\n editor_notes\n tags\n developers {\n entity_id\n name\n }\n deployers {\n entity_id\n name\n }\n harmed_parties {\n entity_id\n name\n }\n nlp_similar_incidents {\n similarity\n incident_id\n }\n editor_similar_incidents\n editor_dissimilar_incidents\n implicated_systems {\n entity_id\n name\n }\n }\n }\n": types.UpdateSubmissionDocument, "\n mutation InsertSubmission($data: SubmissionInsertType!) {\n insertOneSubmission(data: $data) {\n _id\n }\n }\n": types.InsertSubmissionDocument, "\n mutation PromoteSubmission($input: PromoteSubmissionToReportInput!) {\n promoteSubmissionToReport(input: $input) {\n incident_ids\n report_number\n }\n }\n": types.PromoteSubmissionDocument, "\n mutation UpsertSubscription($filter: SubscriptionFilterType!, $update: SubscriptionInsertType!) {\n upsertOneSubscription(filter: $filter, update: $update) {\n _id\n }\n }\n": types.UpsertSubscriptionDocument, @@ -115,19 +113,19 @@ export function gql(source: "\n mutation UpdateEntity($filter: EntityFilterType /** * The gql function is used to parse GraphQL queries into a document that can be used by GraphQL clients. */ -export function gql(source: "\n query FindIncident($filter: IncidentFilterType) {\n incident(filter: $filter) {\n incident_id\n title\n description\n editors {\n userId\n first_name\n last_name\n }\n date\n AllegedDeployerOfAISystem {\n entity_id\n name\n }\n AllegedDeveloperOfAISystem {\n entity_id\n name\n }\n AllegedHarmedOrNearlyHarmedParties {\n entity_id\n name\n }\n nlp_similar_incidents {\n incident_id\n similarity\n }\n editor_similar_incidents\n editor_dissimilar_incidents\n flagged_dissimilar_incidents\n reports {\n report_number\n }\n embedding {\n from_reports\n vector\n }\n editor_notes\n }\n }\n"): (typeof documents)["\n query FindIncident($filter: IncidentFilterType) {\n incident(filter: $filter) {\n incident_id\n title\n description\n editors {\n userId\n first_name\n last_name\n }\n date\n AllegedDeployerOfAISystem {\n entity_id\n name\n }\n AllegedDeveloperOfAISystem {\n entity_id\n name\n }\n AllegedHarmedOrNearlyHarmedParties {\n entity_id\n name\n }\n nlp_similar_incidents {\n incident_id\n similarity\n }\n editor_similar_incidents\n editor_dissimilar_incidents\n flagged_dissimilar_incidents\n reports {\n report_number\n }\n embedding {\n from_reports\n vector\n }\n editor_notes\n }\n }\n"]; +export function gql(source: "\n query FindIncident($filter: IncidentFilterType) {\n incident(filter: $filter) {\n incident_id\n title\n description\n editors {\n userId\n first_name\n last_name\n }\n date\n AllegedDeployerOfAISystem {\n entity_id\n name\n }\n AllegedDeveloperOfAISystem {\n entity_id\n name\n }\n AllegedHarmedOrNearlyHarmedParties {\n entity_id\n name\n }\n implicated_systems {\n entity_id\n name\n }\n nlp_similar_incidents {\n incident_id\n similarity\n }\n editor_similar_incidents\n editor_dissimilar_incidents\n flagged_dissimilar_incidents\n reports {\n report_number\n }\n embedding {\n from_reports\n vector\n }\n editor_notes\n }\n }\n"): (typeof documents)["\n query FindIncident($filter: IncidentFilterType) {\n incident(filter: $filter) {\n incident_id\n title\n description\n editors {\n userId\n first_name\n last_name\n }\n date\n AllegedDeployerOfAISystem {\n entity_id\n name\n }\n AllegedDeveloperOfAISystem {\n entity_id\n name\n }\n AllegedHarmedOrNearlyHarmedParties {\n entity_id\n name\n }\n implicated_systems {\n entity_id\n name\n }\n nlp_similar_incidents {\n incident_id\n similarity\n }\n editor_similar_incidents\n editor_dissimilar_incidents\n flagged_dissimilar_incidents\n reports {\n report_number\n }\n embedding {\n from_reports\n vector\n }\n editor_notes\n }\n }\n"]; /** * The gql function is used to parse GraphQL queries into a document that can be used by GraphQL clients. */ -export function gql(source: "\n query FindIncidentsTable($filter: IncidentFilterType) {\n incidents(filter: $filter) {\n incident_id\n title\n description\n editors {\n userId\n first_name\n last_name\n }\n date\n AllegedDeployerOfAISystem {\n entity_id\n name\n }\n AllegedDeveloperOfAISystem {\n entity_id\n name\n }\n AllegedHarmedOrNearlyHarmedParties {\n entity_id\n name\n }\n reports {\n report_number\n }\n }\n }\n"): (typeof documents)["\n query FindIncidentsTable($filter: IncidentFilterType) {\n incidents(filter: $filter) {\n incident_id\n title\n description\n editors {\n userId\n first_name\n last_name\n }\n date\n AllegedDeployerOfAISystem {\n entity_id\n name\n }\n AllegedDeveloperOfAISystem {\n entity_id\n name\n }\n AllegedHarmedOrNearlyHarmedParties {\n entity_id\n name\n }\n reports {\n report_number\n }\n }\n }\n"]; +export function gql(source: "\n query FindIncidentsTable($filter: IncidentFilterType) {\n incidents(filter: $filter) {\n incident_id\n title\n description\n editors {\n userId\n first_name\n last_name\n }\n date\n AllegedDeployerOfAISystem {\n entity_id\n name\n }\n AllegedDeveloperOfAISystem {\n entity_id\n name\n }\n AllegedHarmedOrNearlyHarmedParties {\n entity_id\n name\n }\n implicated_systems {\n entity_id\n name\n }\n reports {\n report_number\n }\n }\n }\n"): (typeof documents)["\n query FindIncidentsTable($filter: IncidentFilterType) {\n incidents(filter: $filter) {\n incident_id\n title\n description\n editors {\n userId\n first_name\n last_name\n }\n date\n AllegedDeployerOfAISystem {\n entity_id\n name\n }\n AllegedDeveloperOfAISystem {\n entity_id\n name\n }\n AllegedHarmedOrNearlyHarmedParties {\n entity_id\n name\n }\n implicated_systems {\n entity_id\n name\n }\n reports {\n report_number\n }\n }\n }\n"]; /** * The gql function is used to parse GraphQL queries into a document that can be used by GraphQL clients. */ -export function gql(source: "\n query FindIncidentEntities($filter: IncidentFilterType) {\n incident(filter: $filter) {\n incident_id\n AllegedDeployerOfAISystem {\n entity_id\n name\n }\n AllegedDeveloperOfAISystem {\n entity_id\n name\n }\n AllegedHarmedOrNearlyHarmedParties {\n entity_id\n name\n }\n }\n }\n"): (typeof documents)["\n query FindIncidentEntities($filter: IncidentFilterType) {\n incident(filter: $filter) {\n incident_id\n AllegedDeployerOfAISystem {\n entity_id\n name\n }\n AllegedDeveloperOfAISystem {\n entity_id\n name\n }\n AllegedHarmedOrNearlyHarmedParties {\n entity_id\n name\n }\n }\n }\n"]; +export function gql(source: "\n query FindIncidentEntities($filter: IncidentFilterType) {\n incident(filter: $filter) {\n incident_id\n AllegedDeployerOfAISystem {\n entity_id\n name\n }\n AllegedDeveloperOfAISystem {\n entity_id\n name\n }\n AllegedHarmedOrNearlyHarmedParties {\n entity_id\n name\n }\n implicated_systems {\n entity_id\n name\n }\n }\n }\n"): (typeof documents)["\n query FindIncidentEntities($filter: IncidentFilterType) {\n incident(filter: $filter) {\n incident_id\n AllegedDeployerOfAISystem {\n entity_id\n name\n }\n AllegedDeveloperOfAISystem {\n entity_id\n name\n }\n AllegedHarmedOrNearlyHarmedParties {\n entity_id\n name\n }\n implicated_systems {\n entity_id\n name\n }\n }\n }\n"]; /** * The gql function is used to parse GraphQL queries into a document that can be used by GraphQL clients. */ -export function gql(source: "\n query FindIncidents($filter: IncidentFilterType) {\n incidents(filter: $filter) {\n incident_id\n title\n description\n editors {\n userId\n first_name\n last_name\n }\n date\n AllegedDeployerOfAISystem {\n entity_id\n name\n }\n AllegedDeveloperOfAISystem {\n entity_id\n name\n }\n AllegedHarmedOrNearlyHarmedParties {\n entity_id\n name\n }\n nlp_similar_incidents {\n incident_id\n similarity\n }\n editor_similar_incidents\n editor_dissimilar_incidents\n flagged_dissimilar_incidents\n reports {\n report_number\n }\n embedding {\n from_reports\n vector\n }\n }\n }\n"): (typeof documents)["\n query FindIncidents($filter: IncidentFilterType) {\n incidents(filter: $filter) {\n incident_id\n title\n description\n editors {\n userId\n first_name\n last_name\n }\n date\n AllegedDeployerOfAISystem {\n entity_id\n name\n }\n AllegedDeveloperOfAISystem {\n entity_id\n name\n }\n AllegedHarmedOrNearlyHarmedParties {\n entity_id\n name\n }\n nlp_similar_incidents {\n incident_id\n similarity\n }\n editor_similar_incidents\n editor_dissimilar_incidents\n flagged_dissimilar_incidents\n reports {\n report_number\n }\n embedding {\n from_reports\n vector\n }\n }\n }\n"]; +export function gql(source: "\n query FindIncidents($filter: IncidentFilterType) {\n incidents(filter: $filter) {\n incident_id\n title\n description\n editors {\n userId\n first_name\n last_name\n }\n date\n AllegedDeployerOfAISystem {\n entity_id\n name\n }\n AllegedDeveloperOfAISystem {\n entity_id\n name\n }\n AllegedHarmedOrNearlyHarmedParties {\n entity_id\n name\n }\n implicated_systems {\n entity_id\n name\n }\n nlp_similar_incidents {\n incident_id\n similarity\n }\n editor_similar_incidents\n editor_dissimilar_incidents\n flagged_dissimilar_incidents\n reports {\n report_number\n }\n embedding {\n from_reports\n vector\n }\n }\n }\n"): (typeof documents)["\n query FindIncidents($filter: IncidentFilterType) {\n incidents(filter: $filter) {\n incident_id\n title\n description\n editors {\n userId\n first_name\n last_name\n }\n date\n AllegedDeployerOfAISystem {\n entity_id\n name\n }\n AllegedDeveloperOfAISystem {\n entity_id\n name\n }\n AllegedHarmedOrNearlyHarmedParties {\n entity_id\n name\n }\n implicated_systems {\n entity_id\n name\n }\n nlp_similar_incidents {\n incident_id\n similarity\n }\n editor_similar_incidents\n editor_dissimilar_incidents\n flagged_dissimilar_incidents\n reports {\n report_number\n }\n embedding {\n from_reports\n vector\n }\n }\n }\n"]; /** * The gql function is used to parse GraphQL queries into a document that can be used by GraphQL clients. */ @@ -135,7 +133,7 @@ export function gql(source: "\n query FindIncidentsTitles($filter: IncidentFilt /** * The gql function is used to parse GraphQL queries into a document that can be used by GraphQL clients. */ -export function gql(source: "\n mutation UpdateIncident($filter: IncidentFilterType!, $update: IncidentUpdateType!) {\n updateOneIncident(filter: $filter, update: $update) {\n incident_id\n title\n description\n editors {\n userId\n first_name\n last_name\n }\n date\n AllegedDeployerOfAISystem {\n entity_id\n name\n }\n AllegedDeveloperOfAISystem {\n entity_id\n name\n }\n AllegedHarmedOrNearlyHarmedParties {\n entity_id\n name\n }\n nlp_similar_incidents {\n incident_id\n similarity\n }\n editor_similar_incidents\n editor_dissimilar_incidents\n flagged_dissimilar_incidents\n reports {\n report_number\n }\n embedding {\n from_reports\n vector\n }\n editor_notes\n }\n }\n"): (typeof documents)["\n mutation UpdateIncident($filter: IncidentFilterType!, $update: IncidentUpdateType!) {\n updateOneIncident(filter: $filter, update: $update) {\n incident_id\n title\n description\n editors {\n userId\n first_name\n last_name\n }\n date\n AllegedDeployerOfAISystem {\n entity_id\n name\n }\n AllegedDeveloperOfAISystem {\n entity_id\n name\n }\n AllegedHarmedOrNearlyHarmedParties {\n entity_id\n name\n }\n nlp_similar_incidents {\n incident_id\n similarity\n }\n editor_similar_incidents\n editor_dissimilar_incidents\n flagged_dissimilar_incidents\n reports {\n report_number\n }\n embedding {\n from_reports\n vector\n }\n editor_notes\n }\n }\n"]; +export function gql(source: "\n mutation UpdateIncident($filter: IncidentFilterType!, $update: IncidentUpdateType!) {\n updateOneIncident(filter: $filter, update: $update) {\n incident_id\n title\n description\n editors {\n userId\n first_name\n last_name\n }\n date\n AllegedDeployerOfAISystem {\n entity_id\n name\n }\n AllegedDeveloperOfAISystem {\n entity_id\n name\n }\n AllegedHarmedOrNearlyHarmedParties {\n entity_id\n name\n }\n implicated_systems {\n entity_id\n name\n }\n nlp_similar_incidents {\n incident_id\n similarity\n }\n editor_similar_incidents\n editor_dissimilar_incidents\n flagged_dissimilar_incidents\n reports {\n report_number\n }\n embedding {\n from_reports\n vector\n }\n editor_notes\n }\n }\n"): (typeof documents)["\n mutation UpdateIncident($filter: IncidentFilterType!, $update: IncidentUpdateType!) {\n updateOneIncident(filter: $filter, update: $update) {\n incident_id\n title\n description\n editors {\n userId\n first_name\n last_name\n }\n date\n AllegedDeployerOfAISystem {\n entity_id\n name\n }\n AllegedDeveloperOfAISystem {\n entity_id\n name\n }\n AllegedHarmedOrNearlyHarmedParties {\n entity_id\n name\n }\n implicated_systems {\n entity_id\n name\n }\n nlp_similar_incidents {\n incident_id\n similarity\n }\n editor_similar_incidents\n editor_dissimilar_incidents\n flagged_dissimilar_incidents\n reports {\n report_number\n }\n embedding {\n from_reports\n vector\n }\n editor_notes\n }\n }\n"]; /** * The gql function is used to parse GraphQL queries into a document that can be used by GraphQL clients. */ @@ -151,15 +149,11 @@ export function gql(source: "\n query FindLastIncident {\n incidents(sort: { /** * The gql function is used to parse GraphQL queries into a document that can be used by GraphQL clients. */ -export function gql(source: "\n query FindIncidentFull($filter: IncidentFilterType) {\n incident(filter: $filter) {\n incident_id\n title\n description\n editors {\n userId\n first_name\n last_name\n }\n date\n AllegedDeployerOfAISystem {\n entity_id\n name\n }\n AllegedDeveloperOfAISystem {\n entity_id\n name\n }\n AllegedHarmedOrNearlyHarmedParties {\n entity_id\n name\n }\n nlp_similar_incidents {\n incident_id\n similarity\n }\n editor_similar_incidents\n editor_dissimilar_incidents\n flagged_dissimilar_incidents\n reports {\n submitters\n date_published\n report_number\n title\n description\n url\n image_url\n cloudinary_id\n source_domain\n text\n authors\n epoch_date_submitted\n language\n tags\n inputs_outputs\n }\n embedding {\n from_reports\n vector\n }\n editor_notes\n epoch_date_modified\n tsne {\n x\n y\n }\n }\n }\n"): (typeof documents)["\n query FindIncidentFull($filter: IncidentFilterType) {\n incident(filter: $filter) {\n incident_id\n title\n description\n editors {\n userId\n first_name\n last_name\n }\n date\n AllegedDeployerOfAISystem {\n entity_id\n name\n }\n AllegedDeveloperOfAISystem {\n entity_id\n name\n }\n AllegedHarmedOrNearlyHarmedParties {\n entity_id\n name\n }\n nlp_similar_incidents {\n incident_id\n similarity\n }\n editor_similar_incidents\n editor_dissimilar_incidents\n flagged_dissimilar_incidents\n reports {\n submitters\n date_published\n report_number\n title\n description\n url\n image_url\n cloudinary_id\n source_domain\n text\n authors\n epoch_date_submitted\n language\n tags\n inputs_outputs\n }\n embedding {\n from_reports\n vector\n }\n editor_notes\n epoch_date_modified\n tsne {\n x\n y\n }\n }\n }\n"]; +export function gql(source: "\n query FindIncidentFull($filter: IncidentFilterType) {\n incident(filter: $filter) {\n incident_id\n title\n description\n editors {\n userId\n first_name\n last_name\n }\n date\n AllegedDeployerOfAISystem {\n entity_id\n name\n }\n AllegedDeveloperOfAISystem {\n entity_id\n name\n }\n AllegedHarmedOrNearlyHarmedParties {\n entity_id\n name\n }\n implicated_systems {\n entity_id\n name\n }\n nlp_similar_incidents {\n incident_id\n similarity\n }\n editor_similar_incidents\n editor_dissimilar_incidents\n flagged_dissimilar_incidents\n reports {\n submitters\n date_published\n report_number\n title\n description\n url\n image_url\n cloudinary_id\n source_domain\n text\n authors\n epoch_date_submitted\n language\n tags\n inputs_outputs\n }\n embedding {\n from_reports\n vector\n }\n editor_notes\n epoch_date_modified\n tsne {\n x\n y\n }\n }\n }\n"): (typeof documents)["\n query FindIncidentFull($filter: IncidentFilterType) {\n incident(filter: $filter) {\n incident_id\n title\n description\n editors {\n userId\n first_name\n last_name\n }\n date\n AllegedDeployerOfAISystem {\n entity_id\n name\n }\n AllegedDeveloperOfAISystem {\n entity_id\n name\n }\n AllegedHarmedOrNearlyHarmedParties {\n entity_id\n name\n }\n implicated_systems {\n entity_id\n name\n }\n nlp_similar_incidents {\n incident_id\n similarity\n }\n editor_similar_incidents\n editor_dissimilar_incidents\n flagged_dissimilar_incidents\n reports {\n submitters\n date_published\n report_number\n title\n description\n url\n image_url\n cloudinary_id\n source_domain\n text\n authors\n epoch_date_submitted\n language\n tags\n inputs_outputs\n }\n embedding {\n from_reports\n vector\n }\n editor_notes\n epoch_date_modified\n tsne {\n x\n y\n }\n }\n }\n"]; /** * The gql function is used to parse GraphQL queries into a document that can be used by GraphQL clients. */ -export function gql(source: "\n mutation logIncidentHistory($input: History_incidentInsertInput!) {\n logIncidentHistory(input: $input) {\n incident_id\n }\n }\n"): (typeof documents)["\n mutation logIncidentHistory($input: History_incidentInsertInput!) {\n logIncidentHistory(input: $input) {\n incident_id\n }\n }\n"]; -/** - * The gql function is used to parse GraphQL queries into a document that can be used by GraphQL clients. - */ -export function gql(source: "\n query FindIncidentHistory($query: History_incidentQueryInput) {\n history_incidents(query: $query, sortBy: EPOCH_DATE_MODIFIED_DESC) {\n incident_id\n AllegedDeployerOfAISystem\n AllegedDeveloperOfAISystem\n AllegedHarmedOrNearlyHarmedParties\n _id\n date\n description\n modifiedBy\n editor_dissimilar_incidents\n editor_notes\n editor_similar_incidents\n editors\n embedding {\n from_reports\n vector\n }\n epoch_date_modified\n flagged_dissimilar_incidents\n nlp_similar_incidents {\n incident_id\n similarity\n }\n reports\n title\n tsne {\n x\n y\n }\n }\n }\n"): (typeof documents)["\n query FindIncidentHistory($query: History_incidentQueryInput) {\n history_incidents(query: $query, sortBy: EPOCH_DATE_MODIFIED_DESC) {\n incident_id\n AllegedDeployerOfAISystem\n AllegedDeveloperOfAISystem\n AllegedHarmedOrNearlyHarmedParties\n _id\n date\n description\n modifiedBy\n editor_dissimilar_incidents\n editor_notes\n editor_similar_incidents\n editors\n embedding {\n from_reports\n vector\n }\n epoch_date_modified\n flagged_dissimilar_incidents\n nlp_similar_incidents {\n incident_id\n similarity\n }\n reports\n title\n tsne {\n x\n y\n }\n }\n }\n"]; +export function gql(source: "\n query FindIncidentHistory($filter: History_incidentFilterType) {\n history_incidents(filter: $filter, sort: {epoch_date_modified: DESC}) {\n incident_id\n AllegedDeployerOfAISystem\n AllegedDeveloperOfAISystem\n AllegedHarmedOrNearlyHarmedParties\n implicated_systems\n _id\n date\n description\n modifiedBy\n editor_dissimilar_incidents\n editor_notes\n editor_similar_incidents\n editors\n embedding {\n from_reports\n vector\n }\n epoch_date_modified\n flagged_dissimilar_incidents\n nlp_similar_incidents {\n incident_id\n similarity\n }\n reports\n title\n tsne {\n x\n y\n }\n }\n }\n"): (typeof documents)["\n query FindIncidentHistory($filter: History_incidentFilterType) {\n history_incidents(filter: $filter, sort: {epoch_date_modified: DESC}) {\n incident_id\n AllegedDeployerOfAISystem\n AllegedDeveloperOfAISystem\n AllegedHarmedOrNearlyHarmedParties\n implicated_systems\n _id\n date\n description\n modifiedBy\n editor_dissimilar_incidents\n editor_notes\n editor_similar_incidents\n editors\n embedding {\n from_reports\n vector\n }\n epoch_date_modified\n flagged_dissimilar_incidents\n nlp_similar_incidents {\n incident_id\n similarity\n }\n reports\n title\n tsne {\n x\n y\n }\n }\n }\n"]; /** * The gql function is used to parse GraphQL queries into a document that can be used by GraphQL clients. */ @@ -199,11 +193,7 @@ export function gql(source: "\n mutation LinkReportsToIncidents($input: LinkRep /** * The gql function is used to parse GraphQL queries into a document that can be used by GraphQL clients. */ -export function gql(source: "\n mutation logReportHistory($input: History_reportInsertInput!) {\n logReportHistory(input: $input) {\n report_number\n }\n }\n"): (typeof documents)["\n mutation logReportHistory($input: History_reportInsertInput!) {\n logReportHistory(input: $input) {\n report_number\n }\n }\n"]; -/** - * The gql function is used to parse GraphQL queries into a document that can be used by GraphQL clients. - */ -export function gql(source: "\n query FindReportHistory($query: History_reportQueryInput) {\n history_reports(query: $query, sortBy: EPOCH_DATE_MODIFIED_DESC) {\n _id\n authors\n cloudinary_id\n date_downloaded\n date_modified\n date_published\n date_submitted\n description\n editor_notes\n embedding {\n from_text_hash\n vector\n }\n epoch_date_downloaded\n epoch_date_modified\n epoch_date_published\n epoch_date_submitted\n flag\n image_url\n inputs_outputs\n is_incident_report\n language\n modifiedBy\n plain_text\n report_number\n submitters\n tags\n text\n title\n url\n source_domain\n user\n quiet\n }\n }\n"): (typeof documents)["\n query FindReportHistory($query: History_reportQueryInput) {\n history_reports(query: $query, sortBy: EPOCH_DATE_MODIFIED_DESC) {\n _id\n authors\n cloudinary_id\n date_downloaded\n date_modified\n date_published\n date_submitted\n description\n editor_notes\n embedding {\n from_text_hash\n vector\n }\n epoch_date_downloaded\n epoch_date_modified\n epoch_date_published\n epoch_date_submitted\n flag\n image_url\n inputs_outputs\n is_incident_report\n language\n modifiedBy\n plain_text\n report_number\n submitters\n tags\n text\n title\n url\n source_domain\n user\n quiet\n }\n }\n"]; +export function gql(source: "\n query FindReportHistory($filter: History_reportFilterType) {\n history_reports(filter: $filter, sort: { date_modified: DESC }) {\n _id\n authors\n cloudinary_id\n date_downloaded\n date_modified\n date_published\n date_submitted\n description\n editor_notes\n embedding {\n from_text_hash\n vector\n }\n epoch_date_downloaded\n epoch_date_modified\n epoch_date_published\n epoch_date_submitted\n flag\n image_url\n inputs_outputs\n is_incident_report\n language\n modifiedBy\n plain_text\n report_number\n submitters\n tags\n text\n title\n url\n source_domain\n user\n quiet\n }\n }\n"): (typeof documents)["\n query FindReportHistory($filter: History_reportFilterType) {\n history_reports(filter: $filter, sort: { date_modified: DESC }) {\n _id\n authors\n cloudinary_id\n date_downloaded\n date_modified\n date_published\n date_submitted\n description\n editor_notes\n embedding {\n from_text_hash\n vector\n }\n epoch_date_downloaded\n epoch_date_modified\n epoch_date_published\n epoch_date_submitted\n flag\n image_url\n inputs_outputs\n is_incident_report\n language\n modifiedBy\n plain_text\n report_number\n submitters\n tags\n text\n title\n url\n source_domain\n user\n quiet\n }\n }\n"]; /** * The gql function is used to parse GraphQL queries into a document that can be used by GraphQL clients. */ @@ -223,15 +213,15 @@ export function gql(source: "\n mutation DeleteSubmission($_id: ObjectId!) {\n /** * The gql function is used to parse GraphQL queries into a document that can be used by GraphQL clients. */ -export function gql(source: "\n query FindSubmissions {\n submissions {\n _id\n cloudinary_id\n date_downloaded\n date_modified\n date_published\n date_submitted\n description\n image_url\n incident_date\n incident_ids\n incident_editors {\n first_name\n last_name\n userId\n }\n incident_title\n language\n source_domain\n text\n title\n authors\n submitters\n url\n editor_notes\n tags\n nlp_similar_incidents {\n similarity\n incident_id\n }\n editor_similar_incidents\n editor_dissimilar_incidents\n plain_text\n developers {\n entity_id\n name\n }\n deployers {\n entity_id\n name\n }\n harmed_parties {\n entity_id\n name\n }\n status\n user {\n userId\n }\n quiet\n }\n }\n"): (typeof documents)["\n query FindSubmissions {\n submissions {\n _id\n cloudinary_id\n date_downloaded\n date_modified\n date_published\n date_submitted\n description\n image_url\n incident_date\n incident_ids\n incident_editors {\n first_name\n last_name\n userId\n }\n incident_title\n language\n source_domain\n text\n title\n authors\n submitters\n url\n editor_notes\n tags\n nlp_similar_incidents {\n similarity\n incident_id\n }\n editor_similar_incidents\n editor_dissimilar_incidents\n plain_text\n developers {\n entity_id\n name\n }\n deployers {\n entity_id\n name\n }\n harmed_parties {\n entity_id\n name\n }\n status\n user {\n userId\n }\n quiet\n }\n }\n"]; +export function gql(source: "\n query FindSubmissions {\n submissions {\n _id\n cloudinary_id\n date_downloaded\n date_modified\n date_published\n date_submitted\n description\n image_url\n incident_date\n incident_ids\n incident_editors {\n first_name\n last_name\n userId\n }\n incident_title\n language\n source_domain\n text\n title\n authors\n submitters\n url\n editor_notes\n tags\n nlp_similar_incidents {\n similarity\n incident_id\n }\n editor_similar_incidents\n editor_dissimilar_incidents\n plain_text\n developers {\n entity_id\n name\n }\n deployers {\n entity_id\n name\n }\n harmed_parties {\n entity_id\n name\n }\n status\n user {\n userId\n }\n quiet\n implicated_systems {\n entity_id\n name\n }\n }\n }\n"): (typeof documents)["\n query FindSubmissions {\n submissions {\n _id\n cloudinary_id\n date_downloaded\n date_modified\n date_published\n date_submitted\n description\n image_url\n incident_date\n incident_ids\n incident_editors {\n first_name\n last_name\n userId\n }\n incident_title\n language\n source_domain\n text\n title\n authors\n submitters\n url\n editor_notes\n tags\n nlp_similar_incidents {\n similarity\n incident_id\n }\n editor_similar_incidents\n editor_dissimilar_incidents\n plain_text\n developers {\n entity_id\n name\n }\n deployers {\n entity_id\n name\n }\n harmed_parties {\n entity_id\n name\n }\n status\n user {\n userId\n }\n quiet\n implicated_systems {\n entity_id\n name\n }\n }\n }\n"]; /** * The gql function is used to parse GraphQL queries into a document that can be used by GraphQL clients. */ -export function gql(source: "\n query FindSubmission($filter: SubmissionFilterType!) {\n submission(filter: $filter) {\n _id\n cloudinary_id\n date_downloaded\n date_modified\n date_published\n date_submitted\n description\n image_url\n incident_date\n incident_ids\n incident_editors {\n first_name\n last_name\n userId\n }\n incident_title\n language\n source_domain\n text\n title\n authors\n submitters\n url\n editor_notes\n tags\n developers {\n entity_id\n name\n }\n deployers {\n entity_id\n name\n }\n harmed_parties {\n entity_id\n name\n }\n nlp_similar_incidents {\n similarity\n incident_id\n }\n editor_similar_incidents\n editor_dissimilar_incidents\n status\n quiet\n }\n }\n"): (typeof documents)["\n query FindSubmission($filter: SubmissionFilterType!) {\n submission(filter: $filter) {\n _id\n cloudinary_id\n date_downloaded\n date_modified\n date_published\n date_submitted\n description\n image_url\n incident_date\n incident_ids\n incident_editors {\n first_name\n last_name\n userId\n }\n incident_title\n language\n source_domain\n text\n title\n authors\n submitters\n url\n editor_notes\n tags\n developers {\n entity_id\n name\n }\n deployers {\n entity_id\n name\n }\n harmed_parties {\n entity_id\n name\n }\n nlp_similar_incidents {\n similarity\n incident_id\n }\n editor_similar_incidents\n editor_dissimilar_incidents\n status\n quiet\n }\n }\n"]; +export function gql(source: "\n query FindSubmission($filter: SubmissionFilterType!) {\n submission(filter: $filter) {\n _id\n cloudinary_id\n date_downloaded\n date_modified\n date_published\n date_submitted\n description\n image_url\n incident_date\n incident_ids\n incident_editors {\n first_name\n last_name\n userId\n }\n incident_title\n language\n source_domain\n text\n title\n authors\n submitters\n url\n editor_notes\n tags\n developers {\n entity_id\n name\n }\n deployers {\n entity_id\n name\n }\n harmed_parties {\n entity_id\n name\n }\n nlp_similar_incidents {\n similarity\n incident_id\n }\n editor_similar_incidents\n editor_dissimilar_incidents\n status\n quiet\n implicated_systems {\n entity_id\n name\n }\n }\n }\n"): (typeof documents)["\n query FindSubmission($filter: SubmissionFilterType!) {\n submission(filter: $filter) {\n _id\n cloudinary_id\n date_downloaded\n date_modified\n date_published\n date_submitted\n description\n image_url\n incident_date\n incident_ids\n incident_editors {\n first_name\n last_name\n userId\n }\n incident_title\n language\n source_domain\n text\n title\n authors\n submitters\n url\n editor_notes\n tags\n developers {\n entity_id\n name\n }\n deployers {\n entity_id\n name\n }\n harmed_parties {\n entity_id\n name\n }\n nlp_similar_incidents {\n similarity\n incident_id\n }\n editor_similar_incidents\n editor_dissimilar_incidents\n status\n quiet\n implicated_systems {\n entity_id\n name\n }\n }\n }\n"]; /** * The gql function is used to parse GraphQL queries into a document that can be used by GraphQL clients. */ -export function gql(source: "\n mutation UpdateSubmission($filter: SubmissionFilterType!, $update: SubmissionUpdateType!) {\n updateOneSubmission(filter: $filter, update: $update) {\n _id\n cloudinary_id\n date_downloaded\n date_modified\n date_published\n date_submitted\n description\n image_url\n incident_date\n incident_ids\n incident_editors {\n first_name\n last_name\n userId\n }\n incident_title\n language\n source_domain\n text\n title\n authors\n submitters\n url\n editor_notes\n tags\n developers {\n entity_id\n name\n }\n deployers {\n entity_id\n name\n }\n harmed_parties {\n entity_id\n name\n }\n nlp_similar_incidents {\n similarity\n incident_id\n }\n editor_similar_incidents\n editor_dissimilar_incidents\n }\n }\n"): (typeof documents)["\n mutation UpdateSubmission($filter: SubmissionFilterType!, $update: SubmissionUpdateType!) {\n updateOneSubmission(filter: $filter, update: $update) {\n _id\n cloudinary_id\n date_downloaded\n date_modified\n date_published\n date_submitted\n description\n image_url\n incident_date\n incident_ids\n incident_editors {\n first_name\n last_name\n userId\n }\n incident_title\n language\n source_domain\n text\n title\n authors\n submitters\n url\n editor_notes\n tags\n developers {\n entity_id\n name\n }\n deployers {\n entity_id\n name\n }\n harmed_parties {\n entity_id\n name\n }\n nlp_similar_incidents {\n similarity\n incident_id\n }\n editor_similar_incidents\n editor_dissimilar_incidents\n }\n }\n"]; +export function gql(source: "\n mutation UpdateSubmission($filter: SubmissionFilterType!, $update: SubmissionUpdateType!) {\n updateOneSubmission(filter: $filter, update: $update) {\n _id\n cloudinary_id\n date_downloaded\n date_modified\n date_published\n date_submitted\n description\n image_url\n incident_date\n incident_ids\n incident_editors {\n first_name\n last_name\n userId\n }\n incident_title\n language\n source_domain\n text\n title\n authors\n submitters\n url\n editor_notes\n tags\n developers {\n entity_id\n name\n }\n deployers {\n entity_id\n name\n }\n harmed_parties {\n entity_id\n name\n }\n nlp_similar_incidents {\n similarity\n incident_id\n }\n editor_similar_incidents\n editor_dissimilar_incidents\n implicated_systems {\n entity_id\n name\n }\n }\n }\n"): (typeof documents)["\n mutation UpdateSubmission($filter: SubmissionFilterType!, $update: SubmissionUpdateType!) {\n updateOneSubmission(filter: $filter, update: $update) {\n _id\n cloudinary_id\n date_downloaded\n date_modified\n date_published\n date_submitted\n description\n image_url\n incident_date\n incident_ids\n incident_editors {\n first_name\n last_name\n userId\n }\n incident_title\n language\n source_domain\n text\n title\n authors\n submitters\n url\n editor_notes\n tags\n developers {\n entity_id\n name\n }\n deployers {\n entity_id\n name\n }\n harmed_parties {\n entity_id\n name\n }\n nlp_similar_incidents {\n similarity\n incident_id\n }\n editor_similar_incidents\n editor_dissimilar_incidents\n implicated_systems {\n entity_id\n name\n }\n }\n }\n"]; /** * The gql function is used to parse GraphQL queries into a document that can be used by GraphQL clients. */ diff --git a/site/gatsby-site/server/generated/graphql.ts b/site/gatsby-site/server/generated/graphql.ts index c8cfc39840..d2e5da2ed9 100644 --- a/site/gatsby-site/server/generated/graphql.ts +++ b/site/gatsby-site/server/generated/graphql.ts @@ -98,25 +98,18 @@ export type BooleanNotFilter = { export type Candidate = { __typename?: 'Candidate'; _id?: Maybe; - authors?: Maybe>>; - classification_similarity?: Maybe>>; - date_downloaded?: Maybe; - date_published?: Maybe; + created_at?: Maybe; + date_published: Scalars['String']['output']; dismissed?: Maybe; embedding?: Maybe; - epoch_date_downloaded?: Maybe; - epoch_date_published?: Maybe; - image_url?: Maybe; - language?: Maybe; - match: Scalars['Boolean']['output']; + match?: Maybe; matching_entities?: Maybe>>; matching_harm_keywords?: Maybe>>; matching_keywords?: Maybe>>; plain_text?: Maybe; similarity?: Maybe; - source_domain?: Maybe; text?: Maybe; - title?: Maybe; + title: Scalars['String']['output']; url: Scalars['String']['output']; }; @@ -165,6 +158,7 @@ export type CandidateClassification_SimilarityUpdateInput = { export type CandidateEmbedding = { __typename?: 'CandidateEmbedding'; from_text_hash?: Maybe; + source?: Maybe; vector?: Maybe>>; }; @@ -173,6 +167,17 @@ export type CandidateEmbeddingInsertInput = { vector?: InputMaybe>>; }; +export type CandidateEmbeddingInsertType = { + source?: InputMaybe; + vector?: InputMaybe>>; +}; + +export type CandidateEmbeddingObjectFilterType = { + opr?: InputMaybe; + source?: InputMaybe; + vector?: InputMaybe; +}; + export type CandidateEmbeddingQueryInput = { AND?: InputMaybe>; OR?: InputMaybe>; @@ -191,6 +196,17 @@ export type CandidateEmbeddingQueryInput = { vector_nin?: InputMaybe>>; }; +export type CandidateEmbeddingSetObjectType = { + /** If set to true, the object would be overwriten entirely, including fields that are not specified. Non-null validation rules will apply. Once set to true, any child object will overwriten invariably of the value set to this field. */ + _OVERWRITE?: InputMaybe; + source?: InputMaybe; + vector?: InputMaybe>>; +}; + +export type CandidateEmbeddingSortType = { + source?: InputMaybe; +}; + export type CandidateEmbeddingUpdateInput = { from_text_hash?: InputMaybe; from_text_hash_unset?: InputMaybe; @@ -198,178 +214,58 @@ export type CandidateEmbeddingUpdateInput = { vector_unset?: InputMaybe; }; -export type CandidateInsertInput = { +export type CandidateFilterType = { + AND?: InputMaybe>>; + NOR?: InputMaybe>>; + OR?: InputMaybe>>; + _id?: InputMaybe; + created_at?: InputMaybe; + date_published?: InputMaybe; + dismissed?: InputMaybe; + embedding?: InputMaybe; + match?: InputMaybe; + matching_entities?: InputMaybe; + matching_harm_keywords?: InputMaybe; + matching_keywords?: InputMaybe; + plain_text?: InputMaybe; + similarity?: InputMaybe; + text?: InputMaybe; + title?: InputMaybe; + url?: InputMaybe; +}; + +export type CandidateInsertType = { _id?: InputMaybe; - authors?: InputMaybe>>; - classification_similarity?: InputMaybe>>; - date_downloaded?: InputMaybe; - date_published?: InputMaybe; + created_at?: InputMaybe; + date_published: Scalars['String']['input']; dismissed?: InputMaybe; - embedding?: InputMaybe; - epoch_date_downloaded?: InputMaybe; - epoch_date_published?: InputMaybe; - image_url?: InputMaybe; - language?: InputMaybe; - match: Scalars['Boolean']['input']; + embedding?: InputMaybe; + match?: InputMaybe; matching_entities?: InputMaybe>>; matching_harm_keywords?: InputMaybe>>; matching_keywords?: InputMaybe>>; plain_text?: InputMaybe; similarity?: InputMaybe; - source_domain?: InputMaybe; text?: InputMaybe; - title?: InputMaybe; + title: Scalars['String']['input']; url: Scalars['String']['input']; }; -export type CandidateQueryInput = { - AND?: InputMaybe>; - OR?: InputMaybe>; +export type CandidateSetType = { _id?: InputMaybe; - _id_exists?: InputMaybe; - _id_gt?: InputMaybe; - _id_gte?: InputMaybe; - _id_in?: InputMaybe>>; - _id_lt?: InputMaybe; - _id_lte?: InputMaybe; - _id_ne?: InputMaybe; - _id_nin?: InputMaybe>>; - authors?: InputMaybe>>; - authors_exists?: InputMaybe; - authors_in?: InputMaybe>>; - authors_nin?: InputMaybe>>; - classification_similarity?: InputMaybe>>; - classification_similarity_exists?: InputMaybe; - classification_similarity_in?: InputMaybe>>; - classification_similarity_nin?: InputMaybe>>; - date_downloaded?: InputMaybe; - date_downloaded_exists?: InputMaybe; - date_downloaded_gt?: InputMaybe; - date_downloaded_gte?: InputMaybe; - date_downloaded_in?: InputMaybe>>; - date_downloaded_lt?: InputMaybe; - date_downloaded_lte?: InputMaybe; - date_downloaded_ne?: InputMaybe; - date_downloaded_nin?: InputMaybe>>; + created_at?: InputMaybe; date_published?: InputMaybe; - date_published_exists?: InputMaybe; - date_published_gt?: InputMaybe; - date_published_gte?: InputMaybe; - date_published_in?: InputMaybe>>; - date_published_lt?: InputMaybe; - date_published_lte?: InputMaybe; - date_published_ne?: InputMaybe; - date_published_nin?: InputMaybe>>; dismissed?: InputMaybe; - dismissed_exists?: InputMaybe; - dismissed_ne?: InputMaybe; - embedding?: InputMaybe; - embedding_exists?: InputMaybe; - epoch_date_downloaded?: InputMaybe; - epoch_date_downloaded_exists?: InputMaybe; - epoch_date_downloaded_gt?: InputMaybe; - epoch_date_downloaded_gte?: InputMaybe; - epoch_date_downloaded_in?: InputMaybe>>; - epoch_date_downloaded_lt?: InputMaybe; - epoch_date_downloaded_lte?: InputMaybe; - epoch_date_downloaded_ne?: InputMaybe; - epoch_date_downloaded_nin?: InputMaybe>>; - epoch_date_published?: InputMaybe; - epoch_date_published_exists?: InputMaybe; - epoch_date_published_gt?: InputMaybe; - epoch_date_published_gte?: InputMaybe; - epoch_date_published_in?: InputMaybe>>; - epoch_date_published_lt?: InputMaybe; - epoch_date_published_lte?: InputMaybe; - epoch_date_published_ne?: InputMaybe; - epoch_date_published_nin?: InputMaybe>>; - image_url?: InputMaybe; - image_url_exists?: InputMaybe; - image_url_gt?: InputMaybe; - image_url_gte?: InputMaybe; - image_url_in?: InputMaybe>>; - image_url_lt?: InputMaybe; - image_url_lte?: InputMaybe; - image_url_ne?: InputMaybe; - image_url_nin?: InputMaybe>>; - language?: InputMaybe; - language_exists?: InputMaybe; - language_gt?: InputMaybe; - language_gte?: InputMaybe; - language_in?: InputMaybe>>; - language_lt?: InputMaybe; - language_lte?: InputMaybe; - language_ne?: InputMaybe; - language_nin?: InputMaybe>>; + embedding?: InputMaybe; match?: InputMaybe; - match_exists?: InputMaybe; - match_ne?: InputMaybe; matching_entities?: InputMaybe>>; - matching_entities_exists?: InputMaybe; - matching_entities_in?: InputMaybe>>; - matching_entities_nin?: InputMaybe>>; matching_harm_keywords?: InputMaybe>>; - matching_harm_keywords_exists?: InputMaybe; - matching_harm_keywords_in?: InputMaybe>>; - matching_harm_keywords_nin?: InputMaybe>>; matching_keywords?: InputMaybe>>; - matching_keywords_exists?: InputMaybe; - matching_keywords_in?: InputMaybe>>; - matching_keywords_nin?: InputMaybe>>; plain_text?: InputMaybe; - plain_text_exists?: InputMaybe; - plain_text_gt?: InputMaybe; - plain_text_gte?: InputMaybe; - plain_text_in?: InputMaybe>>; - plain_text_lt?: InputMaybe; - plain_text_lte?: InputMaybe; - plain_text_ne?: InputMaybe; - plain_text_nin?: InputMaybe>>; similarity?: InputMaybe; - similarity_exists?: InputMaybe; - similarity_gt?: InputMaybe; - similarity_gte?: InputMaybe; - similarity_in?: InputMaybe>>; - similarity_lt?: InputMaybe; - similarity_lte?: InputMaybe; - similarity_ne?: InputMaybe; - similarity_nin?: InputMaybe>>; - source_domain?: InputMaybe; - source_domain_exists?: InputMaybe; - source_domain_gt?: InputMaybe; - source_domain_gte?: InputMaybe; - source_domain_in?: InputMaybe>>; - source_domain_lt?: InputMaybe; - source_domain_lte?: InputMaybe; - source_domain_ne?: InputMaybe; - source_domain_nin?: InputMaybe>>; text?: InputMaybe; - text_exists?: InputMaybe; - text_gt?: InputMaybe; - text_gte?: InputMaybe; - text_in?: InputMaybe>>; - text_lt?: InputMaybe; - text_lte?: InputMaybe; - text_ne?: InputMaybe; - text_nin?: InputMaybe>>; title?: InputMaybe; - title_exists?: InputMaybe; - title_gt?: InputMaybe; - title_gte?: InputMaybe; - title_in?: InputMaybe>>; - title_lt?: InputMaybe; - title_lte?: InputMaybe; - title_ne?: InputMaybe; - title_nin?: InputMaybe>>; url?: InputMaybe; - url_exists?: InputMaybe; - url_gt?: InputMaybe; - url_gte?: InputMaybe; - url_in?: InputMaybe>>; - url_lt?: InputMaybe; - url_lte?: InputMaybe; - url_ne?: InputMaybe; - url_nin?: InputMaybe>>; }; export enum CandidateSortByInput { @@ -401,52 +297,22 @@ export enum CandidateSortByInput { IdDesc = '_ID_DESC' } -export type CandidateUpdateInput = { - _id?: InputMaybe; - _id_unset?: InputMaybe; - authors?: InputMaybe>>; - authors_unset?: InputMaybe; - classification_similarity?: InputMaybe>>; - classification_similarity_unset?: InputMaybe; - date_downloaded?: InputMaybe; - date_downloaded_unset?: InputMaybe; - date_published?: InputMaybe; - date_published_unset?: InputMaybe; - dismissed?: InputMaybe; - dismissed_unset?: InputMaybe; - embedding?: InputMaybe; - embedding_unset?: InputMaybe; - epoch_date_downloaded?: InputMaybe; - epoch_date_downloaded_inc?: InputMaybe; - epoch_date_downloaded_unset?: InputMaybe; - epoch_date_published?: InputMaybe; - epoch_date_published_inc?: InputMaybe; - epoch_date_published_unset?: InputMaybe; - image_url?: InputMaybe; - image_url_unset?: InputMaybe; - language?: InputMaybe; - language_unset?: InputMaybe; - match?: InputMaybe; - match_unset?: InputMaybe; - matching_entities?: InputMaybe>>; - matching_entities_unset?: InputMaybe; - matching_harm_keywords?: InputMaybe>>; - matching_harm_keywords_unset?: InputMaybe; - matching_keywords?: InputMaybe>>; - matching_keywords_unset?: InputMaybe; - plain_text?: InputMaybe; - plain_text_unset?: InputMaybe; - similarity?: InputMaybe; - similarity_inc?: InputMaybe; - similarity_unset?: InputMaybe; - source_domain?: InputMaybe; - source_domain_unset?: InputMaybe; - text?: InputMaybe; - text_unset?: InputMaybe; - title?: InputMaybe; - title_unset?: InputMaybe; - url?: InputMaybe; - url_unset?: InputMaybe; +export type CandidateSortType = { + _id?: InputMaybe; + created_at?: InputMaybe; + date_published?: InputMaybe; + dismissed?: InputMaybe; + embedding?: InputMaybe; + match?: InputMaybe; + plain_text?: InputMaybe; + similarity?: InputMaybe; + text?: InputMaybe; + title?: InputMaybe; + url?: InputMaybe; +}; + +export type CandidateUpdateType = { + set?: InputMaybe; }; export type Checklist = { @@ -943,8 +809,10 @@ export type CreateVariantInputVariant = { export type CreateVariantPayload = { __typename?: 'CreateVariantPayload'; - incident_id?: Maybe; - report_number?: Maybe; + /** The unique identifier for the incident. */ + incident_id: Scalars['Int']['output']; + /** The unique report number associated with the incident. */ + report_number: Scalars['Int']['output']; }; /** Filter type for DateTime scalar */ @@ -1277,15 +1145,16 @@ export type History_Incident = { editor_notes?: Maybe; editor_similar_incidents?: Maybe>>; editors: Array>; - embedding?: Maybe; + embedding?: Maybe; epoch_date_modified?: Maybe; flagged_dissimilar_incidents?: Maybe>>; + implicated_systems?: Maybe>>; incident_id: Scalars['Int']['output']; modifiedBy?: Maybe; - nlp_similar_incidents?: Maybe>>; + nlp_similar_incidents?: Maybe>>; reports: Array>; title: Scalars['String']['output']; - tsne?: Maybe; + tsne?: Maybe; }; export type History_IncidentEmbedding = { @@ -1319,26 +1188,27 @@ export type History_IncidentEmbeddingUpdateInput = { vector_unset?: InputMaybe; }; -export type History_IncidentInsertInput = { - AllegedDeployerOfAISystem?: InputMaybe>>; - AllegedDeveloperOfAISystem?: InputMaybe>>; - AllegedHarmedOrNearlyHarmedParties?: InputMaybe>>; - _id?: InputMaybe; - date: Scalars['String']['input']; - description?: InputMaybe; - editor_dissimilar_incidents?: InputMaybe>>; - editor_notes?: InputMaybe; - editor_similar_incidents?: InputMaybe>>; - editors: Array>; - embedding?: InputMaybe; - epoch_date_modified?: InputMaybe; - flagged_dissimilar_incidents?: InputMaybe>>; - incident_id: Scalars['Int']['input']; - modifiedBy?: InputMaybe; - nlp_similar_incidents?: InputMaybe>>; - reports: Array>; - title: Scalars['String']['input']; - tsne?: InputMaybe; +export type History_IncidentFilterType = { + AND?: InputMaybe>>; + NOR?: InputMaybe>>; + OR?: InputMaybe>>; + _id?: InputMaybe; + date?: InputMaybe; + description?: InputMaybe; + editor_dissimilar_incidents?: InputMaybe; + editor_notes?: InputMaybe; + editor_similar_incidents?: InputMaybe; + editors?: InputMaybe; + embedding?: InputMaybe; + epoch_date_modified?: InputMaybe; + flagged_dissimilar_incidents?: InputMaybe; + implicated_systems?: InputMaybe; + incident_id?: InputMaybe; + modifiedBy?: InputMaybe; + nlp_similar_incidents?: InputMaybe; + reports?: InputMaybe; + title?: InputMaybe; + tsne?: InputMaybe; }; export type History_IncidentNlp_Similar_Incident = { @@ -1384,123 +1254,6 @@ export type History_IncidentNlp_Similar_IncidentUpdateInput = { similarity_unset?: InputMaybe; }; -export type History_IncidentQueryInput = { - AND?: InputMaybe>; - AllegedDeployerOfAISystem?: InputMaybe>>; - AllegedDeployerOfAISystem_exists?: InputMaybe; - AllegedDeployerOfAISystem_in?: InputMaybe>>; - AllegedDeployerOfAISystem_nin?: InputMaybe>>; - AllegedDeveloperOfAISystem?: InputMaybe>>; - AllegedDeveloperOfAISystem_exists?: InputMaybe; - AllegedDeveloperOfAISystem_in?: InputMaybe>>; - AllegedDeveloperOfAISystem_nin?: InputMaybe>>; - AllegedHarmedOrNearlyHarmedParties?: InputMaybe>>; - AllegedHarmedOrNearlyHarmedParties_exists?: InputMaybe; - AllegedHarmedOrNearlyHarmedParties_in?: InputMaybe>>; - AllegedHarmedOrNearlyHarmedParties_nin?: InputMaybe>>; - OR?: InputMaybe>; - _id?: InputMaybe; - _id_exists?: InputMaybe; - _id_gt?: InputMaybe; - _id_gte?: InputMaybe; - _id_in?: InputMaybe>>; - _id_lt?: InputMaybe; - _id_lte?: InputMaybe; - _id_ne?: InputMaybe; - _id_nin?: InputMaybe>>; - date?: InputMaybe; - date_exists?: InputMaybe; - date_gt?: InputMaybe; - date_gte?: InputMaybe; - date_in?: InputMaybe>>; - date_lt?: InputMaybe; - date_lte?: InputMaybe; - date_ne?: InputMaybe; - date_nin?: InputMaybe>>; - description?: InputMaybe; - description_exists?: InputMaybe; - description_gt?: InputMaybe; - description_gte?: InputMaybe; - description_in?: InputMaybe>>; - description_lt?: InputMaybe; - description_lte?: InputMaybe; - description_ne?: InputMaybe; - description_nin?: InputMaybe>>; - editor_dissimilar_incidents?: InputMaybe>>; - editor_dissimilar_incidents_exists?: InputMaybe; - editor_dissimilar_incidents_in?: InputMaybe>>; - editor_dissimilar_incidents_nin?: InputMaybe>>; - editor_notes?: InputMaybe; - editor_notes_exists?: InputMaybe; - editor_notes_gt?: InputMaybe; - editor_notes_gte?: InputMaybe; - editor_notes_in?: InputMaybe>>; - editor_notes_lt?: InputMaybe; - editor_notes_lte?: InputMaybe; - editor_notes_ne?: InputMaybe; - editor_notes_nin?: InputMaybe>>; - editor_similar_incidents?: InputMaybe>>; - editor_similar_incidents_exists?: InputMaybe; - editor_similar_incidents_in?: InputMaybe>>; - editor_similar_incidents_nin?: InputMaybe>>; - editors?: InputMaybe>>; - editors_exists?: InputMaybe; - editors_in?: InputMaybe>>; - editors_nin?: InputMaybe>>; - embedding?: InputMaybe; - embedding_exists?: InputMaybe; - epoch_date_modified?: InputMaybe; - epoch_date_modified_exists?: InputMaybe; - epoch_date_modified_gt?: InputMaybe; - epoch_date_modified_gte?: InputMaybe; - epoch_date_modified_in?: InputMaybe>>; - epoch_date_modified_lt?: InputMaybe; - epoch_date_modified_lte?: InputMaybe; - epoch_date_modified_ne?: InputMaybe; - epoch_date_modified_nin?: InputMaybe>>; - flagged_dissimilar_incidents?: InputMaybe>>; - flagged_dissimilar_incidents_exists?: InputMaybe; - flagged_dissimilar_incidents_in?: InputMaybe>>; - flagged_dissimilar_incidents_nin?: InputMaybe>>; - incident_id?: InputMaybe; - incident_id_exists?: InputMaybe; - incident_id_gt?: InputMaybe; - incident_id_gte?: InputMaybe; - incident_id_in?: InputMaybe>>; - incident_id_lt?: InputMaybe; - incident_id_lte?: InputMaybe; - incident_id_ne?: InputMaybe; - incident_id_nin?: InputMaybe>>; - modifiedBy?: InputMaybe; - modifiedBy_exists?: InputMaybe; - modifiedBy_gt?: InputMaybe; - modifiedBy_gte?: InputMaybe; - modifiedBy_in?: InputMaybe>>; - modifiedBy_lt?: InputMaybe; - modifiedBy_lte?: InputMaybe; - modifiedBy_ne?: InputMaybe; - modifiedBy_nin?: InputMaybe>>; - nlp_similar_incidents?: InputMaybe>>; - nlp_similar_incidents_exists?: InputMaybe; - nlp_similar_incidents_in?: InputMaybe>>; - nlp_similar_incidents_nin?: InputMaybe>>; - reports?: InputMaybe>>; - reports_exists?: InputMaybe; - reports_in?: InputMaybe>>; - reports_nin?: InputMaybe>>; - title?: InputMaybe; - title_exists?: InputMaybe; - title_gt?: InputMaybe; - title_gte?: InputMaybe; - title_in?: InputMaybe>>; - title_lt?: InputMaybe; - title_lte?: InputMaybe; - title_ne?: InputMaybe; - title_nin?: InputMaybe>>; - tsne?: InputMaybe; - tsne_exists?: InputMaybe; -}; - export enum History_IncidentSortByInput { DateAsc = 'DATE_ASC', DateDesc = 'DATE_DESC', @@ -1520,6 +1273,19 @@ export enum History_IncidentSortByInput { IdDesc = '_ID_DESC' } +export type History_IncidentSortType = { + _id?: InputMaybe; + date?: InputMaybe; + description?: InputMaybe; + editor_notes?: InputMaybe; + embedding?: InputMaybe; + epoch_date_modified?: InputMaybe; + incident_id?: InputMaybe; + modifiedBy?: InputMaybe; + title?: InputMaybe; + tsne?: InputMaybe; +}; + export type History_IncidentTsne = { __typename?: 'History_incidentTsne'; x?: Maybe; @@ -1563,80 +1329,37 @@ export type History_IncidentTsneUpdateInput = { y_unset?: InputMaybe; }; -export type History_IncidentUpdateInput = { - AllegedDeployerOfAISystem?: InputMaybe>>; - AllegedDeployerOfAISystem_unset?: InputMaybe; - AllegedDeveloperOfAISystem?: InputMaybe>>; - AllegedDeveloperOfAISystem_unset?: InputMaybe; - AllegedHarmedOrNearlyHarmedParties?: InputMaybe>>; - AllegedHarmedOrNearlyHarmedParties_unset?: InputMaybe; - _id?: InputMaybe; - _id_unset?: InputMaybe; - date?: InputMaybe; - date_unset?: InputMaybe; - description?: InputMaybe; - description_unset?: InputMaybe; - editor_dissimilar_incidents?: InputMaybe>>; - editor_dissimilar_incidents_unset?: InputMaybe; - editor_notes?: InputMaybe; - editor_notes_unset?: InputMaybe; - editor_similar_incidents?: InputMaybe>>; - editor_similar_incidents_unset?: InputMaybe; - editors?: InputMaybe>>; - editors_unset?: InputMaybe; - embedding?: InputMaybe; - embedding_unset?: InputMaybe; - epoch_date_modified?: InputMaybe; - epoch_date_modified_inc?: InputMaybe; - epoch_date_modified_unset?: InputMaybe; - flagged_dissimilar_incidents?: InputMaybe>>; - flagged_dissimilar_incidents_unset?: InputMaybe; - incident_id?: InputMaybe; - incident_id_inc?: InputMaybe; - incident_id_unset?: InputMaybe; - modifiedBy?: InputMaybe; - modifiedBy_unset?: InputMaybe; - nlp_similar_incidents?: InputMaybe>>; - nlp_similar_incidents_unset?: InputMaybe; - reports?: InputMaybe>>; - reports_unset?: InputMaybe; - title?: InputMaybe; - title_unset?: InputMaybe; - tsne?: InputMaybe; - tsne_unset?: InputMaybe; -}; - export type History_Report = { __typename?: 'History_report'; _id?: Maybe; - authors: Array>; - cloudinary_id: Scalars['String']['output']; + authors?: Maybe>>; + cloudinary_id?: Maybe; date_downloaded: Scalars['DateTime']['output']; date_modified: Scalars['DateTime']['output']; date_published: Scalars['DateTime']['output']; date_submitted: Scalars['DateTime']['output']; description?: Maybe; editor_notes?: Maybe; - embedding?: Maybe; - epoch_date_downloaded: Scalars['Int']['output']; - epoch_date_modified: Scalars['Int']['output']; - epoch_date_published: Scalars['Int']['output']; - epoch_date_submitted: Scalars['Int']['output']; + embedding?: Maybe; + epoch_date_downloaded?: Maybe; + epoch_date_modified?: Maybe; + epoch_date_published?: Maybe; + epoch_date_submitted?: Maybe; flag?: Maybe; - image_url: Scalars['String']['output']; + image_url?: Maybe; inputs_outputs?: Maybe>>; is_incident_report?: Maybe; - language: Scalars['String']['output']; + language?: Maybe; modifiedBy?: Maybe; - plain_text: Scalars['String']['output']; + plain_text?: Maybe; quiet?: Maybe; - report_number: Scalars['Int']['output']; - source_domain: Scalars['String']['output']; - submitters: Array>; - tags: Array>; - text: Scalars['String']['output']; - title: Scalars['String']['output']; - url: Scalars['String']['output']; + report_number?: Maybe; + source_domain?: Maybe; + submitters?: Maybe>>; + tags?: Maybe>>; + text?: Maybe; + title?: Maybe; + url?: Maybe; user?: Maybe; }; @@ -1663,280 +1386,53 @@ export type History_ReportEmbeddingQueryInput = { from_text_hash_lte?: InputMaybe; from_text_hash_ne?: InputMaybe; from_text_hash_nin?: InputMaybe>>; - vector?: InputMaybe>>; - vector_exists?: InputMaybe; - vector_in?: InputMaybe>>; - vector_nin?: InputMaybe>>; -}; - -export type History_ReportEmbeddingUpdateInput = { - from_text_hash?: InputMaybe; - from_text_hash_unset?: InputMaybe; - vector?: InputMaybe>>; - vector_unset?: InputMaybe; -}; - -export type History_ReportInsertInput = { - _id?: InputMaybe; - authors: Array>; - cloudinary_id: Scalars['String']['input']; - date_downloaded: Scalars['DateTime']['input']; - date_modified: Scalars['DateTime']['input']; - date_published: Scalars['DateTime']['input']; - date_submitted: Scalars['DateTime']['input']; - description?: InputMaybe; - editor_notes?: InputMaybe; - embedding?: InputMaybe; - epoch_date_downloaded: Scalars['Int']['input']; - epoch_date_modified: Scalars['Int']['input']; - epoch_date_published: Scalars['Int']['input']; - epoch_date_submitted: Scalars['Int']['input']; - flag?: InputMaybe; - image_url: Scalars['String']['input']; - inputs_outputs?: InputMaybe>>; - is_incident_report?: InputMaybe; - language: Scalars['String']['input']; - modifiedBy?: InputMaybe; - plain_text: Scalars['String']['input']; - quiet?: InputMaybe; - report_number: Scalars['Int']['input']; - source_domain: Scalars['String']['input']; - submitters: Array>; - tags: Array>; - text: Scalars['String']['input']; - title: Scalars['String']['input']; - url: Scalars['String']['input']; - user?: InputMaybe; + vector?: InputMaybe>>; + vector_exists?: InputMaybe; + vector_in?: InputMaybe>>; + vector_nin?: InputMaybe>>; }; -export type History_ReportQueryInput = { - AND?: InputMaybe>; - OR?: InputMaybe>; - _id?: InputMaybe; - _id_exists?: InputMaybe; - _id_gt?: InputMaybe; - _id_gte?: InputMaybe; - _id_in?: InputMaybe>>; - _id_lt?: InputMaybe; - _id_lte?: InputMaybe; - _id_ne?: InputMaybe; - _id_nin?: InputMaybe>>; - authors?: InputMaybe>>; - authors_exists?: InputMaybe; - authors_in?: InputMaybe>>; - authors_nin?: InputMaybe>>; - cloudinary_id?: InputMaybe; - cloudinary_id_exists?: InputMaybe; - cloudinary_id_gt?: InputMaybe; - cloudinary_id_gte?: InputMaybe; - cloudinary_id_in?: InputMaybe>>; - cloudinary_id_lt?: InputMaybe; - cloudinary_id_lte?: InputMaybe; - cloudinary_id_ne?: InputMaybe; - cloudinary_id_nin?: InputMaybe>>; - date_downloaded?: InputMaybe; - date_downloaded_exists?: InputMaybe; - date_downloaded_gt?: InputMaybe; - date_downloaded_gte?: InputMaybe; - date_downloaded_in?: InputMaybe>>; - date_downloaded_lt?: InputMaybe; - date_downloaded_lte?: InputMaybe; - date_downloaded_ne?: InputMaybe; - date_downloaded_nin?: InputMaybe>>; - date_modified?: InputMaybe; - date_modified_exists?: InputMaybe; - date_modified_gt?: InputMaybe; - date_modified_gte?: InputMaybe; - date_modified_in?: InputMaybe>>; - date_modified_lt?: InputMaybe; - date_modified_lte?: InputMaybe; - date_modified_ne?: InputMaybe; - date_modified_nin?: InputMaybe>>; - date_published?: InputMaybe; - date_published_exists?: InputMaybe; - date_published_gt?: InputMaybe; - date_published_gte?: InputMaybe; - date_published_in?: InputMaybe>>; - date_published_lt?: InputMaybe; - date_published_lte?: InputMaybe; - date_published_ne?: InputMaybe; - date_published_nin?: InputMaybe>>; - date_submitted?: InputMaybe; - date_submitted_exists?: InputMaybe; - date_submitted_gt?: InputMaybe; - date_submitted_gte?: InputMaybe; - date_submitted_in?: InputMaybe>>; - date_submitted_lt?: InputMaybe; - date_submitted_lte?: InputMaybe; - date_submitted_ne?: InputMaybe; - date_submitted_nin?: InputMaybe>>; - description?: InputMaybe; - description_exists?: InputMaybe; - description_gt?: InputMaybe; - description_gte?: InputMaybe; - description_in?: InputMaybe>>; - description_lt?: InputMaybe; - description_lte?: InputMaybe; - description_ne?: InputMaybe; - description_nin?: InputMaybe>>; - editor_notes?: InputMaybe; - editor_notes_exists?: InputMaybe; - editor_notes_gt?: InputMaybe; - editor_notes_gte?: InputMaybe; - editor_notes_in?: InputMaybe>>; - editor_notes_lt?: InputMaybe; - editor_notes_lte?: InputMaybe; - editor_notes_ne?: InputMaybe; - editor_notes_nin?: InputMaybe>>; - embedding?: InputMaybe; - embedding_exists?: InputMaybe; - epoch_date_downloaded?: InputMaybe; - epoch_date_downloaded_exists?: InputMaybe; - epoch_date_downloaded_gt?: InputMaybe; - epoch_date_downloaded_gte?: InputMaybe; - epoch_date_downloaded_in?: InputMaybe>>; - epoch_date_downloaded_lt?: InputMaybe; - epoch_date_downloaded_lte?: InputMaybe; - epoch_date_downloaded_ne?: InputMaybe; - epoch_date_downloaded_nin?: InputMaybe>>; - epoch_date_modified?: InputMaybe; - epoch_date_modified_exists?: InputMaybe; - epoch_date_modified_gt?: InputMaybe; - epoch_date_modified_gte?: InputMaybe; - epoch_date_modified_in?: InputMaybe>>; - epoch_date_modified_lt?: InputMaybe; - epoch_date_modified_lte?: InputMaybe; - epoch_date_modified_ne?: InputMaybe; - epoch_date_modified_nin?: InputMaybe>>; - epoch_date_published?: InputMaybe; - epoch_date_published_exists?: InputMaybe; - epoch_date_published_gt?: InputMaybe; - epoch_date_published_gte?: InputMaybe; - epoch_date_published_in?: InputMaybe>>; - epoch_date_published_lt?: InputMaybe; - epoch_date_published_lte?: InputMaybe; - epoch_date_published_ne?: InputMaybe; - epoch_date_published_nin?: InputMaybe>>; - epoch_date_submitted?: InputMaybe; - epoch_date_submitted_exists?: InputMaybe; - epoch_date_submitted_gt?: InputMaybe; - epoch_date_submitted_gte?: InputMaybe; - epoch_date_submitted_in?: InputMaybe>>; - epoch_date_submitted_lt?: InputMaybe; - epoch_date_submitted_lte?: InputMaybe; - epoch_date_submitted_ne?: InputMaybe; - epoch_date_submitted_nin?: InputMaybe>>; - flag?: InputMaybe; - flag_exists?: InputMaybe; - flag_ne?: InputMaybe; - image_url?: InputMaybe; - image_url_exists?: InputMaybe; - image_url_gt?: InputMaybe; - image_url_gte?: InputMaybe; - image_url_in?: InputMaybe>>; - image_url_lt?: InputMaybe; - image_url_lte?: InputMaybe; - image_url_ne?: InputMaybe; - image_url_nin?: InputMaybe>>; - inputs_outputs?: InputMaybe>>; - inputs_outputs_exists?: InputMaybe; - inputs_outputs_in?: InputMaybe>>; - inputs_outputs_nin?: InputMaybe>>; - is_incident_report?: InputMaybe; - is_incident_report_exists?: InputMaybe; - is_incident_report_ne?: InputMaybe; - language?: InputMaybe; - language_exists?: InputMaybe; - language_gt?: InputMaybe; - language_gte?: InputMaybe; - language_in?: InputMaybe>>; - language_lt?: InputMaybe; - language_lte?: InputMaybe; - language_ne?: InputMaybe; - language_nin?: InputMaybe>>; - modifiedBy?: InputMaybe; - modifiedBy_exists?: InputMaybe; - modifiedBy_gt?: InputMaybe; - modifiedBy_gte?: InputMaybe; - modifiedBy_in?: InputMaybe>>; - modifiedBy_lt?: InputMaybe; - modifiedBy_lte?: InputMaybe; - modifiedBy_ne?: InputMaybe; - modifiedBy_nin?: InputMaybe>>; - plain_text?: InputMaybe; - plain_text_exists?: InputMaybe; - plain_text_gt?: InputMaybe; - plain_text_gte?: InputMaybe; - plain_text_in?: InputMaybe>>; - plain_text_lt?: InputMaybe; - plain_text_lte?: InputMaybe; - plain_text_ne?: InputMaybe; - plain_text_nin?: InputMaybe>>; - quiet?: InputMaybe; - quiet_exists?: InputMaybe; - quiet_ne?: InputMaybe; - report_number?: InputMaybe; - report_number_exists?: InputMaybe; - report_number_gt?: InputMaybe; - report_number_gte?: InputMaybe; - report_number_in?: InputMaybe>>; - report_number_lt?: InputMaybe; - report_number_lte?: InputMaybe; - report_number_ne?: InputMaybe; - report_number_nin?: InputMaybe>>; - source_domain?: InputMaybe; - source_domain_exists?: InputMaybe; - source_domain_gt?: InputMaybe; - source_domain_gte?: InputMaybe; - source_domain_in?: InputMaybe>>; - source_domain_lt?: InputMaybe; - source_domain_lte?: InputMaybe; - source_domain_ne?: InputMaybe; - source_domain_nin?: InputMaybe>>; - submitters?: InputMaybe>>; - submitters_exists?: InputMaybe; - submitters_in?: InputMaybe>>; - submitters_nin?: InputMaybe>>; - tags?: InputMaybe>>; - tags_exists?: InputMaybe; - tags_in?: InputMaybe>>; - tags_nin?: InputMaybe>>; - text?: InputMaybe; - text_exists?: InputMaybe; - text_gt?: InputMaybe; - text_gte?: InputMaybe; - text_in?: InputMaybe>>; - text_lt?: InputMaybe; - text_lte?: InputMaybe; - text_ne?: InputMaybe; - text_nin?: InputMaybe>>; - title?: InputMaybe; - title_exists?: InputMaybe; - title_gt?: InputMaybe; - title_gte?: InputMaybe; - title_in?: InputMaybe>>; - title_lt?: InputMaybe; - title_lte?: InputMaybe; - title_ne?: InputMaybe; - title_nin?: InputMaybe>>; - url?: InputMaybe; - url_exists?: InputMaybe; - url_gt?: InputMaybe; - url_gte?: InputMaybe; - url_in?: InputMaybe>>; - url_lt?: InputMaybe; - url_lte?: InputMaybe; - url_ne?: InputMaybe; - url_nin?: InputMaybe>>; - user?: InputMaybe; - user_exists?: InputMaybe; - user_gt?: InputMaybe; - user_gte?: InputMaybe; - user_in?: InputMaybe>>; - user_lt?: InputMaybe; - user_lte?: InputMaybe; - user_ne?: InputMaybe; - user_nin?: InputMaybe>>; +export type History_ReportEmbeddingUpdateInput = { + from_text_hash?: InputMaybe; + from_text_hash_unset?: InputMaybe; + vector?: InputMaybe>>; + vector_unset?: InputMaybe; +}; + +export type History_ReportFilterType = { + AND?: InputMaybe>>; + NOR?: InputMaybe>>; + OR?: InputMaybe>>; + _id?: InputMaybe; + authors?: InputMaybe; + cloudinary_id?: InputMaybe; + date_downloaded?: InputMaybe; + date_modified?: InputMaybe; + date_published?: InputMaybe; + date_submitted?: InputMaybe; + description?: InputMaybe; + editor_notes?: InputMaybe; + embedding?: InputMaybe; + epoch_date_downloaded?: InputMaybe; + epoch_date_modified?: InputMaybe; + epoch_date_published?: InputMaybe; + epoch_date_submitted?: InputMaybe; + flag?: InputMaybe; + image_url?: InputMaybe; + inputs_outputs?: InputMaybe; + is_incident_report?: InputMaybe; + language?: InputMaybe; + modifiedBy?: InputMaybe; + plain_text?: InputMaybe; + quiet?: InputMaybe; + report_number?: InputMaybe; + source_domain?: InputMaybe; + submitters?: InputMaybe; + tags?: InputMaybe; + text?: InputMaybe; + title?: InputMaybe; + url?: InputMaybe; + user?: InputMaybe; }; export enum History_ReportSortByInput { @@ -1986,72 +1482,33 @@ export enum History_ReportSortByInput { IdDesc = '_ID_DESC' } -export type History_ReportUpdateInput = { - _id?: InputMaybe; - _id_unset?: InputMaybe; - authors?: InputMaybe>>; - authors_unset?: InputMaybe; - cloudinary_id?: InputMaybe; - cloudinary_id_unset?: InputMaybe; - date_downloaded?: InputMaybe; - date_downloaded_unset?: InputMaybe; - date_modified?: InputMaybe; - date_modified_unset?: InputMaybe; - date_published?: InputMaybe; - date_published_unset?: InputMaybe; - date_submitted?: InputMaybe; - date_submitted_unset?: InputMaybe; - description?: InputMaybe; - description_unset?: InputMaybe; - editor_notes?: InputMaybe; - editor_notes_unset?: InputMaybe; - embedding?: InputMaybe; - embedding_unset?: InputMaybe; - epoch_date_downloaded?: InputMaybe; - epoch_date_downloaded_inc?: InputMaybe; - epoch_date_downloaded_unset?: InputMaybe; - epoch_date_modified?: InputMaybe; - epoch_date_modified_inc?: InputMaybe; - epoch_date_modified_unset?: InputMaybe; - epoch_date_published?: InputMaybe; - epoch_date_published_inc?: InputMaybe; - epoch_date_published_unset?: InputMaybe; - epoch_date_submitted?: InputMaybe; - epoch_date_submitted_inc?: InputMaybe; - epoch_date_submitted_unset?: InputMaybe; - flag?: InputMaybe; - flag_unset?: InputMaybe; - image_url?: InputMaybe; - image_url_unset?: InputMaybe; - inputs_outputs?: InputMaybe>>; - inputs_outputs_unset?: InputMaybe; - is_incident_report?: InputMaybe; - is_incident_report_unset?: InputMaybe; - language?: InputMaybe; - language_unset?: InputMaybe; - modifiedBy?: InputMaybe; - modifiedBy_unset?: InputMaybe; - plain_text?: InputMaybe; - plain_text_unset?: InputMaybe; - quiet?: InputMaybe; - quiet_unset?: InputMaybe; - report_number?: InputMaybe; - report_number_inc?: InputMaybe; - report_number_unset?: InputMaybe; - source_domain?: InputMaybe; - source_domain_unset?: InputMaybe; - submitters?: InputMaybe>>; - submitters_unset?: InputMaybe; - tags?: InputMaybe>>; - tags_unset?: InputMaybe; - text?: InputMaybe; - text_unset?: InputMaybe; - title?: InputMaybe; - title_unset?: InputMaybe; - url?: InputMaybe; - url_unset?: InputMaybe; - user?: InputMaybe; - user_unset?: InputMaybe; +export type History_ReportSortType = { + _id?: InputMaybe; + cloudinary_id?: InputMaybe; + date_downloaded?: InputMaybe; + date_modified?: InputMaybe; + date_published?: InputMaybe; + date_submitted?: InputMaybe; + description?: InputMaybe; + editor_notes?: InputMaybe; + embedding?: InputMaybe; + epoch_date_downloaded?: InputMaybe; + epoch_date_modified?: InputMaybe; + epoch_date_published?: InputMaybe; + epoch_date_submitted?: InputMaybe; + flag?: InputMaybe; + image_url?: InputMaybe; + is_incident_report?: InputMaybe; + language?: InputMaybe; + modifiedBy?: InputMaybe; + plain_text?: InputMaybe; + quiet?: InputMaybe; + report_number?: InputMaybe; + source_domain?: InputMaybe; + text?: InputMaybe; + title?: InputMaybe; + url?: InputMaybe; + user?: InputMaybe; }; export type Incident = { @@ -2069,6 +1526,7 @@ export type Incident = { embedding?: Maybe; epoch_date_modified?: Maybe; flagged_dissimilar_incidents?: Maybe>>; + implicated_systems?: Maybe>>; incident_id: Scalars['Int']['output']; nlp_similar_incidents?: Maybe>>; reports?: Maybe>>; @@ -2175,6 +1633,7 @@ export type IncidentFilterType = { embedding?: InputMaybe; epoch_date_modified?: InputMaybe; flagged_dissimilar_incidents?: InputMaybe; + implicated_systems?: InputMaybe; incident_id?: InputMaybe; nlp_similar_incidents?: InputMaybe; reports?: InputMaybe; @@ -2182,6 +1641,14 @@ export type IncidentFilterType = { tsne?: InputMaybe; }; +export type IncidentImplicatedSystemsRelationInput = { + link?: InputMaybe>>; +}; + +export type IncidentImplicated_SystemsRelationInput = { + link: Array>; +}; + export type IncidentInsertType = { AllegedDeployerOfAISystem?: InputMaybe; AllegedDeveloperOfAISystem?: InputMaybe; @@ -2196,6 +1663,7 @@ export type IncidentInsertType = { embedding?: InputMaybe; epoch_date_modified?: InputMaybe; flagged_dissimilar_incidents: Array>; + implicated_systems?: InputMaybe; incident_id: Scalars['Int']['input']; nlp_similar_incidents?: InputMaybe>>; reports?: InputMaybe; @@ -2376,6 +1844,7 @@ export type IncidentSetType = { embedding?: InputMaybe; epoch_date_modified?: InputMaybe; flagged_dissimilar_incidents?: InputMaybe>>; + implicated_systems?: InputMaybe; incident_id?: InputMaybe; nlp_similar_incidents?: InputMaybe>>; reports?: InputMaybe; @@ -2663,8 +2132,6 @@ export type Mutation = { deleteOneCandidate?: Maybe; deleteOneChecklist?: Maybe; deleteOneDuplicate?: Maybe; - deleteOneHistory_incident?: Maybe; - deleteOneHistory_report?: Maybe; deleteOneNotification?: Maybe; deleteOneQuickadd?: Maybe; deleteOneReport?: Maybe; @@ -2682,8 +2149,6 @@ export type Mutation = { insertOneCandidate?: Maybe; insertOneChecklist?: Maybe; insertOneDuplicate?: Maybe; - insertOneHistory_incident?: Maybe; - insertOneHistory_report?: Maybe; insertOneIncident?: Maybe; insertOneNotification?: Maybe; insertOneQuickadd?: Maybe; @@ -2692,12 +2157,8 @@ export type Mutation = { linkReportsToIncidents?: Maybe>>; logIncidentHistory?: Maybe; logReportHistory?: Maybe; - processNotifications?: Maybe; promoteSubmissionToReport: PromoteSubmissionToReportPayload; - replaceOneCandidate?: Maybe; replaceOneChecklist?: Maybe; - replaceOneHistory_incident?: Maybe; - replaceOneHistory_report?: Maybe; replaceOneIncident?: Maybe; replaceOneNotification?: Maybe; replaceOneUser?: Maybe; @@ -2713,8 +2174,6 @@ export type Mutation = { updateOneChecklist?: Maybe; updateOneDuplicate?: Maybe; updateOneEntity?: Maybe; - updateOneHistory_incident?: Maybe; - updateOneHistory_report?: Maybe; updateOneIncident?: Maybe; updateOneNotification?: Maybe; updateOneQuickadd?: Maybe; @@ -2728,8 +2187,6 @@ export type Mutation = { upsertOneClassification?: Maybe; upsertOneDuplicate?: Maybe; upsertOneEntity?: Maybe; - upsertOneHistory_incident?: Maybe; - upsertOneHistory_report?: Maybe; upsertOneNotification?: Maybe; upsertOneQuickadd?: Maybe; upsertOneSubscription?: Maybe; @@ -2747,7 +2204,9 @@ export type MutationCreateVariantArgs = { export type MutationDeleteManyCandidatesArgs = { - query?: InputMaybe; + filter?: InputMaybe; + pagination?: InputMaybe; + sort?: InputMaybe; }; @@ -2763,16 +2222,6 @@ export type MutationDeleteManyDuplicatesArgs = { }; -export type MutationDeleteManyHistory_IncidentsArgs = { - query?: InputMaybe; -}; - - -export type MutationDeleteManyHistory_ReportsArgs = { - query?: InputMaybe; -}; - - export type MutationDeleteManyNotificationsArgs = { query?: InputMaybe; }; @@ -2793,7 +2242,9 @@ export type MutationDeleteManySubscriptionsArgs = { export type MutationDeleteOneCandidateArgs = { - query: CandidateQueryInput; + filter?: InputMaybe; + pagination?: InputMaybe; + sort?: InputMaybe; }; @@ -2809,16 +2260,6 @@ export type MutationDeleteOneDuplicateArgs = { }; -export type MutationDeleteOneHistory_IncidentArgs = { - query: History_IncidentQueryInput; -}; - - -export type MutationDeleteOneHistory_ReportArgs = { - query: History_ReportQueryInput; -}; - - export type MutationDeleteOneNotificationArgs = { query: NotificationQueryInput; }; @@ -2863,7 +2304,7 @@ export type MutationGetUserArgs = { export type MutationInsertManyCandidatesArgs = { - data: Array; + data: Array>; }; @@ -2877,16 +2318,6 @@ export type MutationInsertManyDuplicatesArgs = { }; -export type MutationInsertManyHistory_IncidentsArgs = { - data: Array; -}; - - -export type MutationInsertManyHistory_ReportsArgs = { - data: Array; -}; - - export type MutationInsertManyNotificationsArgs = { data: Array; }; @@ -2898,7 +2329,7 @@ export type MutationInsertManyQuickaddsArgs = { export type MutationInsertOneCandidateArgs = { - data: CandidateInsertInput; + data: CandidateInsertType; }; @@ -2912,16 +2343,6 @@ export type MutationInsertOneDuplicateArgs = { }; -export type MutationInsertOneHistory_IncidentArgs = { - data: History_IncidentInsertInput; -}; - - -export type MutationInsertOneHistory_ReportArgs = { - data: History_ReportInsertInput; -}; - - export type MutationInsertOneIncidentArgs = { data: IncidentInsertType; }; @@ -2952,45 +2373,17 @@ export type MutationLinkReportsToIncidentsArgs = { }; -export type MutationLogIncidentHistoryArgs = { - input?: InputMaybe; -}; - - -export type MutationLogReportHistoryArgs = { - input?: InputMaybe; -}; - - export type MutationPromoteSubmissionToReportArgs = { input: PromoteSubmissionToReportInput; }; -export type MutationReplaceOneCandidateArgs = { - data: CandidateInsertInput; - query?: InputMaybe; -}; - - export type MutationReplaceOneChecklistArgs = { data: ChecklistInsertInput; query?: InputMaybe; }; -export type MutationReplaceOneHistory_IncidentArgs = { - data: History_IncidentInsertInput; - query?: InputMaybe; -}; - - -export type MutationReplaceOneHistory_ReportArgs = { - data: History_ReportInsertInput; - query?: InputMaybe; -}; - - export type MutationReplaceOneIncidentArgs = { query?: InputMaybe; }; @@ -3008,8 +2401,8 @@ export type MutationReplaceOneUserArgs = { export type MutationUpdateManyCandidatesArgs = { - query?: InputMaybe; - set: CandidateUpdateInput; + filter: CandidateFilterType; + update: CandidateUpdateType; }; @@ -3025,18 +2418,6 @@ export type MutationUpdateManyDuplicatesArgs = { }; -export type MutationUpdateManyHistory_IncidentsArgs = { - query?: InputMaybe; - set: History_IncidentUpdateInput; -}; - - -export type MutationUpdateManyHistory_ReportsArgs = { - query?: InputMaybe; - set: History_ReportUpdateInput; -}; - - export type MutationUpdateManyIncidentsArgs = { filter: IncidentFilterType; update: IncidentUpdateType; @@ -3056,8 +2437,8 @@ export type MutationUpdateManyQuickaddsArgs = { export type MutationUpdateOneCandidateArgs = { - query?: InputMaybe; - set: CandidateUpdateInput; + filter: CandidateFilterType; + update: CandidateUpdateType; }; @@ -3079,18 +2460,6 @@ export type MutationUpdateOneEntityArgs = { }; -export type MutationUpdateOneHistory_IncidentArgs = { - query?: InputMaybe; - set: History_IncidentUpdateInput; -}; - - -export type MutationUpdateOneHistory_ReportArgs = { - query?: InputMaybe; - set: History_ReportUpdateInput; -}; - - export type MutationUpdateOneIncidentArgs = { filter: IncidentFilterType; update: IncidentUpdateType; @@ -3139,8 +2508,8 @@ export type MutationUpdateOneUserArgs = { export type MutationUpsertOneCandidateArgs = { - data: CandidateInsertInput; - query?: InputMaybe; + filter: CandidateFilterType; + update: CandidateInsertType; }; @@ -3168,18 +2537,6 @@ export type MutationUpsertOneEntityArgs = { }; -export type MutationUpsertOneHistory_IncidentArgs = { - data: History_IncidentInsertInput; - query?: InputMaybe; -}; - - -export type MutationUpsertOneHistory_ReportArgs = { - data: History_ReportInsertInput; - query?: InputMaybe; -}; - - export type MutationUpsertOneNotificationArgs = { data: NotificationInsertInput; query?: InputMaybe; @@ -3417,7 +2774,7 @@ export type Query = { /** Custom scalar for MongoDB ObjectID */ ObjectId?: Maybe; candidate?: Maybe; - candidates: Array>; + candidates?: Maybe>>; checklist?: Maybe; checklists: Array>; classification?: Maybe; @@ -3427,9 +2784,9 @@ export type Query = { entities?: Maybe>>; entity?: Maybe; history_incident?: Maybe; - history_incidents: Array>; + history_incidents?: Maybe>>; history_report?: Maybe; - history_reports: Array>; + history_reports?: Maybe>>; incident?: Maybe; incidents?: Maybe>>; notification?: Maybe; @@ -3451,14 +2808,16 @@ export type Query = { export type QueryCandidateArgs = { - query?: InputMaybe; + filter?: InputMaybe; + pagination?: InputMaybe; + sort?: InputMaybe; }; export type QueryCandidatesArgs = { - limit?: InputMaybe; - query?: InputMaybe; - sortBy?: InputMaybe; + filter?: InputMaybe; + pagination?: InputMaybe; + sort?: InputMaybe; }; @@ -3517,26 +2876,30 @@ export type QueryEntityArgs = { export type QueryHistory_IncidentArgs = { - query?: InputMaybe; + filter?: InputMaybe; + pagination?: InputMaybe; + sort?: InputMaybe; }; export type QueryHistory_IncidentsArgs = { - limit?: InputMaybe; - query?: InputMaybe; - sortBy?: InputMaybe; + filter?: InputMaybe; + pagination?: InputMaybe; + sort?: InputMaybe; }; export type QueryHistory_ReportArgs = { - query?: InputMaybe; + filter?: InputMaybe; + pagination?: InputMaybe; + sort?: InputMaybe; }; export type QueryHistory_ReportsArgs = { - limit?: InputMaybe; - query?: InputMaybe; - sortBy?: InputMaybe; + filter?: InputMaybe; + pagination?: InputMaybe; + sort?: InputMaybe; }; @@ -3985,12 +3348,6 @@ export type ReportSortType = { url?: InputMaybe; }; -export type ReportTranslation = { - __typename?: 'ReportTranslation'; - text?: Maybe; - title?: Maybe; -}; - export type ReportTranslations = { __typename?: 'ReportTranslations'; text?: Maybe; @@ -4032,6 +3389,7 @@ export type RisksPayloadPrecedent = { embedding?: Maybe; epoch_date_modified?: Maybe; flagged_dissimilar_incidents?: Maybe>>; + implicated_systems?: Maybe>>; incident_id?: Maybe; nlp_similar_incidents?: Maybe>>; reports?: Maybe>>; @@ -4198,6 +3556,7 @@ export type Submission = { epoch_date_modified?: Maybe; harmed_parties?: Maybe>>; image_url: Scalars['String']['output']; + implicated_systems?: Maybe>>; incident_date?: Maybe; incident_editors?: Maybe>>; incident_ids?: Maybe>>; @@ -4281,6 +3640,7 @@ export type SubmissionFilterType = { epoch_date_modified?: InputMaybe; harmed_parties?: InputMaybe; image_url?: InputMaybe; + implicated_systems?: InputMaybe; incident_date?: InputMaybe; incident_editors?: InputMaybe; incident_ids?: InputMaybe; @@ -4303,6 +3663,10 @@ export type SubmissionHarmed_PartiesRelationInput = { link: Array>; }; +export type SubmissionImplicated_SystemsRelationInput = { + link: Array>; +}; + export type SubmissionIncident_EditorsRelationInput = { link: Array>; }; @@ -4325,6 +3689,7 @@ export type SubmissionInsertType = { epoch_date_modified?: InputMaybe; harmed_parties?: InputMaybe; image_url: Scalars['String']['input']; + implicated_systems?: InputMaybe; incident_date?: InputMaybe; incident_editors?: InputMaybe; incident_ids?: InputMaybe>>; @@ -4404,6 +3769,7 @@ export type SubmissionSetType = { epoch_date_modified?: InputMaybe; harmed_parties?: InputMaybe; image_url?: InputMaybe; + implicated_systems?: InputMaybe; incident_date?: InputMaybe; incident_editors?: InputMaybe; incident_ids?: InputMaybe>>; @@ -5129,9 +4495,9 @@ export type User = { export type UserAdminDatum = { __typename?: 'UserAdminDatum'; - creationDate?: Maybe; - disabled?: Maybe; - email?: Maybe; + creationDate: Scalars['DateTime']['output']; + disabled: Scalars['Boolean']['output']; + email: Scalars['String']['output']; lastAuthenticationDate?: Maybe; }; @@ -5276,28 +4642,28 @@ export type FindIncidentQueryVariables = Exact<{ }>; -export type FindIncidentQuery = { __typename?: 'Query', incident?: { __typename?: 'Incident', incident_id: number, title: string, description?: string | null, date: string, editor_similar_incidents?: Array | null, editor_dissimilar_incidents?: Array | null, flagged_dissimilar_incidents?: Array | null, editor_notes?: string | null, editors: Array<{ __typename?: 'User', userId: string, first_name?: string | null, last_name?: string | null } | null>, AllegedDeployerOfAISystem?: Array<{ __typename?: 'Entity', entity_id: string, name: string } | null> | null, AllegedDeveloperOfAISystem?: Array<{ __typename?: 'Entity', entity_id: string, name: string } | null> | null, AllegedHarmedOrNearlyHarmedParties?: Array<{ __typename?: 'Entity', entity_id: string, name: string } | null> | null, nlp_similar_incidents?: Array<{ __typename?: 'IncidentNlp_similar_incident', incident_id?: number | null, similarity?: number | null } | null> | null, reports?: Array<{ __typename?: 'Report', report_number: number } | null> | null, embedding?: { __typename?: 'IncidentEmbedding', from_reports?: Array | null, vector?: Array | null } | null } | null }; +export type FindIncidentQuery = { __typename?: 'Query', incident?: { __typename?: 'Incident', incident_id: number, title: string, description?: string | null, date: string, editor_similar_incidents?: Array | null, editor_dissimilar_incidents?: Array | null, flagged_dissimilar_incidents?: Array | null, editor_notes?: string | null, editors: Array<{ __typename?: 'User', userId: string, first_name?: string | null, last_name?: string | null } | null>, AllegedDeployerOfAISystem?: Array<{ __typename?: 'Entity', entity_id: string, name: string } | null> | null, AllegedDeveloperOfAISystem?: Array<{ __typename?: 'Entity', entity_id: string, name: string } | null> | null, AllegedHarmedOrNearlyHarmedParties?: Array<{ __typename?: 'Entity', entity_id: string, name: string } | null> | null, implicated_systems?: Array<{ __typename?: 'Entity', entity_id: string, name: string } | null> | null, nlp_similar_incidents?: Array<{ __typename?: 'IncidentNlp_similar_incident', incident_id?: number | null, similarity?: number | null } | null> | null, reports?: Array<{ __typename?: 'Report', report_number: number } | null> | null, embedding?: { __typename?: 'IncidentEmbedding', from_reports?: Array | null, vector?: Array | null } | null } | null }; export type FindIncidentsTableQueryVariables = Exact<{ filter?: InputMaybe; }>; -export type FindIncidentsTableQuery = { __typename?: 'Query', incidents?: Array<{ __typename?: 'Incident', incident_id: number, title: string, description?: string | null, date: string, editors: Array<{ __typename?: 'User', userId: string, first_name?: string | null, last_name?: string | null } | null>, AllegedDeployerOfAISystem?: Array<{ __typename?: 'Entity', entity_id: string, name: string } | null> | null, AllegedDeveloperOfAISystem?: Array<{ __typename?: 'Entity', entity_id: string, name: string } | null> | null, AllegedHarmedOrNearlyHarmedParties?: Array<{ __typename?: 'Entity', entity_id: string, name: string } | null> | null, reports?: Array<{ __typename?: 'Report', report_number: number } | null> | null } | null> | null }; +export type FindIncidentsTableQuery = { __typename?: 'Query', incidents?: Array<{ __typename?: 'Incident', incident_id: number, title: string, description?: string | null, date: string, editors: Array<{ __typename?: 'User', userId: string, first_name?: string | null, last_name?: string | null } | null>, AllegedDeployerOfAISystem?: Array<{ __typename?: 'Entity', entity_id: string, name: string } | null> | null, AllegedDeveloperOfAISystem?: Array<{ __typename?: 'Entity', entity_id: string, name: string } | null> | null, AllegedHarmedOrNearlyHarmedParties?: Array<{ __typename?: 'Entity', entity_id: string, name: string } | null> | null, implicated_systems?: Array<{ __typename?: 'Entity', entity_id: string, name: string } | null> | null, reports?: Array<{ __typename?: 'Report', report_number: number } | null> | null } | null> | null }; export type FindIncidentEntitiesQueryVariables = Exact<{ filter?: InputMaybe; }>; -export type FindIncidentEntitiesQuery = { __typename?: 'Query', incident?: { __typename?: 'Incident', incident_id: number, AllegedDeployerOfAISystem?: Array<{ __typename?: 'Entity', entity_id: string, name: string } | null> | null, AllegedDeveloperOfAISystem?: Array<{ __typename?: 'Entity', entity_id: string, name: string } | null> | null, AllegedHarmedOrNearlyHarmedParties?: Array<{ __typename?: 'Entity', entity_id: string, name: string } | null> | null } | null }; +export type FindIncidentEntitiesQuery = { __typename?: 'Query', incident?: { __typename?: 'Incident', incident_id: number, AllegedDeployerOfAISystem?: Array<{ __typename?: 'Entity', entity_id: string, name: string } | null> | null, AllegedDeveloperOfAISystem?: Array<{ __typename?: 'Entity', entity_id: string, name: string } | null> | null, AllegedHarmedOrNearlyHarmedParties?: Array<{ __typename?: 'Entity', entity_id: string, name: string } | null> | null, implicated_systems?: Array<{ __typename?: 'Entity', entity_id: string, name: string } | null> | null } | null }; export type FindIncidentsQueryVariables = Exact<{ filter?: InputMaybe; }>; -export type FindIncidentsQuery = { __typename?: 'Query', incidents?: Array<{ __typename?: 'Incident', incident_id: number, title: string, description?: string | null, date: string, editor_similar_incidents?: Array | null, editor_dissimilar_incidents?: Array | null, flagged_dissimilar_incidents?: Array | null, editors: Array<{ __typename?: 'User', userId: string, first_name?: string | null, last_name?: string | null } | null>, AllegedDeployerOfAISystem?: Array<{ __typename?: 'Entity', entity_id: string, name: string } | null> | null, AllegedDeveloperOfAISystem?: Array<{ __typename?: 'Entity', entity_id: string, name: string } | null> | null, AllegedHarmedOrNearlyHarmedParties?: Array<{ __typename?: 'Entity', entity_id: string, name: string } | null> | null, nlp_similar_incidents?: Array<{ __typename?: 'IncidentNlp_similar_incident', incident_id?: number | null, similarity?: number | null } | null> | null, reports?: Array<{ __typename?: 'Report', report_number: number } | null> | null, embedding?: { __typename?: 'IncidentEmbedding', from_reports?: Array | null, vector?: Array | null } | null } | null> | null }; +export type FindIncidentsQuery = { __typename?: 'Query', incidents?: Array<{ __typename?: 'Incident', incident_id: number, title: string, description?: string | null, date: string, editor_similar_incidents?: Array | null, editor_dissimilar_incidents?: Array | null, flagged_dissimilar_incidents?: Array | null, editors: Array<{ __typename?: 'User', userId: string, first_name?: string | null, last_name?: string | null } | null>, AllegedDeployerOfAISystem?: Array<{ __typename?: 'Entity', entity_id: string, name: string } | null> | null, AllegedDeveloperOfAISystem?: Array<{ __typename?: 'Entity', entity_id: string, name: string } | null> | null, AllegedHarmedOrNearlyHarmedParties?: Array<{ __typename?: 'Entity', entity_id: string, name: string } | null> | null, implicated_systems?: Array<{ __typename?: 'Entity', entity_id: string, name: string } | null> | null, nlp_similar_incidents?: Array<{ __typename?: 'IncidentNlp_similar_incident', incident_id?: number | null, similarity?: number | null } | null> | null, reports?: Array<{ __typename?: 'Report', report_number: number } | null> | null, embedding?: { __typename?: 'IncidentEmbedding', from_reports?: Array | null, vector?: Array | null } | null } | null> | null }; export type FindIncidentsTitlesQueryVariables = Exact<{ filter?: InputMaybe; @@ -5312,7 +4678,7 @@ export type UpdateIncidentMutationVariables = Exact<{ }>; -export type UpdateIncidentMutation = { __typename?: 'Mutation', updateOneIncident?: { __typename?: 'Incident', incident_id: number, title: string, description?: string | null, date: string, editor_similar_incidents?: Array | null, editor_dissimilar_incidents?: Array | null, flagged_dissimilar_incidents?: Array | null, editor_notes?: string | null, editors: Array<{ __typename?: 'User', userId: string, first_name?: string | null, last_name?: string | null } | null>, AllegedDeployerOfAISystem?: Array<{ __typename?: 'Entity', entity_id: string, name: string } | null> | null, AllegedDeveloperOfAISystem?: Array<{ __typename?: 'Entity', entity_id: string, name: string } | null> | null, AllegedHarmedOrNearlyHarmedParties?: Array<{ __typename?: 'Entity', entity_id: string, name: string } | null> | null, nlp_similar_incidents?: Array<{ __typename?: 'IncidentNlp_similar_incident', incident_id?: number | null, similarity?: number | null } | null> | null, reports?: Array<{ __typename?: 'Report', report_number: number } | null> | null, embedding?: { __typename?: 'IncidentEmbedding', from_reports?: Array | null, vector?: Array | null } | null } | null }; +export type UpdateIncidentMutation = { __typename?: 'Mutation', updateOneIncident?: { __typename?: 'Incident', incident_id: number, title: string, description?: string | null, date: string, editor_similar_incidents?: Array | null, editor_dissimilar_incidents?: Array | null, flagged_dissimilar_incidents?: Array | null, editor_notes?: string | null, editors: Array<{ __typename?: 'User', userId: string, first_name?: string | null, last_name?: string | null } | null>, AllegedDeployerOfAISystem?: Array<{ __typename?: 'Entity', entity_id: string, name: string } | null> | null, AllegedDeveloperOfAISystem?: Array<{ __typename?: 'Entity', entity_id: string, name: string } | null> | null, AllegedHarmedOrNearlyHarmedParties?: Array<{ __typename?: 'Entity', entity_id: string, name: string } | null> | null, implicated_systems?: Array<{ __typename?: 'Entity', entity_id: string, name: string } | null> | null, nlp_similar_incidents?: Array<{ __typename?: 'IncidentNlp_similar_incident', incident_id?: number | null, similarity?: number | null } | null> | null, reports?: Array<{ __typename?: 'Report', report_number: number } | null> | null, embedding?: { __typename?: 'IncidentEmbedding', from_reports?: Array | null, vector?: Array | null } | null } | null }; export type UpdateIncidentsMutationVariables = Exact<{ filter: IncidentFilterType; @@ -5339,21 +4705,14 @@ export type FindIncidentFullQueryVariables = Exact<{ }>; -export type FindIncidentFullQuery = { __typename?: 'Query', incident?: { __typename?: 'Incident', incident_id: number, title: string, description?: string | null, date: string, editor_similar_incidents?: Array | null, editor_dissimilar_incidents?: Array | null, flagged_dissimilar_incidents?: Array | null, editor_notes?: string | null, epoch_date_modified?: number | null, editors: Array<{ __typename?: 'User', userId: string, first_name?: string | null, last_name?: string | null } | null>, AllegedDeployerOfAISystem?: Array<{ __typename?: 'Entity', entity_id: string, name: string } | null> | null, AllegedDeveloperOfAISystem?: Array<{ __typename?: 'Entity', entity_id: string, name: string } | null> | null, AllegedHarmedOrNearlyHarmedParties?: Array<{ __typename?: 'Entity', entity_id: string, name: string } | null> | null, nlp_similar_incidents?: Array<{ __typename?: 'IncidentNlp_similar_incident', incident_id?: number | null, similarity?: number | null } | null> | null, reports?: Array<{ __typename?: 'Report', submitters: Array, date_published: any, report_number: number, title: string, description?: string | null, url: string, image_url: string, cloudinary_id: string, source_domain: string, text: string, authors: Array, epoch_date_submitted: number, language: string, tags: Array, inputs_outputs?: Array | null } | null> | null, embedding?: { __typename?: 'IncidentEmbedding', from_reports?: Array | null, vector?: Array | null } | null, tsne?: { __typename?: 'IncidentTsne', x?: number | null, y?: number | null } | null } | null }; - -export type LogIncidentHistoryMutationVariables = Exact<{ - input: History_IncidentInsertInput; -}>; - - -export type LogIncidentHistoryMutation = { __typename?: 'Mutation', logIncidentHistory?: { __typename?: 'LogIncidentHistoryPayload', incident_id?: number | null } | null }; +export type FindIncidentFullQuery = { __typename?: 'Query', incident?: { __typename?: 'Incident', incident_id: number, title: string, description?: string | null, date: string, editor_similar_incidents?: Array | null, editor_dissimilar_incidents?: Array | null, flagged_dissimilar_incidents?: Array | null, editor_notes?: string | null, epoch_date_modified?: number | null, editors: Array<{ __typename?: 'User', userId: string, first_name?: string | null, last_name?: string | null } | null>, AllegedDeployerOfAISystem?: Array<{ __typename?: 'Entity', entity_id: string, name: string } | null> | null, AllegedDeveloperOfAISystem?: Array<{ __typename?: 'Entity', entity_id: string, name: string } | null> | null, AllegedHarmedOrNearlyHarmedParties?: Array<{ __typename?: 'Entity', entity_id: string, name: string } | null> | null, implicated_systems?: Array<{ __typename?: 'Entity', entity_id: string, name: string } | null> | null, nlp_similar_incidents?: Array<{ __typename?: 'IncidentNlp_similar_incident', incident_id?: number | null, similarity?: number | null } | null> | null, reports?: Array<{ __typename?: 'Report', submitters: Array, date_published: any, report_number: number, title: string, description?: string | null, url: string, image_url: string, cloudinary_id: string, source_domain: string, text: string, authors: Array, epoch_date_submitted: number, language: string, tags: Array, inputs_outputs?: Array | null } | null> | null, embedding?: { __typename?: 'IncidentEmbedding', from_reports?: Array | null, vector?: Array | null } | null, tsne?: { __typename?: 'IncidentTsne', x?: number | null, y?: number | null } | null } | null }; export type FindIncidentHistoryQueryVariables = Exact<{ - query?: InputMaybe; + filter?: InputMaybe; }>; -export type FindIncidentHistoryQuery = { __typename?: 'Query', history_incidents: Array<{ __typename?: 'History_incident', incident_id: number, AllegedDeployerOfAISystem?: Array | null, AllegedDeveloperOfAISystem?: Array | null, AllegedHarmedOrNearlyHarmedParties?: Array | null, _id?: any | null, date: string, description?: string | null, modifiedBy?: string | null, editor_dissimilar_incidents?: Array | null, editor_notes?: string | null, editor_similar_incidents?: Array | null, editors: Array, epoch_date_modified?: number | null, flagged_dissimilar_incidents?: Array | null, reports: Array, title: string, embedding?: { __typename?: 'History_incidentEmbedding', from_reports?: Array | null, vector?: Array | null } | null, nlp_similar_incidents?: Array<{ __typename?: 'History_incidentNlp_similar_incident', incident_id?: number | null, similarity?: number | null } | null> | null, tsne?: { __typename?: 'History_incidentTsne', x?: number | null, y?: number | null } | null } | null> }; +export type FindIncidentHistoryQuery = { __typename?: 'Query', history_incidents?: Array<{ __typename?: 'History_incident', incident_id: number, AllegedDeployerOfAISystem?: Array | null, AllegedDeveloperOfAISystem?: Array | null, AllegedHarmedOrNearlyHarmedParties?: Array | null, implicated_systems?: Array | null, _id?: any | null, date: string, description?: string | null, modifiedBy?: string | null, editor_dissimilar_incidents?: Array | null, editor_notes?: string | null, editor_similar_incidents?: Array | null, editors: Array, epoch_date_modified?: number | null, flagged_dissimilar_incidents?: Array | null, reports: Array, title: string, embedding?: { __typename?: 'IncidentEmbedding', from_reports?: Array | null, vector?: Array | null } | null, nlp_similar_incidents?: Array<{ __typename?: 'IncidentNlp_similar_incident', incident_id?: number | null, similarity?: number | null } | null> | null, tsne?: { __typename?: 'IncidentTsne', x?: number | null, y?: number | null } | null } | null> | null }; export type FlagIncidentSimilarityMutationVariables = Exact<{ incidentId: Scalars['Int']['input']; @@ -5420,19 +4779,12 @@ export type LinkReportsToIncidentsMutationVariables = Exact<{ export type LinkReportsToIncidentsMutation = { __typename?: 'Mutation', linkReportsToIncidents?: Array<{ __typename?: 'Incident', incident_id: number, reports?: Array<{ __typename?: 'Report', report_number: number } | null> | null } | null> | null }; -export type LogReportHistoryMutationVariables = Exact<{ - input: History_ReportInsertInput; -}>; - - -export type LogReportHistoryMutation = { __typename?: 'Mutation', logReportHistory?: { __typename?: 'LogReportHistoryPayload', report_number?: number | null } | null }; - export type FindReportHistoryQueryVariables = Exact<{ - query?: InputMaybe; + filter?: InputMaybe; }>; -export type FindReportHistoryQuery = { __typename?: 'Query', history_reports: Array<{ __typename?: 'History_report', _id?: any | null, authors: Array, cloudinary_id: string, date_downloaded: any, date_modified: any, date_published: any, date_submitted: any, description?: string | null, editor_notes?: string | null, epoch_date_downloaded: number, epoch_date_modified: number, epoch_date_published: number, epoch_date_submitted: number, flag?: boolean | null, image_url: string, inputs_outputs?: Array | null, is_incident_report?: boolean | null, language: string, modifiedBy?: string | null, plain_text: string, report_number: number, submitters: Array, tags: Array, text: string, title: string, url: string, source_domain: string, user?: string | null, quiet?: boolean | null, embedding?: { __typename?: 'History_reportEmbedding', from_text_hash?: string | null, vector?: Array | null } | null } | null> }; +export type FindReportHistoryQuery = { __typename?: 'Query', history_reports?: Array<{ __typename?: 'History_report', _id?: any | null, authors?: Array | null, cloudinary_id?: string | null, date_downloaded: any, date_modified: any, date_published: any, date_submitted: any, description?: string | null, editor_notes?: string | null, epoch_date_downloaded?: number | null, epoch_date_modified?: number | null, epoch_date_published?: number | null, epoch_date_submitted?: number | null, flag?: boolean | null, image_url?: string | null, inputs_outputs?: Array | null, is_incident_report?: boolean | null, language?: string | null, modifiedBy?: string | null, plain_text?: string | null, report_number?: number | null, submitters?: Array | null, tags?: Array | null, text?: string | null, title?: string | null, url?: string | null, source_domain?: string | null, user?: string | null, quiet?: boolean | null, embedding?: { __typename?: 'ReportEmbedding', from_text_hash?: string | null, vector?: Array | null } | null } | null> | null }; export type FindReportsQueryVariables = Exact<{ filter: ReportFilterType; @@ -5466,14 +4818,14 @@ export type DeleteSubmissionMutation = { __typename?: 'Mutation', deleteOneSubmi export type FindSubmissionsQueryVariables = Exact<{ [key: string]: never; }>; -export type FindSubmissionsQuery = { __typename?: 'Query', submissions?: Array<{ __typename?: 'Submission', _id?: any | null, cloudinary_id?: string | null, date_downloaded: string, date_modified: string, date_published: string, date_submitted: string, description?: string | null, image_url: string, incident_date?: string | null, incident_ids?: Array | null, incident_title?: string | null, language: string, source_domain: string, text: string, title: string, authors: Array, submitters: Array, url: string, editor_notes?: string | null, tags: Array, editor_similar_incidents?: Array | null, editor_dissimilar_incidents?: Array | null, plain_text?: string | null, status?: string | null, quiet?: boolean | null, incident_editors?: Array<{ __typename?: 'User', first_name?: string | null, last_name?: string | null, userId: string } | null> | null, nlp_similar_incidents?: Array<{ __typename?: 'IncidentNlp_similar_incident', similarity?: number | null, incident_id?: number | null } | null> | null, developers?: Array<{ __typename?: 'Entity', entity_id: string, name: string } | null> | null, deployers?: Array<{ __typename?: 'Entity', entity_id: string, name: string } | null> | null, harmed_parties?: Array<{ __typename?: 'Entity', entity_id: string, name: string } | null> | null, user?: { __typename?: 'User', userId: string } | null } | null> | null }; +export type FindSubmissionsQuery = { __typename?: 'Query', submissions?: Array<{ __typename?: 'Submission', _id?: any | null, cloudinary_id?: string | null, date_downloaded: string, date_modified: string, date_published: string, date_submitted: string, description?: string | null, image_url: string, incident_date?: string | null, incident_ids?: Array | null, incident_title?: string | null, language: string, source_domain: string, text: string, title: string, authors: Array, submitters: Array, url: string, editor_notes?: string | null, tags: Array, editor_similar_incidents?: Array | null, editor_dissimilar_incidents?: Array | null, plain_text?: string | null, status?: string | null, quiet?: boolean | null, incident_editors?: Array<{ __typename?: 'User', first_name?: string | null, last_name?: string | null, userId: string } | null> | null, nlp_similar_incidents?: Array<{ __typename?: 'IncidentNlp_similar_incident', similarity?: number | null, incident_id?: number | null } | null> | null, developers?: Array<{ __typename?: 'Entity', entity_id: string, name: string } | null> | null, deployers?: Array<{ __typename?: 'Entity', entity_id: string, name: string } | null> | null, harmed_parties?: Array<{ __typename?: 'Entity', entity_id: string, name: string } | null> | null, user?: { __typename?: 'User', userId: string } | null, implicated_systems?: Array<{ __typename?: 'Entity', entity_id: string, name: string } | null> | null } | null> | null }; export type FindSubmissionQueryVariables = Exact<{ filter: SubmissionFilterType; }>; -export type FindSubmissionQuery = { __typename?: 'Query', submission?: { __typename?: 'Submission', _id?: any | null, cloudinary_id?: string | null, date_downloaded: string, date_modified: string, date_published: string, date_submitted: string, description?: string | null, image_url: string, incident_date?: string | null, incident_ids?: Array | null, incident_title?: string | null, language: string, source_domain: string, text: string, title: string, authors: Array, submitters: Array, url: string, editor_notes?: string | null, tags: Array, editor_similar_incidents?: Array | null, editor_dissimilar_incidents?: Array | null, status?: string | null, quiet?: boolean | null, incident_editors?: Array<{ __typename?: 'User', first_name?: string | null, last_name?: string | null, userId: string } | null> | null, developers?: Array<{ __typename?: 'Entity', entity_id: string, name: string } | null> | null, deployers?: Array<{ __typename?: 'Entity', entity_id: string, name: string } | null> | null, harmed_parties?: Array<{ __typename?: 'Entity', entity_id: string, name: string } | null> | null, nlp_similar_incidents?: Array<{ __typename?: 'IncidentNlp_similar_incident', similarity?: number | null, incident_id?: number | null } | null> | null } | null }; +export type FindSubmissionQuery = { __typename?: 'Query', submission?: { __typename?: 'Submission', _id?: any | null, cloudinary_id?: string | null, date_downloaded: string, date_modified: string, date_published: string, date_submitted: string, description?: string | null, image_url: string, incident_date?: string | null, incident_ids?: Array | null, incident_title?: string | null, language: string, source_domain: string, text: string, title: string, authors: Array, submitters: Array, url: string, editor_notes?: string | null, tags: Array, editor_similar_incidents?: Array | null, editor_dissimilar_incidents?: Array | null, status?: string | null, quiet?: boolean | null, incident_editors?: Array<{ __typename?: 'User', first_name?: string | null, last_name?: string | null, userId: string } | null> | null, developers?: Array<{ __typename?: 'Entity', entity_id: string, name: string } | null> | null, deployers?: Array<{ __typename?: 'Entity', entity_id: string, name: string } | null> | null, harmed_parties?: Array<{ __typename?: 'Entity', entity_id: string, name: string } | null> | null, nlp_similar_incidents?: Array<{ __typename?: 'IncidentNlp_similar_incident', similarity?: number | null, incident_id?: number | null } | null> | null, implicated_systems?: Array<{ __typename?: 'Entity', entity_id: string, name: string } | null> | null } | null }; export type UpdateSubmissionMutationVariables = Exact<{ filter: SubmissionFilterType; @@ -5481,7 +4833,7 @@ export type UpdateSubmissionMutationVariables = Exact<{ }>; -export type UpdateSubmissionMutation = { __typename?: 'Mutation', updateOneSubmission?: { __typename?: 'Submission', _id?: any | null, cloudinary_id?: string | null, date_downloaded: string, date_modified: string, date_published: string, date_submitted: string, description?: string | null, image_url: string, incident_date?: string | null, incident_ids?: Array | null, incident_title?: string | null, language: string, source_domain: string, text: string, title: string, authors: Array, submitters: Array, url: string, editor_notes?: string | null, tags: Array, editor_similar_incidents?: Array | null, editor_dissimilar_incidents?: Array | null, incident_editors?: Array<{ __typename?: 'User', first_name?: string | null, last_name?: string | null, userId: string } | null> | null, developers?: Array<{ __typename?: 'Entity', entity_id: string, name: string } | null> | null, deployers?: Array<{ __typename?: 'Entity', entity_id: string, name: string } | null> | null, harmed_parties?: Array<{ __typename?: 'Entity', entity_id: string, name: string } | null> | null, nlp_similar_incidents?: Array<{ __typename?: 'IncidentNlp_similar_incident', similarity?: number | null, incident_id?: number | null } | null> | null } | null }; +export type UpdateSubmissionMutation = { __typename?: 'Mutation', updateOneSubmission?: { __typename?: 'Submission', _id?: any | null, cloudinary_id?: string | null, date_downloaded: string, date_modified: string, date_published: string, date_submitted: string, description?: string | null, image_url: string, incident_date?: string | null, incident_ids?: Array | null, incident_title?: string | null, language: string, source_domain: string, text: string, title: string, authors: Array, submitters: Array, url: string, editor_notes?: string | null, tags: Array, editor_similar_incidents?: Array | null, editor_dissimilar_incidents?: Array | null, incident_editors?: Array<{ __typename?: 'User', first_name?: string | null, last_name?: string | null, userId: string } | null> | null, developers?: Array<{ __typename?: 'Entity', entity_id: string, name: string } | null> | null, deployers?: Array<{ __typename?: 'Entity', entity_id: string, name: string } | null> | null, harmed_parties?: Array<{ __typename?: 'Entity', entity_id: string, name: string } | null> | null, nlp_similar_incidents?: Array<{ __typename?: 'IncidentNlp_similar_incident', similarity?: number | null, incident_id?: number | null } | null> | null, implicated_systems?: Array<{ __typename?: 'Entity', entity_id: string, name: string } | null> | null } | null }; export type InsertSubmissionMutationVariables = Exact<{ data: SubmissionInsertType; @@ -5543,7 +4895,7 @@ export type FindUserQueryVariables = Exact<{ }>; -export type FindUserQuery = { __typename?: 'Query', user?: { __typename?: 'User', roles: Array, userId: string, first_name?: string | null, last_name?: string | null, adminData?: { __typename?: 'UserAdminDatum', email?: string | null, disabled?: boolean | null, creationDate?: any | null, lastAuthenticationDate?: any | null } | null } | null }; +export type FindUserQuery = { __typename?: 'Query', user?: { __typename?: 'User', roles: Array, userId: string, first_name?: string | null, last_name?: string | null, adminData?: { __typename?: 'UserAdminDatum', email: string, disabled: boolean, creationDate: any, lastAuthenticationDate?: any | null } | null } | null }; export type FindUsersByRoleQueryVariables = Exact<{ role?: InputMaybe | Scalars['String']['input']>; @@ -5593,7 +4945,7 @@ export type CreateVariantMutationVariables = Exact<{ }>; -export type CreateVariantMutation = { __typename?: 'Mutation', createVariant?: { __typename?: 'CreateVariantPayload', incident_id?: number | null, report_number?: number | null } | null }; +export type CreateVariantMutation = { __typename?: 'Mutation', createVariant?: { __typename?: 'CreateVariantPayload', incident_id: number, report_number: number } | null }; export type UpdateVariantMutationVariables = Exact<{ filter: ReportFilterType; @@ -5618,18 +4970,17 @@ export const UpsertEntityDocument = {"kind":"Document","definitions":[{"kind":"O export const FindEntitiesDocument = {"kind":"Document","definitions":[{"kind":"OperationDefinition","operation":"query","name":{"kind":"Name","value":"FindEntities"},"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"entities"},"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"entity_id"}},{"kind":"Field","name":{"kind":"Name","value":"name"}}]}}]}}]} as unknown as DocumentNode; export const FindEntityDocument = {"kind":"Document","definitions":[{"kind":"OperationDefinition","operation":"query","name":{"kind":"Name","value":"FindEntity"},"variableDefinitions":[{"kind":"VariableDefinition","variable":{"kind":"Variable","name":{"kind":"Name","value":"filter"}},"type":{"kind":"NamedType","name":{"kind":"Name","value":"EntityFilterType"}}}],"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"entity"},"arguments":[{"kind":"Argument","name":{"kind":"Name","value":"filter"},"value":{"kind":"Variable","name":{"kind":"Name","value":"filter"}}}],"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"entity_id"}},{"kind":"Field","name":{"kind":"Name","value":"name"}},{"kind":"Field","name":{"kind":"Name","value":"created_at"}},{"kind":"Field","name":{"kind":"Name","value":"date_modified"}}]}}]}}]} as unknown as DocumentNode; export const UpdateEntityDocument = {"kind":"Document","definitions":[{"kind":"OperationDefinition","operation":"mutation","name":{"kind":"Name","value":"UpdateEntity"},"variableDefinitions":[{"kind":"VariableDefinition","variable":{"kind":"Variable","name":{"kind":"Name","value":"filter"}},"type":{"kind":"NonNullType","type":{"kind":"NamedType","name":{"kind":"Name","value":"EntityFilterType"}}}},{"kind":"VariableDefinition","variable":{"kind":"Variable","name":{"kind":"Name","value":"update"}},"type":{"kind":"NonNullType","type":{"kind":"NamedType","name":{"kind":"Name","value":"EntityUpdateType"}}}}],"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"updateOneEntity"},"arguments":[{"kind":"Argument","name":{"kind":"Name","value":"filter"},"value":{"kind":"Variable","name":{"kind":"Name","value":"filter"}}},{"kind":"Argument","name":{"kind":"Name","value":"update"},"value":{"kind":"Variable","name":{"kind":"Name","value":"update"}}}],"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"entity_id"}}]}}]}}]} as unknown as DocumentNode; -export const FindIncidentDocument = {"kind":"Document","definitions":[{"kind":"OperationDefinition","operation":"query","name":{"kind":"Name","value":"FindIncident"},"variableDefinitions":[{"kind":"VariableDefinition","variable":{"kind":"Variable","name":{"kind":"Name","value":"filter"}},"type":{"kind":"NamedType","name":{"kind":"Name","value":"IncidentFilterType"}}}],"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"incident"},"arguments":[{"kind":"Argument","name":{"kind":"Name","value":"filter"},"value":{"kind":"Variable","name":{"kind":"Name","value":"filter"}}}],"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"incident_id"}},{"kind":"Field","name":{"kind":"Name","value":"title"}},{"kind":"Field","name":{"kind":"Name","value":"description"}},{"kind":"Field","name":{"kind":"Name","value":"editors"},"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"userId"}},{"kind":"Field","name":{"kind":"Name","value":"first_name"}},{"kind":"Field","name":{"kind":"Name","value":"last_name"}}]}},{"kind":"Field","name":{"kind":"Name","value":"date"}},{"kind":"Field","name":{"kind":"Name","value":"AllegedDeployerOfAISystem"},"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"entity_id"}},{"kind":"Field","name":{"kind":"Name","value":"name"}}]}},{"kind":"Field","name":{"kind":"Name","value":"AllegedDeveloperOfAISystem"},"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"entity_id"}},{"kind":"Field","name":{"kind":"Name","value":"name"}}]}},{"kind":"Field","name":{"kind":"Name","value":"AllegedHarmedOrNearlyHarmedParties"},"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"entity_id"}},{"kind":"Field","name":{"kind":"Name","value":"name"}}]}},{"kind":"Field","name":{"kind":"Name","value":"nlp_similar_incidents"},"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"incident_id"}},{"kind":"Field","name":{"kind":"Name","value":"similarity"}}]}},{"kind":"Field","name":{"kind":"Name","value":"editor_similar_incidents"}},{"kind":"Field","name":{"kind":"Name","value":"editor_dissimilar_incidents"}},{"kind":"Field","name":{"kind":"Name","value":"flagged_dissimilar_incidents"}},{"kind":"Field","name":{"kind":"Name","value":"reports"},"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"report_number"}}]}},{"kind":"Field","name":{"kind":"Name","value":"embedding"},"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"from_reports"}},{"kind":"Field","name":{"kind":"Name","value":"vector"}}]}},{"kind":"Field","name":{"kind":"Name","value":"editor_notes"}}]}}]}}]} as unknown as DocumentNode; -export const FindIncidentsTableDocument = {"kind":"Document","definitions":[{"kind":"OperationDefinition","operation":"query","name":{"kind":"Name","value":"FindIncidentsTable"},"variableDefinitions":[{"kind":"VariableDefinition","variable":{"kind":"Variable","name":{"kind":"Name","value":"filter"}},"type":{"kind":"NamedType","name":{"kind":"Name","value":"IncidentFilterType"}}}],"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"incidents"},"arguments":[{"kind":"Argument","name":{"kind":"Name","value":"filter"},"value":{"kind":"Variable","name":{"kind":"Name","value":"filter"}}}],"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"incident_id"}},{"kind":"Field","name":{"kind":"Name","value":"title"}},{"kind":"Field","name":{"kind":"Name","value":"description"}},{"kind":"Field","name":{"kind":"Name","value":"editors"},"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"userId"}},{"kind":"Field","name":{"kind":"Name","value":"first_name"}},{"kind":"Field","name":{"kind":"Name","value":"last_name"}}]}},{"kind":"Field","name":{"kind":"Name","value":"date"}},{"kind":"Field","name":{"kind":"Name","value":"AllegedDeployerOfAISystem"},"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"entity_id"}},{"kind":"Field","name":{"kind":"Name","value":"name"}}]}},{"kind":"Field","name":{"kind":"Name","value":"AllegedDeveloperOfAISystem"},"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"entity_id"}},{"kind":"Field","name":{"kind":"Name","value":"name"}}]}},{"kind":"Field","name":{"kind":"Name","value":"AllegedHarmedOrNearlyHarmedParties"},"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"entity_id"}},{"kind":"Field","name":{"kind":"Name","value":"name"}}]}},{"kind":"Field","name":{"kind":"Name","value":"reports"},"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"report_number"}}]}}]}}]}}]} as unknown as DocumentNode; -export const FindIncidentEntitiesDocument = {"kind":"Document","definitions":[{"kind":"OperationDefinition","operation":"query","name":{"kind":"Name","value":"FindIncidentEntities"},"variableDefinitions":[{"kind":"VariableDefinition","variable":{"kind":"Variable","name":{"kind":"Name","value":"filter"}},"type":{"kind":"NamedType","name":{"kind":"Name","value":"IncidentFilterType"}}}],"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"incident"},"arguments":[{"kind":"Argument","name":{"kind":"Name","value":"filter"},"value":{"kind":"Variable","name":{"kind":"Name","value":"filter"}}}],"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"incident_id"}},{"kind":"Field","name":{"kind":"Name","value":"AllegedDeployerOfAISystem"},"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"entity_id"}},{"kind":"Field","name":{"kind":"Name","value":"name"}}]}},{"kind":"Field","name":{"kind":"Name","value":"AllegedDeveloperOfAISystem"},"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"entity_id"}},{"kind":"Field","name":{"kind":"Name","value":"name"}}]}},{"kind":"Field","name":{"kind":"Name","value":"AllegedHarmedOrNearlyHarmedParties"},"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"entity_id"}},{"kind":"Field","name":{"kind":"Name","value":"name"}}]}}]}}]}}]} as unknown as DocumentNode; -export const FindIncidentsDocument = {"kind":"Document","definitions":[{"kind":"OperationDefinition","operation":"query","name":{"kind":"Name","value":"FindIncidents"},"variableDefinitions":[{"kind":"VariableDefinition","variable":{"kind":"Variable","name":{"kind":"Name","value":"filter"}},"type":{"kind":"NamedType","name":{"kind":"Name","value":"IncidentFilterType"}}}],"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"incidents"},"arguments":[{"kind":"Argument","name":{"kind":"Name","value":"filter"},"value":{"kind":"Variable","name":{"kind":"Name","value":"filter"}}}],"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"incident_id"}},{"kind":"Field","name":{"kind":"Name","value":"title"}},{"kind":"Field","name":{"kind":"Name","value":"description"}},{"kind":"Field","name":{"kind":"Name","value":"editors"},"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"userId"}},{"kind":"Field","name":{"kind":"Name","value":"first_name"}},{"kind":"Field","name":{"kind":"Name","value":"last_name"}}]}},{"kind":"Field","name":{"kind":"Name","value":"date"}},{"kind":"Field","name":{"kind":"Name","value":"AllegedDeployerOfAISystem"},"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"entity_id"}},{"kind":"Field","name":{"kind":"Name","value":"name"}}]}},{"kind":"Field","name":{"kind":"Name","value":"AllegedDeveloperOfAISystem"},"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"entity_id"}},{"kind":"Field","name":{"kind":"Name","value":"name"}}]}},{"kind":"Field","name":{"kind":"Name","value":"AllegedHarmedOrNearlyHarmedParties"},"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"entity_id"}},{"kind":"Field","name":{"kind":"Name","value":"name"}}]}},{"kind":"Field","name":{"kind":"Name","value":"nlp_similar_incidents"},"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"incident_id"}},{"kind":"Field","name":{"kind":"Name","value":"similarity"}}]}},{"kind":"Field","name":{"kind":"Name","value":"editor_similar_incidents"}},{"kind":"Field","name":{"kind":"Name","value":"editor_dissimilar_incidents"}},{"kind":"Field","name":{"kind":"Name","value":"flagged_dissimilar_incidents"}},{"kind":"Field","name":{"kind":"Name","value":"reports"},"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"report_number"}}]}},{"kind":"Field","name":{"kind":"Name","value":"embedding"},"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"from_reports"}},{"kind":"Field","name":{"kind":"Name","value":"vector"}}]}}]}}]}}]} as unknown as DocumentNode; +export const FindIncidentDocument = {"kind":"Document","definitions":[{"kind":"OperationDefinition","operation":"query","name":{"kind":"Name","value":"FindIncident"},"variableDefinitions":[{"kind":"VariableDefinition","variable":{"kind":"Variable","name":{"kind":"Name","value":"filter"}},"type":{"kind":"NamedType","name":{"kind":"Name","value":"IncidentFilterType"}}}],"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"incident"},"arguments":[{"kind":"Argument","name":{"kind":"Name","value":"filter"},"value":{"kind":"Variable","name":{"kind":"Name","value":"filter"}}}],"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"incident_id"}},{"kind":"Field","name":{"kind":"Name","value":"title"}},{"kind":"Field","name":{"kind":"Name","value":"description"}},{"kind":"Field","name":{"kind":"Name","value":"editors"},"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"userId"}},{"kind":"Field","name":{"kind":"Name","value":"first_name"}},{"kind":"Field","name":{"kind":"Name","value":"last_name"}}]}},{"kind":"Field","name":{"kind":"Name","value":"date"}},{"kind":"Field","name":{"kind":"Name","value":"AllegedDeployerOfAISystem"},"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"entity_id"}},{"kind":"Field","name":{"kind":"Name","value":"name"}}]}},{"kind":"Field","name":{"kind":"Name","value":"AllegedDeveloperOfAISystem"},"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"entity_id"}},{"kind":"Field","name":{"kind":"Name","value":"name"}}]}},{"kind":"Field","name":{"kind":"Name","value":"AllegedHarmedOrNearlyHarmedParties"},"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"entity_id"}},{"kind":"Field","name":{"kind":"Name","value":"name"}}]}},{"kind":"Field","name":{"kind":"Name","value":"implicated_systems"},"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"entity_id"}},{"kind":"Field","name":{"kind":"Name","value":"name"}}]}},{"kind":"Field","name":{"kind":"Name","value":"nlp_similar_incidents"},"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"incident_id"}},{"kind":"Field","name":{"kind":"Name","value":"similarity"}}]}},{"kind":"Field","name":{"kind":"Name","value":"editor_similar_incidents"}},{"kind":"Field","name":{"kind":"Name","value":"editor_dissimilar_incidents"}},{"kind":"Field","name":{"kind":"Name","value":"flagged_dissimilar_incidents"}},{"kind":"Field","name":{"kind":"Name","value":"reports"},"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"report_number"}}]}},{"kind":"Field","name":{"kind":"Name","value":"embedding"},"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"from_reports"}},{"kind":"Field","name":{"kind":"Name","value":"vector"}}]}},{"kind":"Field","name":{"kind":"Name","value":"editor_notes"}}]}}]}}]} as unknown as DocumentNode; +export const FindIncidentsTableDocument = {"kind":"Document","definitions":[{"kind":"OperationDefinition","operation":"query","name":{"kind":"Name","value":"FindIncidentsTable"},"variableDefinitions":[{"kind":"VariableDefinition","variable":{"kind":"Variable","name":{"kind":"Name","value":"filter"}},"type":{"kind":"NamedType","name":{"kind":"Name","value":"IncidentFilterType"}}}],"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"incidents"},"arguments":[{"kind":"Argument","name":{"kind":"Name","value":"filter"},"value":{"kind":"Variable","name":{"kind":"Name","value":"filter"}}}],"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"incident_id"}},{"kind":"Field","name":{"kind":"Name","value":"title"}},{"kind":"Field","name":{"kind":"Name","value":"description"}},{"kind":"Field","name":{"kind":"Name","value":"editors"},"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"userId"}},{"kind":"Field","name":{"kind":"Name","value":"first_name"}},{"kind":"Field","name":{"kind":"Name","value":"last_name"}}]}},{"kind":"Field","name":{"kind":"Name","value":"date"}},{"kind":"Field","name":{"kind":"Name","value":"AllegedDeployerOfAISystem"},"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"entity_id"}},{"kind":"Field","name":{"kind":"Name","value":"name"}}]}},{"kind":"Field","name":{"kind":"Name","value":"AllegedDeveloperOfAISystem"},"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"entity_id"}},{"kind":"Field","name":{"kind":"Name","value":"name"}}]}},{"kind":"Field","name":{"kind":"Name","value":"AllegedHarmedOrNearlyHarmedParties"},"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"entity_id"}},{"kind":"Field","name":{"kind":"Name","value":"name"}}]}},{"kind":"Field","name":{"kind":"Name","value":"implicated_systems"},"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"entity_id"}},{"kind":"Field","name":{"kind":"Name","value":"name"}}]}},{"kind":"Field","name":{"kind":"Name","value":"reports"},"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"report_number"}}]}}]}}]}}]} as unknown as DocumentNode; +export const FindIncidentEntitiesDocument = {"kind":"Document","definitions":[{"kind":"OperationDefinition","operation":"query","name":{"kind":"Name","value":"FindIncidentEntities"},"variableDefinitions":[{"kind":"VariableDefinition","variable":{"kind":"Variable","name":{"kind":"Name","value":"filter"}},"type":{"kind":"NamedType","name":{"kind":"Name","value":"IncidentFilterType"}}}],"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"incident"},"arguments":[{"kind":"Argument","name":{"kind":"Name","value":"filter"},"value":{"kind":"Variable","name":{"kind":"Name","value":"filter"}}}],"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"incident_id"}},{"kind":"Field","name":{"kind":"Name","value":"AllegedDeployerOfAISystem"},"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"entity_id"}},{"kind":"Field","name":{"kind":"Name","value":"name"}}]}},{"kind":"Field","name":{"kind":"Name","value":"AllegedDeveloperOfAISystem"},"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"entity_id"}},{"kind":"Field","name":{"kind":"Name","value":"name"}}]}},{"kind":"Field","name":{"kind":"Name","value":"AllegedHarmedOrNearlyHarmedParties"},"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"entity_id"}},{"kind":"Field","name":{"kind":"Name","value":"name"}}]}},{"kind":"Field","name":{"kind":"Name","value":"implicated_systems"},"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"entity_id"}},{"kind":"Field","name":{"kind":"Name","value":"name"}}]}}]}}]}}]} as unknown as DocumentNode; +export const FindIncidentsDocument = {"kind":"Document","definitions":[{"kind":"OperationDefinition","operation":"query","name":{"kind":"Name","value":"FindIncidents"},"variableDefinitions":[{"kind":"VariableDefinition","variable":{"kind":"Variable","name":{"kind":"Name","value":"filter"}},"type":{"kind":"NamedType","name":{"kind":"Name","value":"IncidentFilterType"}}}],"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"incidents"},"arguments":[{"kind":"Argument","name":{"kind":"Name","value":"filter"},"value":{"kind":"Variable","name":{"kind":"Name","value":"filter"}}}],"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"incident_id"}},{"kind":"Field","name":{"kind":"Name","value":"title"}},{"kind":"Field","name":{"kind":"Name","value":"description"}},{"kind":"Field","name":{"kind":"Name","value":"editors"},"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"userId"}},{"kind":"Field","name":{"kind":"Name","value":"first_name"}},{"kind":"Field","name":{"kind":"Name","value":"last_name"}}]}},{"kind":"Field","name":{"kind":"Name","value":"date"}},{"kind":"Field","name":{"kind":"Name","value":"AllegedDeployerOfAISystem"},"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"entity_id"}},{"kind":"Field","name":{"kind":"Name","value":"name"}}]}},{"kind":"Field","name":{"kind":"Name","value":"AllegedDeveloperOfAISystem"},"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"entity_id"}},{"kind":"Field","name":{"kind":"Name","value":"name"}}]}},{"kind":"Field","name":{"kind":"Name","value":"AllegedHarmedOrNearlyHarmedParties"},"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"entity_id"}},{"kind":"Field","name":{"kind":"Name","value":"name"}}]}},{"kind":"Field","name":{"kind":"Name","value":"implicated_systems"},"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"entity_id"}},{"kind":"Field","name":{"kind":"Name","value":"name"}}]}},{"kind":"Field","name":{"kind":"Name","value":"nlp_similar_incidents"},"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"incident_id"}},{"kind":"Field","name":{"kind":"Name","value":"similarity"}}]}},{"kind":"Field","name":{"kind":"Name","value":"editor_similar_incidents"}},{"kind":"Field","name":{"kind":"Name","value":"editor_dissimilar_incidents"}},{"kind":"Field","name":{"kind":"Name","value":"flagged_dissimilar_incidents"}},{"kind":"Field","name":{"kind":"Name","value":"reports"},"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"report_number"}}]}},{"kind":"Field","name":{"kind":"Name","value":"embedding"},"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"from_reports"}},{"kind":"Field","name":{"kind":"Name","value":"vector"}}]}}]}}]}}]} as unknown as DocumentNode; export const FindIncidentsTitlesDocument = {"kind":"Document","definitions":[{"kind":"OperationDefinition","operation":"query","name":{"kind":"Name","value":"FindIncidentsTitles"},"variableDefinitions":[{"kind":"VariableDefinition","variable":{"kind":"Variable","name":{"kind":"Name","value":"filter"}},"type":{"kind":"NamedType","name":{"kind":"Name","value":"IncidentFilterType"}}}],"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"incidents"},"arguments":[{"kind":"Argument","name":{"kind":"Name","value":"filter"},"value":{"kind":"Variable","name":{"kind":"Name","value":"filter"}}}],"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"incident_id"}},{"kind":"Field","name":{"kind":"Name","value":"title"}}]}}]}}]} as unknown as DocumentNode; -export const UpdateIncidentDocument = {"kind":"Document","definitions":[{"kind":"OperationDefinition","operation":"mutation","name":{"kind":"Name","value":"UpdateIncident"},"variableDefinitions":[{"kind":"VariableDefinition","variable":{"kind":"Variable","name":{"kind":"Name","value":"filter"}},"type":{"kind":"NonNullType","type":{"kind":"NamedType","name":{"kind":"Name","value":"IncidentFilterType"}}}},{"kind":"VariableDefinition","variable":{"kind":"Variable","name":{"kind":"Name","value":"update"}},"type":{"kind":"NonNullType","type":{"kind":"NamedType","name":{"kind":"Name","value":"IncidentUpdateType"}}}}],"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"updateOneIncident"},"arguments":[{"kind":"Argument","name":{"kind":"Name","value":"filter"},"value":{"kind":"Variable","name":{"kind":"Name","value":"filter"}}},{"kind":"Argument","name":{"kind":"Name","value":"update"},"value":{"kind":"Variable","name":{"kind":"Name","value":"update"}}}],"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"incident_id"}},{"kind":"Field","name":{"kind":"Name","value":"title"}},{"kind":"Field","name":{"kind":"Name","value":"description"}},{"kind":"Field","name":{"kind":"Name","value":"editors"},"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"userId"}},{"kind":"Field","name":{"kind":"Name","value":"first_name"}},{"kind":"Field","name":{"kind":"Name","value":"last_name"}}]}},{"kind":"Field","name":{"kind":"Name","value":"date"}},{"kind":"Field","name":{"kind":"Name","value":"AllegedDeployerOfAISystem"},"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"entity_id"}},{"kind":"Field","name":{"kind":"Name","value":"name"}}]}},{"kind":"Field","name":{"kind":"Name","value":"AllegedDeveloperOfAISystem"},"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"entity_id"}},{"kind":"Field","name":{"kind":"Name","value":"name"}}]}},{"kind":"Field","name":{"kind":"Name","value":"AllegedHarmedOrNearlyHarmedParties"},"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"entity_id"}},{"kind":"Field","name":{"kind":"Name","value":"name"}}]}},{"kind":"Field","name":{"kind":"Name","value":"nlp_similar_incidents"},"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"incident_id"}},{"kind":"Field","name":{"kind":"Name","value":"similarity"}}]}},{"kind":"Field","name":{"kind":"Name","value":"editor_similar_incidents"}},{"kind":"Field","name":{"kind":"Name","value":"editor_dissimilar_incidents"}},{"kind":"Field","name":{"kind":"Name","value":"flagged_dissimilar_incidents"}},{"kind":"Field","name":{"kind":"Name","value":"reports"},"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"report_number"}}]}},{"kind":"Field","name":{"kind":"Name","value":"embedding"},"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"from_reports"}},{"kind":"Field","name":{"kind":"Name","value":"vector"}}]}},{"kind":"Field","name":{"kind":"Name","value":"editor_notes"}}]}}]}}]} as unknown as DocumentNode; +export const UpdateIncidentDocument = {"kind":"Document","definitions":[{"kind":"OperationDefinition","operation":"mutation","name":{"kind":"Name","value":"UpdateIncident"},"variableDefinitions":[{"kind":"VariableDefinition","variable":{"kind":"Variable","name":{"kind":"Name","value":"filter"}},"type":{"kind":"NonNullType","type":{"kind":"NamedType","name":{"kind":"Name","value":"IncidentFilterType"}}}},{"kind":"VariableDefinition","variable":{"kind":"Variable","name":{"kind":"Name","value":"update"}},"type":{"kind":"NonNullType","type":{"kind":"NamedType","name":{"kind":"Name","value":"IncidentUpdateType"}}}}],"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"updateOneIncident"},"arguments":[{"kind":"Argument","name":{"kind":"Name","value":"filter"},"value":{"kind":"Variable","name":{"kind":"Name","value":"filter"}}},{"kind":"Argument","name":{"kind":"Name","value":"update"},"value":{"kind":"Variable","name":{"kind":"Name","value":"update"}}}],"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"incident_id"}},{"kind":"Field","name":{"kind":"Name","value":"title"}},{"kind":"Field","name":{"kind":"Name","value":"description"}},{"kind":"Field","name":{"kind":"Name","value":"editors"},"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"userId"}},{"kind":"Field","name":{"kind":"Name","value":"first_name"}},{"kind":"Field","name":{"kind":"Name","value":"last_name"}}]}},{"kind":"Field","name":{"kind":"Name","value":"date"}},{"kind":"Field","name":{"kind":"Name","value":"AllegedDeployerOfAISystem"},"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"entity_id"}},{"kind":"Field","name":{"kind":"Name","value":"name"}}]}},{"kind":"Field","name":{"kind":"Name","value":"AllegedDeveloperOfAISystem"},"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"entity_id"}},{"kind":"Field","name":{"kind":"Name","value":"name"}}]}},{"kind":"Field","name":{"kind":"Name","value":"AllegedHarmedOrNearlyHarmedParties"},"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"entity_id"}},{"kind":"Field","name":{"kind":"Name","value":"name"}}]}},{"kind":"Field","name":{"kind":"Name","value":"implicated_systems"},"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"entity_id"}},{"kind":"Field","name":{"kind":"Name","value":"name"}}]}},{"kind":"Field","name":{"kind":"Name","value":"nlp_similar_incidents"},"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"incident_id"}},{"kind":"Field","name":{"kind":"Name","value":"similarity"}}]}},{"kind":"Field","name":{"kind":"Name","value":"editor_similar_incidents"}},{"kind":"Field","name":{"kind":"Name","value":"editor_dissimilar_incidents"}},{"kind":"Field","name":{"kind":"Name","value":"flagged_dissimilar_incidents"}},{"kind":"Field","name":{"kind":"Name","value":"reports"},"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"report_number"}}]}},{"kind":"Field","name":{"kind":"Name","value":"embedding"},"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"from_reports"}},{"kind":"Field","name":{"kind":"Name","value":"vector"}}]}},{"kind":"Field","name":{"kind":"Name","value":"editor_notes"}}]}}]}}]} as unknown as DocumentNode; export const UpdateIncidentsDocument = {"kind":"Document","definitions":[{"kind":"OperationDefinition","operation":"mutation","name":{"kind":"Name","value":"UpdateIncidents"},"variableDefinitions":[{"kind":"VariableDefinition","variable":{"kind":"Variable","name":{"kind":"Name","value":"filter"}},"type":{"kind":"NonNullType","type":{"kind":"NamedType","name":{"kind":"Name","value":"IncidentFilterType"}}}},{"kind":"VariableDefinition","variable":{"kind":"Variable","name":{"kind":"Name","value":"update"}},"type":{"kind":"NonNullType","type":{"kind":"NamedType","name":{"kind":"Name","value":"IncidentUpdateType"}}}}],"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"updateManyIncidents"},"arguments":[{"kind":"Argument","name":{"kind":"Name","value":"filter"},"value":{"kind":"Variable","name":{"kind":"Name","value":"filter"}}},{"kind":"Argument","name":{"kind":"Name","value":"update"},"value":{"kind":"Variable","name":{"kind":"Name","value":"update"}}}],"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"matchedCount"}},{"kind":"Field","name":{"kind":"Name","value":"modifiedCount"}}]}}]}}]} as unknown as DocumentNode; export const InsertIncidentDocument = {"kind":"Document","definitions":[{"kind":"OperationDefinition","operation":"mutation","name":{"kind":"Name","value":"InsertIncident"},"variableDefinitions":[{"kind":"VariableDefinition","variable":{"kind":"Variable","name":{"kind":"Name","value":"data"}},"type":{"kind":"NonNullType","type":{"kind":"NamedType","name":{"kind":"Name","value":"IncidentInsertType"}}}}],"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"insertOneIncident"},"arguments":[{"kind":"Argument","name":{"kind":"Name","value":"data"},"value":{"kind":"Variable","name":{"kind":"Name","value":"data"}}}],"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"incident_id"}}]}}]}}]} as unknown as DocumentNode; export const FindLastIncidentDocument = {"kind":"Document","definitions":[{"kind":"OperationDefinition","operation":"query","name":{"kind":"Name","value":"FindLastIncident"},"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"incidents"},"arguments":[{"kind":"Argument","name":{"kind":"Name","value":"sort"},"value":{"kind":"ObjectValue","fields":[{"kind":"ObjectField","name":{"kind":"Name","value":"incident_id"},"value":{"kind":"EnumValue","value":"DESC"}}]}},{"kind":"Argument","name":{"kind":"Name","value":"pagination"},"value":{"kind":"ObjectValue","fields":[{"kind":"ObjectField","name":{"kind":"Name","value":"limit"},"value":{"kind":"IntValue","value":"1"}},{"kind":"ObjectField","name":{"kind":"Name","value":"skip"},"value":{"kind":"IntValue","value":"0"}}]}}],"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"incident_id"}}]}}]}}]} as unknown as DocumentNode; -export const FindIncidentFullDocument = {"kind":"Document","definitions":[{"kind":"OperationDefinition","operation":"query","name":{"kind":"Name","value":"FindIncidentFull"},"variableDefinitions":[{"kind":"VariableDefinition","variable":{"kind":"Variable","name":{"kind":"Name","value":"filter"}},"type":{"kind":"NamedType","name":{"kind":"Name","value":"IncidentFilterType"}}}],"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"incident"},"arguments":[{"kind":"Argument","name":{"kind":"Name","value":"filter"},"value":{"kind":"Variable","name":{"kind":"Name","value":"filter"}}}],"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"incident_id"}},{"kind":"Field","name":{"kind":"Name","value":"title"}},{"kind":"Field","name":{"kind":"Name","value":"description"}},{"kind":"Field","name":{"kind":"Name","value":"editors"},"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"userId"}},{"kind":"Field","name":{"kind":"Name","value":"first_name"}},{"kind":"Field","name":{"kind":"Name","value":"last_name"}}]}},{"kind":"Field","name":{"kind":"Name","value":"date"}},{"kind":"Field","name":{"kind":"Name","value":"AllegedDeployerOfAISystem"},"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"entity_id"}},{"kind":"Field","name":{"kind":"Name","value":"name"}}]}},{"kind":"Field","name":{"kind":"Name","value":"AllegedDeveloperOfAISystem"},"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"entity_id"}},{"kind":"Field","name":{"kind":"Name","value":"name"}}]}},{"kind":"Field","name":{"kind":"Name","value":"AllegedHarmedOrNearlyHarmedParties"},"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"entity_id"}},{"kind":"Field","name":{"kind":"Name","value":"name"}}]}},{"kind":"Field","name":{"kind":"Name","value":"nlp_similar_incidents"},"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"incident_id"}},{"kind":"Field","name":{"kind":"Name","value":"similarity"}}]}},{"kind":"Field","name":{"kind":"Name","value":"editor_similar_incidents"}},{"kind":"Field","name":{"kind":"Name","value":"editor_dissimilar_incidents"}},{"kind":"Field","name":{"kind":"Name","value":"flagged_dissimilar_incidents"}},{"kind":"Field","name":{"kind":"Name","value":"reports"},"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"submitters"}},{"kind":"Field","name":{"kind":"Name","value":"date_published"}},{"kind":"Field","name":{"kind":"Name","value":"report_number"}},{"kind":"Field","name":{"kind":"Name","value":"title"}},{"kind":"Field","name":{"kind":"Name","value":"description"}},{"kind":"Field","name":{"kind":"Name","value":"url"}},{"kind":"Field","name":{"kind":"Name","value":"image_url"}},{"kind":"Field","name":{"kind":"Name","value":"cloudinary_id"}},{"kind":"Field","name":{"kind":"Name","value":"source_domain"}},{"kind":"Field","name":{"kind":"Name","value":"text"}},{"kind":"Field","name":{"kind":"Name","value":"authors"}},{"kind":"Field","name":{"kind":"Name","value":"epoch_date_submitted"}},{"kind":"Field","name":{"kind":"Name","value":"language"}},{"kind":"Field","name":{"kind":"Name","value":"tags"}},{"kind":"Field","name":{"kind":"Name","value":"inputs_outputs"}}]}},{"kind":"Field","name":{"kind":"Name","value":"embedding"},"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"from_reports"}},{"kind":"Field","name":{"kind":"Name","value":"vector"}}]}},{"kind":"Field","name":{"kind":"Name","value":"editor_notes"}},{"kind":"Field","name":{"kind":"Name","value":"epoch_date_modified"}},{"kind":"Field","name":{"kind":"Name","value":"tsne"},"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"x"}},{"kind":"Field","name":{"kind":"Name","value":"y"}}]}}]}}]}}]} as unknown as DocumentNode; -export const LogIncidentHistoryDocument = {"kind":"Document","definitions":[{"kind":"OperationDefinition","operation":"mutation","name":{"kind":"Name","value":"logIncidentHistory"},"variableDefinitions":[{"kind":"VariableDefinition","variable":{"kind":"Variable","name":{"kind":"Name","value":"input"}},"type":{"kind":"NonNullType","type":{"kind":"NamedType","name":{"kind":"Name","value":"History_incidentInsertInput"}}}}],"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"logIncidentHistory"},"arguments":[{"kind":"Argument","name":{"kind":"Name","value":"input"},"value":{"kind":"Variable","name":{"kind":"Name","value":"input"}}}],"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"incident_id"}}]}}]}}]} as unknown as DocumentNode; -export const FindIncidentHistoryDocument = {"kind":"Document","definitions":[{"kind":"OperationDefinition","operation":"query","name":{"kind":"Name","value":"FindIncidentHistory"},"variableDefinitions":[{"kind":"VariableDefinition","variable":{"kind":"Variable","name":{"kind":"Name","value":"query"}},"type":{"kind":"NamedType","name":{"kind":"Name","value":"History_incidentQueryInput"}}}],"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"history_incidents"},"arguments":[{"kind":"Argument","name":{"kind":"Name","value":"query"},"value":{"kind":"Variable","name":{"kind":"Name","value":"query"}}},{"kind":"Argument","name":{"kind":"Name","value":"sortBy"},"value":{"kind":"EnumValue","value":"EPOCH_DATE_MODIFIED_DESC"}}],"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"incident_id"}},{"kind":"Field","name":{"kind":"Name","value":"AllegedDeployerOfAISystem"}},{"kind":"Field","name":{"kind":"Name","value":"AllegedDeveloperOfAISystem"}},{"kind":"Field","name":{"kind":"Name","value":"AllegedHarmedOrNearlyHarmedParties"}},{"kind":"Field","name":{"kind":"Name","value":"_id"}},{"kind":"Field","name":{"kind":"Name","value":"date"}},{"kind":"Field","name":{"kind":"Name","value":"description"}},{"kind":"Field","name":{"kind":"Name","value":"modifiedBy"}},{"kind":"Field","name":{"kind":"Name","value":"editor_dissimilar_incidents"}},{"kind":"Field","name":{"kind":"Name","value":"editor_notes"}},{"kind":"Field","name":{"kind":"Name","value":"editor_similar_incidents"}},{"kind":"Field","name":{"kind":"Name","value":"editors"}},{"kind":"Field","name":{"kind":"Name","value":"embedding"},"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"from_reports"}},{"kind":"Field","name":{"kind":"Name","value":"vector"}}]}},{"kind":"Field","name":{"kind":"Name","value":"epoch_date_modified"}},{"kind":"Field","name":{"kind":"Name","value":"flagged_dissimilar_incidents"}},{"kind":"Field","name":{"kind":"Name","value":"nlp_similar_incidents"},"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"incident_id"}},{"kind":"Field","name":{"kind":"Name","value":"similarity"}}]}},{"kind":"Field","name":{"kind":"Name","value":"reports"}},{"kind":"Field","name":{"kind":"Name","value":"title"}},{"kind":"Field","name":{"kind":"Name","value":"tsne"},"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"x"}},{"kind":"Field","name":{"kind":"Name","value":"y"}}]}}]}}]}}]} as unknown as DocumentNode; +export const FindIncidentFullDocument = {"kind":"Document","definitions":[{"kind":"OperationDefinition","operation":"query","name":{"kind":"Name","value":"FindIncidentFull"},"variableDefinitions":[{"kind":"VariableDefinition","variable":{"kind":"Variable","name":{"kind":"Name","value":"filter"}},"type":{"kind":"NamedType","name":{"kind":"Name","value":"IncidentFilterType"}}}],"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"incident"},"arguments":[{"kind":"Argument","name":{"kind":"Name","value":"filter"},"value":{"kind":"Variable","name":{"kind":"Name","value":"filter"}}}],"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"incident_id"}},{"kind":"Field","name":{"kind":"Name","value":"title"}},{"kind":"Field","name":{"kind":"Name","value":"description"}},{"kind":"Field","name":{"kind":"Name","value":"editors"},"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"userId"}},{"kind":"Field","name":{"kind":"Name","value":"first_name"}},{"kind":"Field","name":{"kind":"Name","value":"last_name"}}]}},{"kind":"Field","name":{"kind":"Name","value":"date"}},{"kind":"Field","name":{"kind":"Name","value":"AllegedDeployerOfAISystem"},"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"entity_id"}},{"kind":"Field","name":{"kind":"Name","value":"name"}}]}},{"kind":"Field","name":{"kind":"Name","value":"AllegedDeveloperOfAISystem"},"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"entity_id"}},{"kind":"Field","name":{"kind":"Name","value":"name"}}]}},{"kind":"Field","name":{"kind":"Name","value":"AllegedHarmedOrNearlyHarmedParties"},"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"entity_id"}},{"kind":"Field","name":{"kind":"Name","value":"name"}}]}},{"kind":"Field","name":{"kind":"Name","value":"implicated_systems"},"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"entity_id"}},{"kind":"Field","name":{"kind":"Name","value":"name"}}]}},{"kind":"Field","name":{"kind":"Name","value":"nlp_similar_incidents"},"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"incident_id"}},{"kind":"Field","name":{"kind":"Name","value":"similarity"}}]}},{"kind":"Field","name":{"kind":"Name","value":"editor_similar_incidents"}},{"kind":"Field","name":{"kind":"Name","value":"editor_dissimilar_incidents"}},{"kind":"Field","name":{"kind":"Name","value":"flagged_dissimilar_incidents"}},{"kind":"Field","name":{"kind":"Name","value":"reports"},"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"submitters"}},{"kind":"Field","name":{"kind":"Name","value":"date_published"}},{"kind":"Field","name":{"kind":"Name","value":"report_number"}},{"kind":"Field","name":{"kind":"Name","value":"title"}},{"kind":"Field","name":{"kind":"Name","value":"description"}},{"kind":"Field","name":{"kind":"Name","value":"url"}},{"kind":"Field","name":{"kind":"Name","value":"image_url"}},{"kind":"Field","name":{"kind":"Name","value":"cloudinary_id"}},{"kind":"Field","name":{"kind":"Name","value":"source_domain"}},{"kind":"Field","name":{"kind":"Name","value":"text"}},{"kind":"Field","name":{"kind":"Name","value":"authors"}},{"kind":"Field","name":{"kind":"Name","value":"epoch_date_submitted"}},{"kind":"Field","name":{"kind":"Name","value":"language"}},{"kind":"Field","name":{"kind":"Name","value":"tags"}},{"kind":"Field","name":{"kind":"Name","value":"inputs_outputs"}}]}},{"kind":"Field","name":{"kind":"Name","value":"embedding"},"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"from_reports"}},{"kind":"Field","name":{"kind":"Name","value":"vector"}}]}},{"kind":"Field","name":{"kind":"Name","value":"editor_notes"}},{"kind":"Field","name":{"kind":"Name","value":"epoch_date_modified"}},{"kind":"Field","name":{"kind":"Name","value":"tsne"},"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"x"}},{"kind":"Field","name":{"kind":"Name","value":"y"}}]}}]}}]}}]} as unknown as DocumentNode; +export const FindIncidentHistoryDocument = {"kind":"Document","definitions":[{"kind":"OperationDefinition","operation":"query","name":{"kind":"Name","value":"FindIncidentHistory"},"variableDefinitions":[{"kind":"VariableDefinition","variable":{"kind":"Variable","name":{"kind":"Name","value":"filter"}},"type":{"kind":"NamedType","name":{"kind":"Name","value":"History_incidentFilterType"}}}],"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"history_incidents"},"arguments":[{"kind":"Argument","name":{"kind":"Name","value":"filter"},"value":{"kind":"Variable","name":{"kind":"Name","value":"filter"}}},{"kind":"Argument","name":{"kind":"Name","value":"sort"},"value":{"kind":"ObjectValue","fields":[{"kind":"ObjectField","name":{"kind":"Name","value":"epoch_date_modified"},"value":{"kind":"EnumValue","value":"DESC"}}]}}],"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"incident_id"}},{"kind":"Field","name":{"kind":"Name","value":"AllegedDeployerOfAISystem"}},{"kind":"Field","name":{"kind":"Name","value":"AllegedDeveloperOfAISystem"}},{"kind":"Field","name":{"kind":"Name","value":"AllegedHarmedOrNearlyHarmedParties"}},{"kind":"Field","name":{"kind":"Name","value":"implicated_systems"}},{"kind":"Field","name":{"kind":"Name","value":"_id"}},{"kind":"Field","name":{"kind":"Name","value":"date"}},{"kind":"Field","name":{"kind":"Name","value":"description"}},{"kind":"Field","name":{"kind":"Name","value":"modifiedBy"}},{"kind":"Field","name":{"kind":"Name","value":"editor_dissimilar_incidents"}},{"kind":"Field","name":{"kind":"Name","value":"editor_notes"}},{"kind":"Field","name":{"kind":"Name","value":"editor_similar_incidents"}},{"kind":"Field","name":{"kind":"Name","value":"editors"}},{"kind":"Field","name":{"kind":"Name","value":"embedding"},"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"from_reports"}},{"kind":"Field","name":{"kind":"Name","value":"vector"}}]}},{"kind":"Field","name":{"kind":"Name","value":"epoch_date_modified"}},{"kind":"Field","name":{"kind":"Name","value":"flagged_dissimilar_incidents"}},{"kind":"Field","name":{"kind":"Name","value":"nlp_similar_incidents"},"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"incident_id"}},{"kind":"Field","name":{"kind":"Name","value":"similarity"}}]}},{"kind":"Field","name":{"kind":"Name","value":"reports"}},{"kind":"Field","name":{"kind":"Name","value":"title"}},{"kind":"Field","name":{"kind":"Name","value":"tsne"},"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"x"}},{"kind":"Field","name":{"kind":"Name","value":"y"}}]}}]}}]}}]} as unknown as DocumentNode; export const FlagIncidentSimilarityDocument = {"kind":"Document","definitions":[{"kind":"OperationDefinition","operation":"mutation","name":{"kind":"Name","value":"FlagIncidentSimilarity"},"variableDefinitions":[{"kind":"VariableDefinition","variable":{"kind":"Variable","name":{"kind":"Name","value":"incidentId"}},"type":{"kind":"NonNullType","type":{"kind":"NamedType","name":{"kind":"Name","value":"Int"}}}},{"kind":"VariableDefinition","variable":{"kind":"Variable","name":{"kind":"Name","value":"dissimilarIds"}},"type":{"kind":"ListType","type":{"kind":"NonNullType","type":{"kind":"NamedType","name":{"kind":"Name","value":"Int"}}}}}],"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"flagIncidentSimilarity"},"arguments":[{"kind":"Argument","name":{"kind":"Name","value":"incidentId"},"value":{"kind":"Variable","name":{"kind":"Name","value":"incidentId"}}},{"kind":"Argument","name":{"kind":"Name","value":"dissimilarIds"},"value":{"kind":"Variable","name":{"kind":"Name","value":"dissimilarIds"}}}],"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"incident_id"}},{"kind":"Field","name":{"kind":"Name","value":"flagged_dissimilar_incidents"}},{"kind":"Field","name":{"kind":"Name","value":"editors"},"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"userId"}}]}}]}}]}}]} as unknown as DocumentNode; export const AllQuickAddDocument = {"kind":"Document","definitions":[{"kind":"OperationDefinition","operation":"query","name":{"kind":"Name","value":"AllQuickAdd"},"variableDefinitions":[{"kind":"VariableDefinition","variable":{"kind":"Variable","name":{"kind":"Name","value":"filter"}},"type":{"kind":"NonNullType","type":{"kind":"NamedType","name":{"kind":"Name","value":"QuickaddFilterType"}}}}],"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"quickadds"},"arguments":[{"kind":"Argument","name":{"kind":"Name","value":"filter"},"value":{"kind":"Variable","name":{"kind":"Name","value":"filter"}}}],"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"_id"}},{"kind":"Field","name":{"kind":"Name","value":"date_submitted"}},{"kind":"Field","name":{"kind":"Name","value":"url"}},{"kind":"Field","name":{"kind":"Name","value":"source_domain"}}]}}]}}]} as unknown as DocumentNode; export const DeleteOneQuickAddDocument = {"kind":"Document","definitions":[{"kind":"OperationDefinition","operation":"mutation","name":{"kind":"Name","value":"DeleteOneQuickAdd"},"variableDefinitions":[{"kind":"VariableDefinition","variable":{"kind":"Variable","name":{"kind":"Name","value":"filter"}},"type":{"kind":"NamedType","name":{"kind":"Name","value":"QuickaddFilterType"}}}],"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"deleteManyQuickadds"},"arguments":[{"kind":"Argument","name":{"kind":"Name","value":"filter"},"value":{"kind":"Variable","name":{"kind":"Name","value":"filter"}}}],"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"deletedCount"}}]}}]}}]} as unknown as DocumentNode; @@ -5639,15 +4990,14 @@ export const FindReportWithTranslationsDocument = {"kind":"Document","definition export const UpdateReportDocument = {"kind":"Document","definitions":[{"kind":"OperationDefinition","operation":"mutation","name":{"kind":"Name","value":"UpdateReport"},"variableDefinitions":[{"kind":"VariableDefinition","variable":{"kind":"Variable","name":{"kind":"Name","value":"filter"}},"type":{"kind":"NonNullType","type":{"kind":"NamedType","name":{"kind":"Name","value":"ReportFilterType"}}}},{"kind":"VariableDefinition","variable":{"kind":"Variable","name":{"kind":"Name","value":"update"}},"type":{"kind":"NonNullType","type":{"kind":"NamedType","name":{"kind":"Name","value":"ReportUpdateType"}}}}],"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"updateOneReport"},"arguments":[{"kind":"Argument","name":{"kind":"Name","value":"filter"},"value":{"kind":"Variable","name":{"kind":"Name","value":"filter"}}},{"kind":"Argument","name":{"kind":"Name","value":"update"},"value":{"kind":"Variable","name":{"kind":"Name","value":"update"}}}],"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"url"}},{"kind":"Field","name":{"kind":"Name","value":"title"}},{"kind":"Field","name":{"kind":"Name","value":"authors"}},{"kind":"Field","name":{"kind":"Name","value":"submitters"}},{"kind":"Field","name":{"kind":"Name","value":"date_published"}},{"kind":"Field","name":{"kind":"Name","value":"date_downloaded"}},{"kind":"Field","name":{"kind":"Name","value":"date_modified"}},{"kind":"Field","name":{"kind":"Name","value":"epoch_date_published"}},{"kind":"Field","name":{"kind":"Name","value":"epoch_date_downloaded"}},{"kind":"Field","name":{"kind":"Name","value":"epoch_date_modified"}},{"kind":"Field","name":{"kind":"Name","value":"image_url"}},{"kind":"Field","name":{"kind":"Name","value":"text"}},{"kind":"Field","name":{"kind":"Name","value":"plain_text"}},{"kind":"Field","name":{"kind":"Name","value":"tags"}},{"kind":"Field","name":{"kind":"Name","value":"flag"}},{"kind":"Field","name":{"kind":"Name","value":"report_number"}},{"kind":"Field","name":{"kind":"Name","value":"editor_notes"}},{"kind":"Field","name":{"kind":"Name","value":"language"}},{"kind":"Field","name":{"kind":"Name","value":"quiet"}}]}}]}}]} as unknown as DocumentNode; export const DeleteOneReportDocument = {"kind":"Document","definitions":[{"kind":"OperationDefinition","operation":"mutation","name":{"kind":"Name","value":"DeleteOneReport"},"variableDefinitions":[{"kind":"VariableDefinition","variable":{"kind":"Variable","name":{"kind":"Name","value":"filter"}},"type":{"kind":"NonNullType","type":{"kind":"NamedType","name":{"kind":"Name","value":"ReportFilterType"}}}}],"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"deleteOneReport"},"arguments":[{"kind":"Argument","name":{"kind":"Name","value":"filter"},"value":{"kind":"Variable","name":{"kind":"Name","value":"filter"}}}],"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"report_number"}}]}}]}}]} as unknown as DocumentNode; export const LinkReportsToIncidentsDocument = {"kind":"Document","definitions":[{"kind":"OperationDefinition","operation":"mutation","name":{"kind":"Name","value":"LinkReportsToIncidents"},"variableDefinitions":[{"kind":"VariableDefinition","variable":{"kind":"Variable","name":{"kind":"Name","value":"input"}},"type":{"kind":"NonNullType","type":{"kind":"NamedType","name":{"kind":"Name","value":"LinkReportsToIncidentsInput"}}}}],"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"linkReportsToIncidents"},"arguments":[{"kind":"Argument","name":{"kind":"Name","value":"input"},"value":{"kind":"Variable","name":{"kind":"Name","value":"input"}}}],"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"incident_id"}},{"kind":"Field","name":{"kind":"Name","value":"reports"},"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"report_number"}}]}}]}}]}}]} as unknown as DocumentNode; -export const LogReportHistoryDocument = {"kind":"Document","definitions":[{"kind":"OperationDefinition","operation":"mutation","name":{"kind":"Name","value":"logReportHistory"},"variableDefinitions":[{"kind":"VariableDefinition","variable":{"kind":"Variable","name":{"kind":"Name","value":"input"}},"type":{"kind":"NonNullType","type":{"kind":"NamedType","name":{"kind":"Name","value":"History_reportInsertInput"}}}}],"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"logReportHistory"},"arguments":[{"kind":"Argument","name":{"kind":"Name","value":"input"},"value":{"kind":"Variable","name":{"kind":"Name","value":"input"}}}],"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"report_number"}}]}}]}}]} as unknown as DocumentNode; -export const FindReportHistoryDocument = {"kind":"Document","definitions":[{"kind":"OperationDefinition","operation":"query","name":{"kind":"Name","value":"FindReportHistory"},"variableDefinitions":[{"kind":"VariableDefinition","variable":{"kind":"Variable","name":{"kind":"Name","value":"query"}},"type":{"kind":"NamedType","name":{"kind":"Name","value":"History_reportQueryInput"}}}],"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"history_reports"},"arguments":[{"kind":"Argument","name":{"kind":"Name","value":"query"},"value":{"kind":"Variable","name":{"kind":"Name","value":"query"}}},{"kind":"Argument","name":{"kind":"Name","value":"sortBy"},"value":{"kind":"EnumValue","value":"EPOCH_DATE_MODIFIED_DESC"}}],"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"_id"}},{"kind":"Field","name":{"kind":"Name","value":"authors"}},{"kind":"Field","name":{"kind":"Name","value":"cloudinary_id"}},{"kind":"Field","name":{"kind":"Name","value":"date_downloaded"}},{"kind":"Field","name":{"kind":"Name","value":"date_modified"}},{"kind":"Field","name":{"kind":"Name","value":"date_published"}},{"kind":"Field","name":{"kind":"Name","value":"date_submitted"}},{"kind":"Field","name":{"kind":"Name","value":"description"}},{"kind":"Field","name":{"kind":"Name","value":"editor_notes"}},{"kind":"Field","name":{"kind":"Name","value":"embedding"},"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"from_text_hash"}},{"kind":"Field","name":{"kind":"Name","value":"vector"}}]}},{"kind":"Field","name":{"kind":"Name","value":"epoch_date_downloaded"}},{"kind":"Field","name":{"kind":"Name","value":"epoch_date_modified"}},{"kind":"Field","name":{"kind":"Name","value":"epoch_date_published"}},{"kind":"Field","name":{"kind":"Name","value":"epoch_date_submitted"}},{"kind":"Field","name":{"kind":"Name","value":"flag"}},{"kind":"Field","name":{"kind":"Name","value":"image_url"}},{"kind":"Field","name":{"kind":"Name","value":"inputs_outputs"}},{"kind":"Field","name":{"kind":"Name","value":"is_incident_report"}},{"kind":"Field","name":{"kind":"Name","value":"language"}},{"kind":"Field","name":{"kind":"Name","value":"modifiedBy"}},{"kind":"Field","name":{"kind":"Name","value":"plain_text"}},{"kind":"Field","name":{"kind":"Name","value":"report_number"}},{"kind":"Field","name":{"kind":"Name","value":"submitters"}},{"kind":"Field","name":{"kind":"Name","value":"tags"}},{"kind":"Field","name":{"kind":"Name","value":"text"}},{"kind":"Field","name":{"kind":"Name","value":"title"}},{"kind":"Field","name":{"kind":"Name","value":"url"}},{"kind":"Field","name":{"kind":"Name","value":"source_domain"}},{"kind":"Field","name":{"kind":"Name","value":"user"}},{"kind":"Field","name":{"kind":"Name","value":"quiet"}}]}}]}}]} as unknown as DocumentNode; +export const FindReportHistoryDocument = {"kind":"Document","definitions":[{"kind":"OperationDefinition","operation":"query","name":{"kind":"Name","value":"FindReportHistory"},"variableDefinitions":[{"kind":"VariableDefinition","variable":{"kind":"Variable","name":{"kind":"Name","value":"filter"}},"type":{"kind":"NamedType","name":{"kind":"Name","value":"History_reportFilterType"}}}],"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"history_reports"},"arguments":[{"kind":"Argument","name":{"kind":"Name","value":"filter"},"value":{"kind":"Variable","name":{"kind":"Name","value":"filter"}}},{"kind":"Argument","name":{"kind":"Name","value":"sort"},"value":{"kind":"ObjectValue","fields":[{"kind":"ObjectField","name":{"kind":"Name","value":"date_modified"},"value":{"kind":"EnumValue","value":"DESC"}}]}}],"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"_id"}},{"kind":"Field","name":{"kind":"Name","value":"authors"}},{"kind":"Field","name":{"kind":"Name","value":"cloudinary_id"}},{"kind":"Field","name":{"kind":"Name","value":"date_downloaded"}},{"kind":"Field","name":{"kind":"Name","value":"date_modified"}},{"kind":"Field","name":{"kind":"Name","value":"date_published"}},{"kind":"Field","name":{"kind":"Name","value":"date_submitted"}},{"kind":"Field","name":{"kind":"Name","value":"description"}},{"kind":"Field","name":{"kind":"Name","value":"editor_notes"}},{"kind":"Field","name":{"kind":"Name","value":"embedding"},"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"from_text_hash"}},{"kind":"Field","name":{"kind":"Name","value":"vector"}}]}},{"kind":"Field","name":{"kind":"Name","value":"epoch_date_downloaded"}},{"kind":"Field","name":{"kind":"Name","value":"epoch_date_modified"}},{"kind":"Field","name":{"kind":"Name","value":"epoch_date_published"}},{"kind":"Field","name":{"kind":"Name","value":"epoch_date_submitted"}},{"kind":"Field","name":{"kind":"Name","value":"flag"}},{"kind":"Field","name":{"kind":"Name","value":"image_url"}},{"kind":"Field","name":{"kind":"Name","value":"inputs_outputs"}},{"kind":"Field","name":{"kind":"Name","value":"is_incident_report"}},{"kind":"Field","name":{"kind":"Name","value":"language"}},{"kind":"Field","name":{"kind":"Name","value":"modifiedBy"}},{"kind":"Field","name":{"kind":"Name","value":"plain_text"}},{"kind":"Field","name":{"kind":"Name","value":"report_number"}},{"kind":"Field","name":{"kind":"Name","value":"submitters"}},{"kind":"Field","name":{"kind":"Name","value":"tags"}},{"kind":"Field","name":{"kind":"Name","value":"text"}},{"kind":"Field","name":{"kind":"Name","value":"title"}},{"kind":"Field","name":{"kind":"Name","value":"url"}},{"kind":"Field","name":{"kind":"Name","value":"source_domain"}},{"kind":"Field","name":{"kind":"Name","value":"user"}},{"kind":"Field","name":{"kind":"Name","value":"quiet"}}]}}]}}]} as unknown as DocumentNode; export const FindReportsDocument = {"kind":"Document","definitions":[{"kind":"OperationDefinition","operation":"query","name":{"kind":"Name","value":"FindReports"},"variableDefinitions":[{"kind":"VariableDefinition","variable":{"kind":"Variable","name":{"kind":"Name","value":"filter"}},"type":{"kind":"NonNullType","type":{"kind":"NamedType","name":{"kind":"Name","value":"ReportFilterType"}}}}],"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"reports"},"arguments":[{"kind":"Argument","name":{"kind":"Name","value":"filter"},"value":{"kind":"Variable","name":{"kind":"Name","value":"filter"}}}],"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"_id"}},{"kind":"Field","name":{"kind":"Name","value":"submitters"}},{"kind":"Field","name":{"kind":"Name","value":"date_published"}},{"kind":"Field","name":{"kind":"Name","value":"report_number"}},{"kind":"Field","name":{"kind":"Name","value":"title"}},{"kind":"Field","name":{"kind":"Name","value":"description"}},{"kind":"Field","name":{"kind":"Name","value":"url"}},{"kind":"Field","name":{"kind":"Name","value":"image_url"}},{"kind":"Field","name":{"kind":"Name","value":"cloudinary_id"}},{"kind":"Field","name":{"kind":"Name","value":"source_domain"}},{"kind":"Field","name":{"kind":"Name","value":"text"}},{"kind":"Field","name":{"kind":"Name","value":"authors"}},{"kind":"Field","name":{"kind":"Name","value":"epoch_date_submitted"}},{"kind":"Field","name":{"kind":"Name","value":"language"}},{"kind":"Field","name":{"kind":"Name","value":"tags"}},{"kind":"Field","name":{"kind":"Name","value":"inputs_outputs"}}]}}]}}]} as unknown as DocumentNode; export const FindReportsTableDocument = {"kind":"Document","definitions":[{"kind":"OperationDefinition","operation":"query","name":{"kind":"Name","value":"FindReportsTable"},"variableDefinitions":[{"kind":"VariableDefinition","variable":{"kind":"Variable","name":{"kind":"Name","value":"filter"}},"type":{"kind":"NonNullType","type":{"kind":"NamedType","name":{"kind":"Name","value":"ReportFilterType"}}}}],"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"reports"},"arguments":[{"kind":"Argument","name":{"kind":"Name","value":"filter"},"value":{"kind":"Variable","name":{"kind":"Name","value":"filter"}}},{"kind":"Argument","name":{"kind":"Name","value":"sort"},"value":{"kind":"ObjectValue","fields":[{"kind":"ObjectField","name":{"kind":"Name","value":"report_number"},"value":{"kind":"EnumValue","value":"DESC"}}]}}],"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"_id"}},{"kind":"Field","name":{"kind":"Name","value":"submitters"}},{"kind":"Field","name":{"kind":"Name","value":"date_published"}},{"kind":"Field","name":{"kind":"Name","value":"date_downloaded"}},{"kind":"Field","name":{"kind":"Name","value":"date_submitted"}},{"kind":"Field","name":{"kind":"Name","value":"date_modified"}},{"kind":"Field","name":{"kind":"Name","value":"report_number"}},{"kind":"Field","name":{"kind":"Name","value":"title"}},{"kind":"Field","name":{"kind":"Name","value":"description"}},{"kind":"Field","name":{"kind":"Name","value":"url"}},{"kind":"Field","name":{"kind":"Name","value":"image_url"}},{"kind":"Field","name":{"kind":"Name","value":"cloudinary_id"}},{"kind":"Field","name":{"kind":"Name","value":"source_domain"}},{"kind":"Field","name":{"kind":"Name","value":"text"}},{"kind":"Field","name":{"kind":"Name","value":"authors"}},{"kind":"Field","name":{"kind":"Name","value":"epoch_date_submitted"}},{"kind":"Field","name":{"kind":"Name","value":"language"}},{"kind":"Field","name":{"kind":"Name","value":"tags"}},{"kind":"Field","name":{"kind":"Name","value":"inputs_outputs"}},{"kind":"Field","name":{"kind":"Name","value":"editor_notes"}},{"kind":"Field","name":{"kind":"Name","value":"is_incident_report"}}]}}]}}]} as unknown as DocumentNode; export const FlagReportDocument = {"kind":"Document","definitions":[{"kind":"OperationDefinition","operation":"mutation","name":{"kind":"Name","value":"FlagReport"},"variableDefinitions":[{"kind":"VariableDefinition","variable":{"kind":"Variable","name":{"kind":"Name","value":"report_number"}},"type":{"kind":"NonNullType","type":{"kind":"NamedType","name":{"kind":"Name","value":"Int"}}}},{"kind":"VariableDefinition","variable":{"kind":"Variable","name":{"kind":"Name","value":"input"}},"type":{"kind":"NonNullType","type":{"kind":"NamedType","name":{"kind":"Name","value":"Boolean"}}}}],"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"flagReport"},"arguments":[{"kind":"Argument","name":{"kind":"Name","value":"report_number"},"value":{"kind":"Variable","name":{"kind":"Name","value":"report_number"}}},{"kind":"Argument","name":{"kind":"Name","value":"input"},"value":{"kind":"Variable","name":{"kind":"Name","value":"input"}}}],"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"report_number"}},{"kind":"Field","name":{"kind":"Name","value":"flag"}},{"kind":"Field","name":{"kind":"Name","value":"date_modified"}},{"kind":"Field","name":{"kind":"Name","value":"epoch_date_modified"}}]}}]}}]} as unknown as DocumentNode; export const DeleteSubmissionDocument = {"kind":"Document","definitions":[{"kind":"OperationDefinition","operation":"mutation","name":{"kind":"Name","value":"DeleteSubmission"},"variableDefinitions":[{"kind":"VariableDefinition","variable":{"kind":"Variable","name":{"kind":"Name","value":"_id"}},"type":{"kind":"NonNullType","type":{"kind":"NamedType","name":{"kind":"Name","value":"ObjectId"}}}}],"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"deleteOneSubmission"},"arguments":[{"kind":"Argument","name":{"kind":"Name","value":"filter"},"value":{"kind":"ObjectValue","fields":[{"kind":"ObjectField","name":{"kind":"Name","value":"_id"},"value":{"kind":"ObjectValue","fields":[{"kind":"ObjectField","name":{"kind":"Name","value":"EQ"},"value":{"kind":"Variable","name":{"kind":"Name","value":"_id"}}}]}}]}}],"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"_id"}}]}}]}}]} as unknown as DocumentNode; -export const FindSubmissionsDocument = {"kind":"Document","definitions":[{"kind":"OperationDefinition","operation":"query","name":{"kind":"Name","value":"FindSubmissions"},"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"submissions"},"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"_id"}},{"kind":"Field","name":{"kind":"Name","value":"cloudinary_id"}},{"kind":"Field","name":{"kind":"Name","value":"date_downloaded"}},{"kind":"Field","name":{"kind":"Name","value":"date_modified"}},{"kind":"Field","name":{"kind":"Name","value":"date_published"}},{"kind":"Field","name":{"kind":"Name","value":"date_submitted"}},{"kind":"Field","name":{"kind":"Name","value":"description"}},{"kind":"Field","name":{"kind":"Name","value":"image_url"}},{"kind":"Field","name":{"kind":"Name","value":"incident_date"}},{"kind":"Field","name":{"kind":"Name","value":"incident_ids"}},{"kind":"Field","name":{"kind":"Name","value":"incident_editors"},"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"first_name"}},{"kind":"Field","name":{"kind":"Name","value":"last_name"}},{"kind":"Field","name":{"kind":"Name","value":"userId"}}]}},{"kind":"Field","name":{"kind":"Name","value":"incident_title"}},{"kind":"Field","name":{"kind":"Name","value":"language"}},{"kind":"Field","name":{"kind":"Name","value":"source_domain"}},{"kind":"Field","name":{"kind":"Name","value":"text"}},{"kind":"Field","name":{"kind":"Name","value":"title"}},{"kind":"Field","name":{"kind":"Name","value":"authors"}},{"kind":"Field","name":{"kind":"Name","value":"submitters"}},{"kind":"Field","name":{"kind":"Name","value":"url"}},{"kind":"Field","name":{"kind":"Name","value":"editor_notes"}},{"kind":"Field","name":{"kind":"Name","value":"tags"}},{"kind":"Field","name":{"kind":"Name","value":"nlp_similar_incidents"},"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"similarity"}},{"kind":"Field","name":{"kind":"Name","value":"incident_id"}}]}},{"kind":"Field","name":{"kind":"Name","value":"editor_similar_incidents"}},{"kind":"Field","name":{"kind":"Name","value":"editor_dissimilar_incidents"}},{"kind":"Field","name":{"kind":"Name","value":"plain_text"}},{"kind":"Field","name":{"kind":"Name","value":"developers"},"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"entity_id"}},{"kind":"Field","name":{"kind":"Name","value":"name"}}]}},{"kind":"Field","name":{"kind":"Name","value":"deployers"},"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"entity_id"}},{"kind":"Field","name":{"kind":"Name","value":"name"}}]}},{"kind":"Field","name":{"kind":"Name","value":"harmed_parties"},"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"entity_id"}},{"kind":"Field","name":{"kind":"Name","value":"name"}}]}},{"kind":"Field","name":{"kind":"Name","value":"status"}},{"kind":"Field","name":{"kind":"Name","value":"user"},"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"userId"}}]}},{"kind":"Field","name":{"kind":"Name","value":"quiet"}}]}}]}}]} as unknown as DocumentNode; -export const FindSubmissionDocument = {"kind":"Document","definitions":[{"kind":"OperationDefinition","operation":"query","name":{"kind":"Name","value":"FindSubmission"},"variableDefinitions":[{"kind":"VariableDefinition","variable":{"kind":"Variable","name":{"kind":"Name","value":"filter"}},"type":{"kind":"NonNullType","type":{"kind":"NamedType","name":{"kind":"Name","value":"SubmissionFilterType"}}}}],"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"submission"},"arguments":[{"kind":"Argument","name":{"kind":"Name","value":"filter"},"value":{"kind":"Variable","name":{"kind":"Name","value":"filter"}}}],"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"_id"}},{"kind":"Field","name":{"kind":"Name","value":"cloudinary_id"}},{"kind":"Field","name":{"kind":"Name","value":"date_downloaded"}},{"kind":"Field","name":{"kind":"Name","value":"date_modified"}},{"kind":"Field","name":{"kind":"Name","value":"date_published"}},{"kind":"Field","name":{"kind":"Name","value":"date_submitted"}},{"kind":"Field","name":{"kind":"Name","value":"description"}},{"kind":"Field","name":{"kind":"Name","value":"image_url"}},{"kind":"Field","name":{"kind":"Name","value":"incident_date"}},{"kind":"Field","name":{"kind":"Name","value":"incident_ids"}},{"kind":"Field","name":{"kind":"Name","value":"incident_editors"},"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"first_name"}},{"kind":"Field","name":{"kind":"Name","value":"last_name"}},{"kind":"Field","name":{"kind":"Name","value":"userId"}}]}},{"kind":"Field","name":{"kind":"Name","value":"incident_title"}},{"kind":"Field","name":{"kind":"Name","value":"language"}},{"kind":"Field","name":{"kind":"Name","value":"source_domain"}},{"kind":"Field","name":{"kind":"Name","value":"text"}},{"kind":"Field","name":{"kind":"Name","value":"title"}},{"kind":"Field","name":{"kind":"Name","value":"authors"}},{"kind":"Field","name":{"kind":"Name","value":"submitters"}},{"kind":"Field","name":{"kind":"Name","value":"url"}},{"kind":"Field","name":{"kind":"Name","value":"editor_notes"}},{"kind":"Field","name":{"kind":"Name","value":"tags"}},{"kind":"Field","name":{"kind":"Name","value":"developers"},"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"entity_id"}},{"kind":"Field","name":{"kind":"Name","value":"name"}}]}},{"kind":"Field","name":{"kind":"Name","value":"deployers"},"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"entity_id"}},{"kind":"Field","name":{"kind":"Name","value":"name"}}]}},{"kind":"Field","name":{"kind":"Name","value":"harmed_parties"},"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"entity_id"}},{"kind":"Field","name":{"kind":"Name","value":"name"}}]}},{"kind":"Field","name":{"kind":"Name","value":"nlp_similar_incidents"},"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"similarity"}},{"kind":"Field","name":{"kind":"Name","value":"incident_id"}}]}},{"kind":"Field","name":{"kind":"Name","value":"editor_similar_incidents"}},{"kind":"Field","name":{"kind":"Name","value":"editor_dissimilar_incidents"}},{"kind":"Field","name":{"kind":"Name","value":"status"}},{"kind":"Field","name":{"kind":"Name","value":"quiet"}}]}}]}}]} as unknown as DocumentNode; -export const UpdateSubmissionDocument = {"kind":"Document","definitions":[{"kind":"OperationDefinition","operation":"mutation","name":{"kind":"Name","value":"UpdateSubmission"},"variableDefinitions":[{"kind":"VariableDefinition","variable":{"kind":"Variable","name":{"kind":"Name","value":"filter"}},"type":{"kind":"NonNullType","type":{"kind":"NamedType","name":{"kind":"Name","value":"SubmissionFilterType"}}}},{"kind":"VariableDefinition","variable":{"kind":"Variable","name":{"kind":"Name","value":"update"}},"type":{"kind":"NonNullType","type":{"kind":"NamedType","name":{"kind":"Name","value":"SubmissionUpdateType"}}}}],"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"updateOneSubmission"},"arguments":[{"kind":"Argument","name":{"kind":"Name","value":"filter"},"value":{"kind":"Variable","name":{"kind":"Name","value":"filter"}}},{"kind":"Argument","name":{"kind":"Name","value":"update"},"value":{"kind":"Variable","name":{"kind":"Name","value":"update"}}}],"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"_id"}},{"kind":"Field","name":{"kind":"Name","value":"cloudinary_id"}},{"kind":"Field","name":{"kind":"Name","value":"date_downloaded"}},{"kind":"Field","name":{"kind":"Name","value":"date_modified"}},{"kind":"Field","name":{"kind":"Name","value":"date_published"}},{"kind":"Field","name":{"kind":"Name","value":"date_submitted"}},{"kind":"Field","name":{"kind":"Name","value":"description"}},{"kind":"Field","name":{"kind":"Name","value":"image_url"}},{"kind":"Field","name":{"kind":"Name","value":"incident_date"}},{"kind":"Field","name":{"kind":"Name","value":"incident_ids"}},{"kind":"Field","name":{"kind":"Name","value":"incident_editors"},"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"first_name"}},{"kind":"Field","name":{"kind":"Name","value":"last_name"}},{"kind":"Field","name":{"kind":"Name","value":"userId"}}]}},{"kind":"Field","name":{"kind":"Name","value":"incident_title"}},{"kind":"Field","name":{"kind":"Name","value":"language"}},{"kind":"Field","name":{"kind":"Name","value":"source_domain"}},{"kind":"Field","name":{"kind":"Name","value":"text"}},{"kind":"Field","name":{"kind":"Name","value":"title"}},{"kind":"Field","name":{"kind":"Name","value":"authors"}},{"kind":"Field","name":{"kind":"Name","value":"submitters"}},{"kind":"Field","name":{"kind":"Name","value":"url"}},{"kind":"Field","name":{"kind":"Name","value":"editor_notes"}},{"kind":"Field","name":{"kind":"Name","value":"tags"}},{"kind":"Field","name":{"kind":"Name","value":"developers"},"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"entity_id"}},{"kind":"Field","name":{"kind":"Name","value":"name"}}]}},{"kind":"Field","name":{"kind":"Name","value":"deployers"},"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"entity_id"}},{"kind":"Field","name":{"kind":"Name","value":"name"}}]}},{"kind":"Field","name":{"kind":"Name","value":"harmed_parties"},"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"entity_id"}},{"kind":"Field","name":{"kind":"Name","value":"name"}}]}},{"kind":"Field","name":{"kind":"Name","value":"nlp_similar_incidents"},"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"similarity"}},{"kind":"Field","name":{"kind":"Name","value":"incident_id"}}]}},{"kind":"Field","name":{"kind":"Name","value":"editor_similar_incidents"}},{"kind":"Field","name":{"kind":"Name","value":"editor_dissimilar_incidents"}}]}}]}}]} as unknown as DocumentNode; +export const FindSubmissionsDocument = {"kind":"Document","definitions":[{"kind":"OperationDefinition","operation":"query","name":{"kind":"Name","value":"FindSubmissions"},"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"submissions"},"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"_id"}},{"kind":"Field","name":{"kind":"Name","value":"cloudinary_id"}},{"kind":"Field","name":{"kind":"Name","value":"date_downloaded"}},{"kind":"Field","name":{"kind":"Name","value":"date_modified"}},{"kind":"Field","name":{"kind":"Name","value":"date_published"}},{"kind":"Field","name":{"kind":"Name","value":"date_submitted"}},{"kind":"Field","name":{"kind":"Name","value":"description"}},{"kind":"Field","name":{"kind":"Name","value":"image_url"}},{"kind":"Field","name":{"kind":"Name","value":"incident_date"}},{"kind":"Field","name":{"kind":"Name","value":"incident_ids"}},{"kind":"Field","name":{"kind":"Name","value":"incident_editors"},"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"first_name"}},{"kind":"Field","name":{"kind":"Name","value":"last_name"}},{"kind":"Field","name":{"kind":"Name","value":"userId"}}]}},{"kind":"Field","name":{"kind":"Name","value":"incident_title"}},{"kind":"Field","name":{"kind":"Name","value":"language"}},{"kind":"Field","name":{"kind":"Name","value":"source_domain"}},{"kind":"Field","name":{"kind":"Name","value":"text"}},{"kind":"Field","name":{"kind":"Name","value":"title"}},{"kind":"Field","name":{"kind":"Name","value":"authors"}},{"kind":"Field","name":{"kind":"Name","value":"submitters"}},{"kind":"Field","name":{"kind":"Name","value":"url"}},{"kind":"Field","name":{"kind":"Name","value":"editor_notes"}},{"kind":"Field","name":{"kind":"Name","value":"tags"}},{"kind":"Field","name":{"kind":"Name","value":"nlp_similar_incidents"},"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"similarity"}},{"kind":"Field","name":{"kind":"Name","value":"incident_id"}}]}},{"kind":"Field","name":{"kind":"Name","value":"editor_similar_incidents"}},{"kind":"Field","name":{"kind":"Name","value":"editor_dissimilar_incidents"}},{"kind":"Field","name":{"kind":"Name","value":"plain_text"}},{"kind":"Field","name":{"kind":"Name","value":"developers"},"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"entity_id"}},{"kind":"Field","name":{"kind":"Name","value":"name"}}]}},{"kind":"Field","name":{"kind":"Name","value":"deployers"},"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"entity_id"}},{"kind":"Field","name":{"kind":"Name","value":"name"}}]}},{"kind":"Field","name":{"kind":"Name","value":"harmed_parties"},"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"entity_id"}},{"kind":"Field","name":{"kind":"Name","value":"name"}}]}},{"kind":"Field","name":{"kind":"Name","value":"status"}},{"kind":"Field","name":{"kind":"Name","value":"user"},"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"userId"}}]}},{"kind":"Field","name":{"kind":"Name","value":"quiet"}},{"kind":"Field","name":{"kind":"Name","value":"implicated_systems"},"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"entity_id"}},{"kind":"Field","name":{"kind":"Name","value":"name"}}]}}]}}]}}]} as unknown as DocumentNode; +export const FindSubmissionDocument = {"kind":"Document","definitions":[{"kind":"OperationDefinition","operation":"query","name":{"kind":"Name","value":"FindSubmission"},"variableDefinitions":[{"kind":"VariableDefinition","variable":{"kind":"Variable","name":{"kind":"Name","value":"filter"}},"type":{"kind":"NonNullType","type":{"kind":"NamedType","name":{"kind":"Name","value":"SubmissionFilterType"}}}}],"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"submission"},"arguments":[{"kind":"Argument","name":{"kind":"Name","value":"filter"},"value":{"kind":"Variable","name":{"kind":"Name","value":"filter"}}}],"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"_id"}},{"kind":"Field","name":{"kind":"Name","value":"cloudinary_id"}},{"kind":"Field","name":{"kind":"Name","value":"date_downloaded"}},{"kind":"Field","name":{"kind":"Name","value":"date_modified"}},{"kind":"Field","name":{"kind":"Name","value":"date_published"}},{"kind":"Field","name":{"kind":"Name","value":"date_submitted"}},{"kind":"Field","name":{"kind":"Name","value":"description"}},{"kind":"Field","name":{"kind":"Name","value":"image_url"}},{"kind":"Field","name":{"kind":"Name","value":"incident_date"}},{"kind":"Field","name":{"kind":"Name","value":"incident_ids"}},{"kind":"Field","name":{"kind":"Name","value":"incident_editors"},"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"first_name"}},{"kind":"Field","name":{"kind":"Name","value":"last_name"}},{"kind":"Field","name":{"kind":"Name","value":"userId"}}]}},{"kind":"Field","name":{"kind":"Name","value":"incident_title"}},{"kind":"Field","name":{"kind":"Name","value":"language"}},{"kind":"Field","name":{"kind":"Name","value":"source_domain"}},{"kind":"Field","name":{"kind":"Name","value":"text"}},{"kind":"Field","name":{"kind":"Name","value":"title"}},{"kind":"Field","name":{"kind":"Name","value":"authors"}},{"kind":"Field","name":{"kind":"Name","value":"submitters"}},{"kind":"Field","name":{"kind":"Name","value":"url"}},{"kind":"Field","name":{"kind":"Name","value":"editor_notes"}},{"kind":"Field","name":{"kind":"Name","value":"tags"}},{"kind":"Field","name":{"kind":"Name","value":"developers"},"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"entity_id"}},{"kind":"Field","name":{"kind":"Name","value":"name"}}]}},{"kind":"Field","name":{"kind":"Name","value":"deployers"},"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"entity_id"}},{"kind":"Field","name":{"kind":"Name","value":"name"}}]}},{"kind":"Field","name":{"kind":"Name","value":"harmed_parties"},"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"entity_id"}},{"kind":"Field","name":{"kind":"Name","value":"name"}}]}},{"kind":"Field","name":{"kind":"Name","value":"nlp_similar_incidents"},"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"similarity"}},{"kind":"Field","name":{"kind":"Name","value":"incident_id"}}]}},{"kind":"Field","name":{"kind":"Name","value":"editor_similar_incidents"}},{"kind":"Field","name":{"kind":"Name","value":"editor_dissimilar_incidents"}},{"kind":"Field","name":{"kind":"Name","value":"status"}},{"kind":"Field","name":{"kind":"Name","value":"quiet"}},{"kind":"Field","name":{"kind":"Name","value":"implicated_systems"},"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"entity_id"}},{"kind":"Field","name":{"kind":"Name","value":"name"}}]}}]}}]}}]} as unknown as DocumentNode; +export const UpdateSubmissionDocument = {"kind":"Document","definitions":[{"kind":"OperationDefinition","operation":"mutation","name":{"kind":"Name","value":"UpdateSubmission"},"variableDefinitions":[{"kind":"VariableDefinition","variable":{"kind":"Variable","name":{"kind":"Name","value":"filter"}},"type":{"kind":"NonNullType","type":{"kind":"NamedType","name":{"kind":"Name","value":"SubmissionFilterType"}}}},{"kind":"VariableDefinition","variable":{"kind":"Variable","name":{"kind":"Name","value":"update"}},"type":{"kind":"NonNullType","type":{"kind":"NamedType","name":{"kind":"Name","value":"SubmissionUpdateType"}}}}],"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"updateOneSubmission"},"arguments":[{"kind":"Argument","name":{"kind":"Name","value":"filter"},"value":{"kind":"Variable","name":{"kind":"Name","value":"filter"}}},{"kind":"Argument","name":{"kind":"Name","value":"update"},"value":{"kind":"Variable","name":{"kind":"Name","value":"update"}}}],"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"_id"}},{"kind":"Field","name":{"kind":"Name","value":"cloudinary_id"}},{"kind":"Field","name":{"kind":"Name","value":"date_downloaded"}},{"kind":"Field","name":{"kind":"Name","value":"date_modified"}},{"kind":"Field","name":{"kind":"Name","value":"date_published"}},{"kind":"Field","name":{"kind":"Name","value":"date_submitted"}},{"kind":"Field","name":{"kind":"Name","value":"description"}},{"kind":"Field","name":{"kind":"Name","value":"image_url"}},{"kind":"Field","name":{"kind":"Name","value":"incident_date"}},{"kind":"Field","name":{"kind":"Name","value":"incident_ids"}},{"kind":"Field","name":{"kind":"Name","value":"incident_editors"},"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"first_name"}},{"kind":"Field","name":{"kind":"Name","value":"last_name"}},{"kind":"Field","name":{"kind":"Name","value":"userId"}}]}},{"kind":"Field","name":{"kind":"Name","value":"incident_title"}},{"kind":"Field","name":{"kind":"Name","value":"language"}},{"kind":"Field","name":{"kind":"Name","value":"source_domain"}},{"kind":"Field","name":{"kind":"Name","value":"text"}},{"kind":"Field","name":{"kind":"Name","value":"title"}},{"kind":"Field","name":{"kind":"Name","value":"authors"}},{"kind":"Field","name":{"kind":"Name","value":"submitters"}},{"kind":"Field","name":{"kind":"Name","value":"url"}},{"kind":"Field","name":{"kind":"Name","value":"editor_notes"}},{"kind":"Field","name":{"kind":"Name","value":"tags"}},{"kind":"Field","name":{"kind":"Name","value":"developers"},"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"entity_id"}},{"kind":"Field","name":{"kind":"Name","value":"name"}}]}},{"kind":"Field","name":{"kind":"Name","value":"deployers"},"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"entity_id"}},{"kind":"Field","name":{"kind":"Name","value":"name"}}]}},{"kind":"Field","name":{"kind":"Name","value":"harmed_parties"},"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"entity_id"}},{"kind":"Field","name":{"kind":"Name","value":"name"}}]}},{"kind":"Field","name":{"kind":"Name","value":"nlp_similar_incidents"},"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"similarity"}},{"kind":"Field","name":{"kind":"Name","value":"incident_id"}}]}},{"kind":"Field","name":{"kind":"Name","value":"editor_similar_incidents"}},{"kind":"Field","name":{"kind":"Name","value":"editor_dissimilar_incidents"}},{"kind":"Field","name":{"kind":"Name","value":"implicated_systems"},"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"entity_id"}},{"kind":"Field","name":{"kind":"Name","value":"name"}}]}}]}}]}}]} as unknown as DocumentNode; export const InsertSubmissionDocument = {"kind":"Document","definitions":[{"kind":"OperationDefinition","operation":"mutation","name":{"kind":"Name","value":"InsertSubmission"},"variableDefinitions":[{"kind":"VariableDefinition","variable":{"kind":"Variable","name":{"kind":"Name","value":"data"}},"type":{"kind":"NonNullType","type":{"kind":"NamedType","name":{"kind":"Name","value":"SubmissionInsertType"}}}}],"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"insertOneSubmission"},"arguments":[{"kind":"Argument","name":{"kind":"Name","value":"data"},"value":{"kind":"Variable","name":{"kind":"Name","value":"data"}}}],"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"_id"}}]}}]}}]} as unknown as DocumentNode; export const PromoteSubmissionDocument = {"kind":"Document","definitions":[{"kind":"OperationDefinition","operation":"mutation","name":{"kind":"Name","value":"PromoteSubmission"},"variableDefinitions":[{"kind":"VariableDefinition","variable":{"kind":"Variable","name":{"kind":"Name","value":"input"}},"type":{"kind":"NonNullType","type":{"kind":"NamedType","name":{"kind":"Name","value":"PromoteSubmissionToReportInput"}}}}],"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"promoteSubmissionToReport"},"arguments":[{"kind":"Argument","name":{"kind":"Name","value":"input"},"value":{"kind":"Variable","name":{"kind":"Name","value":"input"}}}],"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"incident_ids"}},{"kind":"Field","name":{"kind":"Name","value":"report_number"}}]}}]}}]} as unknown as DocumentNode; export const UpsertSubscriptionDocument = {"kind":"Document","definitions":[{"kind":"OperationDefinition","operation":"mutation","name":{"kind":"Name","value":"UpsertSubscription"},"variableDefinitions":[{"kind":"VariableDefinition","variable":{"kind":"Variable","name":{"kind":"Name","value":"filter"}},"type":{"kind":"NonNullType","type":{"kind":"NamedType","name":{"kind":"Name","value":"SubscriptionFilterType"}}}},{"kind":"VariableDefinition","variable":{"kind":"Variable","name":{"kind":"Name","value":"update"}},"type":{"kind":"NonNullType","type":{"kind":"NamedType","name":{"kind":"Name","value":"SubscriptionInsertType"}}}}],"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"upsertOneSubscription"},"arguments":[{"kind":"Argument","name":{"kind":"Name","value":"filter"},"value":{"kind":"Variable","name":{"kind":"Name","value":"filter"}}},{"kind":"Argument","name":{"kind":"Name","value":"update"},"value":{"kind":"Variable","name":{"kind":"Name","value":"update"}}}],"selectionSet":{"kind":"SelectionSet","selections":[{"kind":"Field","name":{"kind":"Name","value":"_id"}}]}}]}}]} as unknown as DocumentNode; diff --git a/site/gatsby-site/server/interfaces.ts b/site/gatsby-site/server/interfaces.ts index 98965de2b6..ea48abd00f 100644 --- a/site/gatsby-site/server/interfaces.ts +++ b/site/gatsby-site/server/interfaces.ts @@ -1,47 +1,53 @@ import { MongoClient } from 'mongodb'; -import { Classification, Duplicate, Entity, Incident, Report, Submission, Subscription, User, Notification } from './generated/graphql'; +import { Classification, Duplicate, Entity, Incident, Report, Submission, Subscription, User, Notification, History_Report, History_Incident } from './generated/graphql'; import { IncomingMessage } from 'http'; export interface Context { - user: { - id: string, - roles: string[], - } | null, - req: IncomingMessage, - client: MongoClient, + user: { + id: string, + roles: string[], + } | null, + req: IncomingMessage, + client: MongoClient, } -export type DBIncident = Omit - & { "Alleged deployer of AI system": string[], "Alleged developer of AI system": string[], "Alleged harmed or nearly harmed parties": string[] } - & { reports: number[] } - & { editors: string[] } +export type DBIncident = Omit + & { "Alleged deployer of AI system": string[], "Alleged developer of AI system": string[], "Alleged harmed or nearly harmed parties": string[], implicated_systems: string[] } + & { reports: number[] } + & { editors: string[] } + +export type DBIncidentHistory = Omit + & { "Alleged deployer of AI system": string[], "Alleged developer of AI system": string[], "Alleged harmed or nearly harmed parties": string[], implicated_systems: string[] }; export type DBEntity = Entity; export type DBDuplicate = Duplicate; export type DBClassification = Omit - & { incidents: number[] } - & { reports: number[] } + & { incidents: number[] } + & { reports: number[] } export type DBReport = Omit - & { user: string } + & { user: string } + +export type DBReportHistory = Omit; export type DBUser = Omit; -export type DBSubmission = Omit - & { developers: string[] } - & { deployers: string[] } - & { harmed_parties: string[] } - & { user: string } - & { incident_editors: string[] } +export type DBSubmission = Omit + & { developers: string[] } + & { deployers: string[] } + & { harmed_parties: string[] } + & { user: string } + & { incident_editors: string[] } + & { implicated_systems: string[] } export type SubscriptionTypes = 'incident' | 'new-incidents' | 'entity' | 'submission-promoted'; export type DBSubscription = Omit - & { entityId?: string, incident_id?: number, userId: string, type: SubscriptionTypes }; + & { entityId?: string, incident_id?: number, userId: string, type: SubscriptionTypes }; export type NotificationTypes = 'new-report-incident' | 'incident-updated' | 'entity' | 'new-incidents' | 'submission-promoted' -export type DBNotification = Omit & { userId?: string, type: NotificationTypes } \ No newline at end of file +export type DBNotification = Omit & { userId?: string, type: NotificationTypes } diff --git a/site/gatsby-site/server/local.ts b/site/gatsby-site/server/local.ts index bc342e7a74..044a27d63a 100644 --- a/site/gatsby-site/server/local.ts +++ b/site/gatsby-site/server/local.ts @@ -50,6 +50,12 @@ import { permissions as taxaPermissions } from './fields/taxa'; +import { + queryFields as candidatesQueryFields, + mutationFields as candidatesMutationFields, + permissions as candidatesPermissions +} from './fields/candidates'; + import { queryFields as subscriptionsQueryFields, mutationFields as subscriptionsMutationFields, @@ -64,10 +70,19 @@ import { import { queryFields as notificationsQueryFields, - mutationFields as notificationsMutationFields, permissions as notificationsPermissions } from './fields/notifications'; +import { + queryFields as reportsHistoryQueryFields, + permissions as reportsHistoryPermissions +} from './fields/reportsHistory'; + +import { + queryFields as incidentsHistoryQueryFields, + permissions as incidentsHistoryPermissions, +} from './fields/incidentsHistory'; + export const getSchema = () => { @@ -86,9 +101,12 @@ export const getSchema = () => { ...submissionsQueryFields, ...classificationsQueryFields, ...taxaQueryFields, + ...candidatesQueryFields, ...subscriptionsQueryFields, ...duplicatesQueryFields, ...notificationsQueryFields, + ...reportsHistoryQueryFields, + ...incidentsHistoryQueryFields, } }); @@ -102,9 +120,9 @@ export const getSchema = () => { ...usersMutationFields, ...submissionsMutationFields, ...classificationsMutationFields, + ...candidatesMutationFields, ...subscriptionsMutationFields, ...duplicatesMutationFields, - ...notificationsMutationFields, } }); @@ -139,9 +157,12 @@ export const getSchema = () => { ...submissionsPermissions.Query, ...classificationsPermissions.Query, ...taxaPermissions.Query, + ...candidatesPermissions.Query, ...subscriptionsPermissions.Query, ...duplicatesPermissions.Query, ...notificationsPermissions.Query, + ...reportsHistoryPermissions.Query, + ...incidentsHistoryPermissions.Query, }, Mutation: { "*": deny, @@ -152,9 +173,9 @@ export const getSchema = () => { ...usersPermissions.Mutation, ...submissionsPermissions.Mutation, ...classificationsPermissions.Mutation, + ...candidatesPermissions.Mutation, ...subscriptionsPermissions.Mutation, ...duplicatesPermissions.Mutation, - ...notificationsPermissions.Mutation, }, }, { diff --git a/site/gatsby-site/server/remote.ts b/site/gatsby-site/server/remote.ts index 6c43fe419f..c850c0dd72 100644 --- a/site/gatsby-site/server/remote.ts +++ b/site/gatsby-site/server/remote.ts @@ -85,6 +85,10 @@ const ignoreTypes = [ 'TaxaUpdateInput', 'TaxaInsertInput', + 'Candidate', + 'CandidateQueryInput', + 'CandidateUpdateInput', + 'CandidateInsertInput', 'Subscription', 'SubscriptionQueryInput', 'SubscriptionUpdateInput', @@ -94,6 +98,16 @@ const ignoreTypes = [ 'DuplicateQueryInput', 'DuplicateUpdateInput', 'DuplicateInsertInput', + + 'History_report', + 'History_reportQueryInput', + 'History_reportUpdateInput', + 'History_reportInsertInput', + + 'History_incident', + 'History_incidentQueryInput', + 'History_incidentUpdateInput', + 'History_incidentInsertInput', ]; const ignoredQueries = [ @@ -120,6 +134,9 @@ const ignoredQueries = [ 'taxa', 'taxas', + + 'candidate', + 'candidates', 'subscription', 'subscriptions', @@ -129,6 +146,12 @@ const ignoredQueries = [ 'notification', 'notifications', + + 'history_report', + 'history_reports', + + 'history_incident', + 'history_incidents', ]; const ignoredMutations = [ @@ -212,6 +235,16 @@ const ignoredMutations = [ 'updateOneTaxa', 'updateManyTaxas', 'upsertOneTaxa', + 'upsertManyTaxas', + + 'deleteOneCandidates', + 'deleteManyCandidates', + 'insertOneCandidate', + 'insertManyCandidates', + 'updateOneCandidate', + 'updateManyCandidates', + 'upsertOneCandidate', + 'upsertManyCandidates', 'deleteOneSubscription', 'deleteManySubscriptions', diff --git a/site/gatsby-site/server/remoteTypeDefs.ts b/site/gatsby-site/server/remoteTypeDefs.ts index 8e1aa81cd4..f3da6e0f3c 100644 --- a/site/gatsby-site/server/remoteTypeDefs.ts +++ b/site/gatsby-site/server/remoteTypeDefs.ts @@ -1,2585 +1,2470 @@ import gql from "graphql-tag"; export default gql` -enum IncidentSortByInput { - INCIDENT_ID_ASC - DATE_ASC - EDITOR_NOTES_ASC - EPOCH_DATE_MODIFIED_ASC - EPOCH_DATE_MODIFIED_DESC - _ID_DESC - DATE_DESC - EDITOR_NOTES_DESC - DESCRIPTION_DESC - INCIDENT_ID_DESC - TITLE_ASC - _ID_ASC - DESCRIPTION_ASC - TITLE_DESC -} - -type ChecklistRiskPrecedent { - description: String - incident_id: Int - tags: [String] - title: String +type DefaultAdminUser { + message: String + status: Int + userId: String } -input History_reportInsertInput { - text: String! - title: String! - user: String - epoch_date_downloaded: Int! - embedding: History_reportEmbeddingInsertInput - cloudinary_id: String! - date_downloaded: DateTime! - quiet: Boolean - epoch_date_submitted: Int! - inputs_outputs: [String] - flag: Boolean - is_incident_report: Boolean - _id: ObjectId - source_domain: String! - description: String - submitters: [String]! - tags: [String]! - date_modified: DateTime! - epoch_date_published: Int! - authors: [String]! - image_url: String! - date_published: DateTime! - language: String! - plain_text: String! - editor_notes: String - modifiedBy: String - epoch_date_modified: Int! - date_submitted: DateTime! - report_number: Int! - url: String! +input SubmissionEmbeddingQueryInput { + AND: [SubmissionEmbeddingQueryInput!] + from_text_hash_lt: String + from_text_hash_gte: String + vector_in: [Float] + from_text_hash_gt: String + from_text_hash_lte: String + OR: [SubmissionEmbeddingQueryInput!] + vector: [Float] + from_text_hash_nin: [String] + from_text_hash_ne: String + from_text_hash_in: [String] + vector_nin: [Float] + from_text_hash_exists: Boolean + vector_exists: Boolean + from_text_hash: String } -input CreateVariantInput { - variant: CreateVariantInputVariant - incidentId: Int +input TaxaField_listComplete_fromQueryInput { + all_exists: Boolean + current_in: [String] + current_exists: Boolean + AND: [TaxaField_listComplete_fromQueryInput!] + all_in: [String] + current: [String] + OR: [TaxaField_listComplete_fromQueryInput!] + all: [String] + all_nin: [String] + current_nin: [String] } -type RisksPayloadPrecedentNlp_similar_incident { +input ChecklistRiskPrecedentQueryInput { + description_in: [String] + incident_id_lt: Int + title: String + incident_id_lte: Int + incident_id_exists: Boolean + description_lte: String + description_lt: String + incident_id_ne: Int + incident_id_nin: [Int] + OR: [ChecklistRiskPrecedentQueryInput!] + incident_id_gt: Int + title_gt: String + title_ne: String incident_id: Int - similarity: Float -} - -input UserQueryInput { - _id_exists: Boolean - last_name_in: [String] - first_name_ne: String - roles_nin: [String] - last_name_lt: String - last_name_gt: String - first_name_lte: String - last_name_nin: [String] - userId: String - _id_lt: ObjectId - first_name_in: [String] - _id_in: [ObjectId] - userId_lte: String - last_name_gte: String - first_name: String - userId_gte: String - _id_gte: ObjectId - first_name_exists: Boolean - OR: [UserQueryInput!] - roles_in: [String] - roles: [String] - userId_gt: String - _id_nin: [ObjectId] - last_name_lte: String - userId_lt: String - last_name_ne: String - userId_nin: [String] - userId_exists: Boolean - first_name_nin: [String] - first_name_gt: String - _id: ObjectId - _id_lte: ObjectId - first_name_gte: String - last_name: String - last_name_exists: Boolean - AND: [UserQueryInput!] - userId_in: [String] - userId_ne: String - _id_gt: ObjectId - _id_ne: ObjectId - roles_exists: Boolean - first_name_lt: String + title_nin: [String] + title_exists: Boolean + incident_id_gte: Int + description_exists: Boolean + tags_nin: [String] + title_lt: String + title_in: [String] + tags: [String] + title_lte: String + tags_exists: Boolean + description_ne: String + description_gte: String + description: String + description_nin: [String] + title_gte: String + AND: [ChecklistRiskPrecedentQueryInput!] + tags_in: [String] + description_gt: String + incident_id_in: [Int] } -enum EntitySortByInput { - DATE_MODIFIED_DESC +enum ChecklistSortByInput { + DATE_UPDATED_DESC + ENTITY_ID_ASC + OWNER_ID_ASC + ID_DESC NAME_ASC _ID_ASC - CREATED_AT_DESC - DATE_MODIFIED_ASC + DATE_CREATED_ASC + ID_ASC + DATE_CREATED_DESC + DATE_UPDATED_ASC ENTITY_ID_DESC NAME_DESC + OWNER_ID_DESC _ID_DESC - CREATED_AT_ASC - ENTITY_ID_ASC + ABOUT_ASC + ABOUT_DESC } -input TaxaField_listComplete_fromQueryInput { - current: [String] - current_exists: Boolean - current_nin: [String] - all: [String] - all_exists: Boolean - all_in: [String] - AND: [TaxaField_listComplete_fromQueryInput!] - OR: [TaxaField_listComplete_fromQueryInput!] - all_nin: [String] - current_in: [String] +input IncidentAllegedDeveloperOfAISystemRelationInput { + link: [String] + create: [EntityInsertInput] } -type TaxaField_listItem_fieldComplete_from { - all: [String] - current: [String] - entities: Boolean +input IncidentImplicatedSystemsRelationInput { + link: [String] + create: [EntityInsertInput] } -type DefaultAdminUser { - message: String - status: Int - userId: String +input ClassificationAttributeInsertInput { + short_name: String + value_json: String } -input NotificationInsertInput { - userId: NotificationUserIdRelationInput - _id: ObjectId +input ChecklistRiskPrecedentUpdateInput { + description: String incident_id: Int - processed: Boolean - sentDate: DateTime - type: String -} - -input NotificationUserIdRelationInput { - create: UserInsertInput - link: String + title: String + incident_id_inc: Int + incident_id_unset: Boolean + tags_unset: Boolean + tags: [String] + title_unset: Boolean + description_unset: Boolean } -type History_incidentNlp_similar_incident { +type SubmissionNlp_similar_incident { incident_id: Int similarity: Float } -input ReportInsertInput { - plain_text: String! - report_number: Int! - epoch_date_submitted: Int! - quiet: Boolean - epoch_date_published: Int! - source_domain: String! - _id: ObjectId - inputs_outputs: [String] - date_submitted: DateTime! - flag: Boolean - image_url: String! - description: String - embedding: ReportEmbeddingInsertInput - user: ReportUserRelationInput - text: String! - date_modified: DateTime! - submitters: [String]! - is_incident_report: Boolean - cloudinary_id: String! - url: String! - tags: [String]! - title: String! - date_downloaded: DateTime! - epoch_date_modified: Int! - editor_notes: String - date_published: DateTime! - authors: [String]! - epoch_date_downloaded: Int! - language: String! -} - -type Subscription { - _id: ObjectId - entityId: Entity - incident_id: Incident - type: String! - userId: User! +input ReportEmbeddingQueryInput { + from_text_hash_lte: String + vector_in: [Float] + AND: [ReportEmbeddingQueryInput!] + from_text_hash_nin: [String] + vector: [Float] + from_text_hash_in: [String] + vector_nin: [Float] + vector_exists: Boolean + OR: [ReportEmbeddingQueryInput!] + from_text_hash_gte: String + from_text_hash_gt: String + from_text_hash: String + from_text_hash_lt: String + from_text_hash_ne: String + from_text_hash_exists: Boolean } -type Mutation { - createDefaultAdminUser(input: CreateDefaultAdminUserInput): DefaultAdminUser - createVariant(input: CreateVariantInput): CreateVariantPayload - deleteManyCandidates(query: CandidateQueryInput): DeleteManyPayload - deleteManyChecklists(query: ChecklistQueryInput): DeleteManyPayload - deleteManyClassifications(query: ClassificationQueryInput): DeleteManyPayload - deleteManyDuplicates(query: DuplicateQueryInput): DeleteManyPayload - deleteManyEntities(query: EntityQueryInput): DeleteManyPayload - deleteManyHistory_incidents(query: History_incidentQueryInput): DeleteManyPayload - deleteManyHistory_reports(query: History_reportQueryInput): DeleteManyPayload - deleteManyIncidents(query: IncidentQueryInput): DeleteManyPayload - deleteManyNotifications(query: NotificationQueryInput): DeleteManyPayload - deleteManyQuickadds(query: QuickaddQueryInput): DeleteManyPayload - deleteManyReports(query: ReportQueryInput): DeleteManyPayload - deleteManySubmissions(query: SubmissionQueryInput): DeleteManyPayload - deleteManySubscriptions(query: SubscriptionQueryInput): DeleteManyPayload - deleteManyTaxas(query: TaxaQueryInput): DeleteManyPayload - deleteManyUsers(query: UserQueryInput): DeleteManyPayload - deleteOneCandidate(query: CandidateQueryInput!): Candidate - deleteOneChecklist(query: ChecklistQueryInput!): Checklist - deleteOneClassification(query: ClassificationQueryInput!): Classification - deleteOneDuplicate(query: DuplicateQueryInput!): Duplicate - deleteOneEntity(query: EntityQueryInput!): Entity - deleteOneHistory_incident(query: History_incidentQueryInput!): History_incident - deleteOneHistory_report(query: History_reportQueryInput!): History_report - deleteOneIncident(query: IncidentQueryInput!): Incident - deleteOneNotification(query: NotificationQueryInput!): Notification - deleteOneQuickadd(query: QuickaddQueryInput!): Quickadd - deleteOneReport(query: ReportQueryInput!): Report - deleteOneSubmission(query: SubmissionQueryInput!): Submission - deleteOneSubscription(query: SubscriptionQueryInput!): Subscription - deleteOneTaxa(query: TaxaQueryInput!): Taxa - deleteOneUser(query: UserQueryInput!): User - getUser(input: GetUserInput): AppUser - insertManyCandidates(data: [CandidateInsertInput!]!): InsertManyPayload - insertManyChecklists(data: [ChecklistInsertInput!]!): InsertManyPayload - insertManyClassifications(data: [ClassificationInsertInput!]!): InsertManyPayload - insertManyDuplicates(data: [DuplicateInsertInput!]!): InsertManyPayload - insertManyEntities(data: [EntityInsertInput!]!): InsertManyPayload - insertManyHistory_incidents(data: [History_incidentInsertInput!]!): InsertManyPayload - insertManyHistory_reports(data: [History_reportInsertInput!]!): InsertManyPayload - insertManyIncidents(data: [IncidentInsertInput!]!): InsertManyPayload - insertManyNotifications(data: [NotificationInsertInput!]!): InsertManyPayload - insertManyQuickadds(data: [QuickaddInsertInput!]!): InsertManyPayload - insertManyReports(data: [ReportInsertInput!]!): InsertManyPayload - insertManySubmissions(data: [SubmissionInsertInput!]!): InsertManyPayload - insertManySubscriptions(data: [SubscriptionInsertInput!]!): InsertManyPayload - insertManyTaxas(data: [TaxaInsertInput!]!): InsertManyPayload - insertManyUsers(data: [UserInsertInput!]!): InsertManyPayload - insertOneCandidate(data: CandidateInsertInput!): Candidate - insertOneChecklist(data: ChecklistInsertInput!): Checklist - insertOneClassification(data: ClassificationInsertInput!): Classification - insertOneDuplicate(data: DuplicateInsertInput!): Duplicate - insertOneEntity(data: EntityInsertInput!): Entity - insertOneHistory_incident(data: History_incidentInsertInput!): History_incident - insertOneHistory_report(data: History_reportInsertInput!): History_report - insertOneIncident(data: IncidentInsertInput!): Incident - insertOneNotification(data: NotificationInsertInput!): Notification - insertOneQuickadd(data: QuickaddInsertInput!): Quickadd - insertOneReport(data: ReportInsertInput!): Report - insertOneSubmission(data: SubmissionInsertInput!): Submission - insertOneSubscription(data: SubscriptionInsertInput!): Subscription - insertOneTaxa(data: TaxaInsertInput!): Taxa - insertOneUser(data: UserInsertInput!): User - linkReportsToIncidents(input: LinkReportsToIncidentsInput): [Incident] - logIncidentHistory(input: History_incidentInsertInput): LogIncidentHistoryPayload - logReportHistory(input: History_reportInsertInput): LogReportHistoryPayload - processNotifications: Int - promoteSubmissionToReport(input: PromoteSubmissionToReportInput): PromoteSubmissionToReportPayload - replaceOneCandidate(data: CandidateInsertInput!, query: CandidateQueryInput): Candidate - replaceOneChecklist(query: ChecklistQueryInput, data: ChecklistInsertInput!): Checklist - replaceOneClassification(query: ClassificationQueryInput, data: ClassificationInsertInput!): Classification - replaceOneDuplicate(query: DuplicateQueryInput, data: DuplicateInsertInput!): Duplicate - replaceOneEntity(query: EntityQueryInput, data: EntityInsertInput!): Entity - replaceOneHistory_incident(query: History_incidentQueryInput, data: History_incidentInsertInput!): History_incident - replaceOneHistory_report(query: History_reportQueryInput, data: History_reportInsertInput!): History_report - replaceOneIncident(query: IncidentQueryInput, data: IncidentInsertInput!): Incident - replaceOneNotification(query: NotificationQueryInput, data: NotificationInsertInput!): Notification - replaceOneQuickadd(query: QuickaddQueryInput, data: QuickaddInsertInput!): Quickadd - replaceOneReport(query: ReportQueryInput, data: ReportInsertInput!): Report - replaceOneSubmission(query: SubmissionQueryInput, data: SubmissionInsertInput!): Submission - replaceOneSubscription(query: SubscriptionQueryInput, data: SubscriptionInsertInput!): Subscription - replaceOneTaxa(query: TaxaQueryInput, data: TaxaInsertInput!): Taxa - replaceOneUser(query: UserQueryInput, data: UserInsertInput!): User - updateManyCandidates(query: CandidateQueryInput, set: CandidateUpdateInput!): UpdateManyPayload - updateManyChecklists(query: ChecklistQueryInput, set: ChecklistUpdateInput!): UpdateManyPayload - updateManyClassifications(set: ClassificationUpdateInput!, query: ClassificationQueryInput): UpdateManyPayload - updateManyDuplicates(query: DuplicateQueryInput, set: DuplicateUpdateInput!): UpdateManyPayload - updateManyEntities(query: EntityQueryInput, set: EntityUpdateInput!): UpdateManyPayload - updateManyHistory_incidents(set: History_incidentUpdateInput!, query: History_incidentQueryInput): UpdateManyPayload - updateManyHistory_reports(query: History_reportQueryInput, set: History_reportUpdateInput!): UpdateManyPayload - updateManyIncidents(query: IncidentQueryInput, set: IncidentUpdateInput!): UpdateManyPayload - updateManyNotifications(query: NotificationQueryInput, set: NotificationUpdateInput!): UpdateManyPayload - updateManyQuickadds(query: QuickaddQueryInput, set: QuickaddUpdateInput!): UpdateManyPayload - updateManyReports(query: ReportQueryInput, set: ReportUpdateInput!): UpdateManyPayload - updateManySubmissions(query: SubmissionQueryInput, set: SubmissionUpdateInput!): UpdateManyPayload - updateManySubscriptions(query: SubscriptionQueryInput, set: SubscriptionUpdateInput!): UpdateManyPayload - updateManyTaxas(query: TaxaQueryInput, set: TaxaUpdateInput!): UpdateManyPayload - updateManyUsers(set: UserUpdateInput!, query: UserQueryInput): UpdateManyPayload - updateOneCandidate(query: CandidateQueryInput, set: CandidateUpdateInput!): Candidate - updateOneChecklist(query: ChecklistQueryInput, set: ChecklistUpdateInput!): Checklist - updateOneClassification(set: ClassificationUpdateInput!, query: ClassificationQueryInput): Classification - updateOneDuplicate(query: DuplicateQueryInput, set: DuplicateUpdateInput!): Duplicate - updateOneEntity(query: EntityQueryInput, set: EntityUpdateInput!): Entity - updateOneHistory_incident(query: History_incidentQueryInput, set: History_incidentUpdateInput!): History_incident - updateOneHistory_report(query: History_reportQueryInput, set: History_reportUpdateInput!): History_report - updateOneIncident(query: IncidentQueryInput, set: IncidentUpdateInput!): Incident - updateOneNotification(set: NotificationUpdateInput!, query: NotificationQueryInput): Notification - updateOneQuickadd(set: QuickaddUpdateInput!, query: QuickaddQueryInput): Quickadd - updateOneReport(query: ReportQueryInput, set: ReportUpdateInput!): Report - updateOneReportTranslation(input: UpdateOneReportTranslationInput): Report - updateOneSubmission(query: SubmissionQueryInput, set: SubmissionUpdateInput!): Submission - updateOneSubscription(query: SubscriptionQueryInput, set: SubscriptionUpdateInput!): Subscription - updateOneTaxa(query: TaxaQueryInput, set: TaxaUpdateInput!): Taxa - updateOneUser(query: UserQueryInput, set: UserUpdateInput!): User - upsertOneCandidate(query: CandidateQueryInput, data: CandidateInsertInput!): Candidate - upsertOneChecklist(query: ChecklistQueryInput, data: ChecklistInsertInput!): Checklist - upsertOneClassification(data: ClassificationInsertInput!, query: ClassificationQueryInput): Classification - upsertOneDuplicate(query: DuplicateQueryInput, data: DuplicateInsertInput!): Duplicate - upsertOneEntity(query: EntityQueryInput, data: EntityInsertInput!): Entity - upsertOneHistory_incident(query: History_incidentQueryInput, data: History_incidentInsertInput!): History_incident - upsertOneHistory_report(query: History_reportQueryInput, data: History_reportInsertInput!): History_report - upsertOneIncident(query: IncidentQueryInput, data: IncidentInsertInput!): Incident - upsertOneNotification(query: NotificationQueryInput, data: NotificationInsertInput!): Notification - upsertOneQuickadd(query: QuickaddQueryInput, data: QuickaddInsertInput!): Quickadd - upsertOneReport(query: ReportQueryInput, data: ReportInsertInput!): Report - upsertOneSubmission(data: SubmissionInsertInput!, query: SubmissionQueryInput): Submission - upsertOneSubscription(query: SubscriptionQueryInput, data: SubscriptionInsertInput!): Subscription - upsertOneTaxa(query: TaxaQueryInput, data: TaxaInsertInput!): Taxa - upsertOneUser(query: UserQueryInput, data: UserInsertInput!): User +input SubscriptionUserIdRelationInput { + create: UserInsertInput + link: String } -input ClassificationInsertInput { - reports: ClassificationReportsRelationInput! +input IncidentUpdateInput { + flagged_dissimilar_incidents_unset: Boolean + AllegedDeployerOfAISystem: IncidentAllegedDeployerOfAISystemRelationInput + description_unset: Boolean + _id_unset: Boolean + editor_notes: String _id: ObjectId - attributes: [ClassificationAttributeInsertInput] - incidents: ClassificationIncidentsRelationInput! - namespace: String! - notes: String - publish: Boolean -} - -input ReportEmbeddingUpdateInput { - from_text_hash_unset: Boolean - vector: [Float] - vector_unset: Boolean - from_text_hash: String + editor_notes_unset: Boolean + epoch_date_modified: Int + embedding: IncidentEmbeddingUpdateInput + editors: IncidentEditorsRelationInput + editor_similar_incidents: [Int] + AllegedDeployerOfAISystem_unset: Boolean + epoch_date_modified_unset: Boolean + embedding_unset: Boolean + date_unset: Boolean + tsne_unset: Boolean + nlp_similar_incidents: [IncidentNlp_similar_incidentUpdateInput] + epoch_date_modified_inc: Int + description: String + title: String + flagged_dissimilar_incidents: [Int] + date: String + incident_id_inc: Int + AllegedDeveloperOfAISystem: IncidentAllegedDeveloperOfAISystemRelationInput + AllegedHarmedOrNearlyHarmedParties: IncidentAllegedHarmedOrNearlyHarmedPartiesRelationInput + tsne: IncidentTsneUpdateInput + nlp_similar_incidents_unset: Boolean + editors_unset: Boolean + reports_unset: Boolean + incident_id: Int + title_unset: Boolean + reports: IncidentReportsRelationInput + editor_dissimilar_incidents: [Int] + editor_dissimilar_incidents_unset: Boolean + incident_id_unset: Boolean + editor_similar_incidents_unset: Boolean + AllegedHarmedOrNearlyHarmedParties_unset: Boolean + AllegedDeveloperOfAISystem_unset: Boolean + implicated_systems: IncidentImplicatedSystemsRelationInput } -enum NotificationSortByInput { +enum IncidentSortByInput { + DESCRIPTION_ASC + EPOCH_DATE_MODIFIED_ASC + EPOCH_DATE_MODIFIED_DESC _ID_ASC - _ID_DESC + DATE_DESC + DESCRIPTION_DESC INCIDENT_ID_ASC - SENTDATE_DESC - TYPE_ASC - TYPE_DESC - INCIDENT_ID_DESC - SENTDATE_ASC - USERID_ASC - USERID_DESC -} - -enum CandidateSortByInput { - SIMILARITY_ASC - SOURCE_DOMAIN_ASC - TEXT_DESC - _ID_DESC - DATE_PUBLISHED_ASC - DATE_PUBLISHED_DESC - IMAGE_URL_ASC - LANGUAGE_DESC - URL_ASC - URL_DESC - EPOCH_DATE_DOWNLOADED_ASC - EPOCH_DATE_PUBLISHED_ASC - EPOCH_DATE_PUBLISHED_DESC - LANGUAGE_ASC - TITLE_DESC - _ID_ASC - DATE_DOWNLOADED_DESC - PLAIN_TEXT_ASC - TEXT_ASC TITLE_ASC - SOURCE_DOMAIN_DESC - DATE_DOWNLOADED_ASC - EPOCH_DATE_DOWNLOADED_DESC - IMAGE_URL_DESC - PLAIN_TEXT_DESC - SIMILARITY_DESC -} - -type RisksPayloadPrecedentEmbedding { - from_reports: [Int] - vector: [Float] + DATE_ASC + EDITOR_NOTES_DESC + TITLE_DESC + _ID_DESC + EDITOR_NOTES_ASC + INCIDENT_ID_DESC } -input ClassificationQueryInput { - publish_ne: Boolean - incidents_nin: [IncidentQueryInput] - namespace_in: [String] - notes_exists: Boolean - reports_nin: [ReportQueryInput] - OR: [ClassificationQueryInput!] - attributes_in: [ClassificationAttributeQueryInput] - notes: String +input SubmissionUpdateInput { + language_unset: Boolean + harmed_parties: SubmissionHarmed_partiesRelationInput + embedding: SubmissionEmbeddingUpdateInput + incident_editors_unset: Boolean + nlp_similar_incidents_unset: Boolean + editor_similar_incidents: [Int] + description_unset: Boolean + deployers: SubmissionDeployersRelationInput + source_domain_unset: Boolean + tags: [String] + authors_unset: Boolean + date_modified_unset: Boolean + user: SubmissionUserRelationInput + _id_unset: Boolean + editor_notes: String + plain_text: String + title_unset: Boolean + url_unset: Boolean + editor_similar_incidents_unset: Boolean + editor_notes_unset: Boolean + cloudinary_id_unset: Boolean + text: String + date_downloaded_unset: Boolean + submitters: [String] + editor_dissimilar_incidents: [Int] + developers: SubmissionDevelopersRelationInput + date_submitted: String + title: String + status: String + authors: [String] + incident_title: String + date_modified: String + date_downloaded: String + language: String + plain_text_unset: Boolean _id: ObjectId - AND: [ClassificationQueryInput!] - attributes_nin: [ClassificationAttributeQueryInput] - notes_nin: [String] - _id_in: [ObjectId] - notes_lt: String - namespace_ne: String - incidents_in: [IncidentQueryInput] - incidents_exists: Boolean - reports: [ReportQueryInput] - namespace: String - _id_lt: ObjectId - _id_ne: ObjectId - namespace_nin: [String] - namespace_lte: String - notes_in: [String] - namespace_gte: String - _id_gt: ObjectId - _id_gte: ObjectId - notes_lte: String - attributes_exists: Boolean - incidents: [IncidentQueryInput] - notes_gt: String - notes_ne: String - reports_exists: Boolean - notes_gte: String - publish_exists: Boolean - namespace_exists: Boolean - reports_in: [ReportQueryInput] - _id_nin: [ObjectId] - attributes: [ClassificationAttributeQueryInput] - publish: Boolean - namespace_gt: String - _id_exists: Boolean - _id_lte: ObjectId - namespace_lt: String + harmed_parties_unset: Boolean + date_published_unset: Boolean + incident_ids: [Int] + submitters_unset: Boolean + user_unset: Boolean + developers_unset: Boolean + status_unset: Boolean + date_published: String + tags_unset: Boolean + incident_title_unset: Boolean + embedding_unset: Boolean + incident_ids_unset: Boolean + url: String + editor_dissimilar_incidents_unset: Boolean + incident_date: String + image_url: String + quiet_unset: Boolean + description: String + epoch_date_modified_unset: Boolean + cloudinary_id: String + source_domain: String + incident_editors: SubmissionIncident_editorsRelationInput + incident_date_unset: Boolean + epoch_date_modified: Int + text_unset: Boolean + date_submitted_unset: Boolean + epoch_date_modified_inc: Int + image_url_unset: Boolean + nlp_similar_incidents: [SubmissionNlp_similar_incidentUpdateInput] + quiet: Boolean + deployers_unset: Boolean } -input EntityUpdateInput { - created_at: DateTime - date_modified: DateTime - date_modified_unset: Boolean - _id: ObjectId - _id_unset: Boolean - created_at_unset: Boolean - entity_id: String - entity_id_unset: Boolean - name: String - name_unset: Boolean +input SubmissionNlp_similar_incidentUpdateInput { + similarity_inc: Float + similarity_unset: Boolean + incident_id: Int + incident_id_inc: Int + incident_id_unset: Boolean + similarity: Float } -input History_reportEmbeddingInsertInput { - from_text_hash: String - vector: [Float] +input CandidateUpdateInput { + matching_entities_unset: Boolean + similarity_unset: Boolean + text: String + classification_similarity: [CandidateClassification_similarityUpdateInput] + language: String + match: Boolean + _id_unset: Boolean + embedding_unset: Boolean + authors: [String] + epoch_date_published_unset: Boolean + matching_keywords_unset: Boolean + date_downloaded: String + source_domain_unset: Boolean + embedding: CandidateEmbeddingUpdateInput + matching_harm_keywords_unset: Boolean + classification_similarity_unset: Boolean + title: String + plain_text: String + text_unset: Boolean + similarity: Float + similarity_inc: Float + dismissed_unset: Boolean + title_unset: Boolean + image_url_unset: Boolean + epoch_date_downloaded_unset: Boolean + date_downloaded_unset: Boolean + epoch_date_downloaded: Int + matching_harm_keywords: [String] + dismissed: Boolean + matching_keywords: [String] + plain_text_unset: Boolean + authors_unset: Boolean + source_domain: String + url: String + epoch_date_published: Int + url_unset: Boolean + date_published: String + epoch_date_published_inc: Int + language_unset: Boolean + date_published_unset: Boolean + match_unset: Boolean + epoch_date_downloaded_inc: Int + matching_entities: [String] + image_url: String + _id: ObjectId } -input History_incidentEmbeddingInsertInput { - from_reports: [Int] - vector: [Float] +input TaxaField_listItem_fieldComplete_fromInsertInput { + entities: Boolean + all: [String] + current: [String] } -input ChecklistUpdateInput { - risks_unset: Boolean +input EntityUpdateInput { + date_modified: DateTime + entity_id: String + created_at: DateTime + date_modified_unset: Boolean + entity_id_unset: Boolean + name_unset: Boolean _id: ObjectId _id_unset: Boolean - date_updated_unset: Boolean - name_unset: Boolean - date_created: DateTime - tags_goals: [String] - tags_methods_unset: Boolean - entity_id_unset: Boolean name: String - owner_id: String - tags_methods: [String] - id_unset: Boolean - entity_id: String - owner_id_unset: Boolean - risks: [ChecklistRiskUpdateInput] - tags_other: [String] - about: String - tags_goals_unset: Boolean - tags_other_unset: Boolean - date_created_unset: Boolean - date_updated: DateTime - id: String - about_unset: Boolean -} - -type TaxaField_listComplete_from { - all: [String] - current: [String] + created_at_unset: Boolean } -type Quickadd { +input DuplicateQueryInput { + _id_nin: [ObjectId] + true_incident_number_lt: Int + true_incident_number_gt: Int + true_incident_number_lte: Int + _id_ne: ObjectId + _id_gte: ObjectId + _id_exists: Boolean + true_incident_number_exists: Boolean + true_incident_number_ne: Int + true_incident_number_in: [Int] + duplicate_incident_number_lt: Int + duplicate_incident_number: Int + duplicate_incident_number_lte: Int + true_incident_number_gte: Int + duplicate_incident_number_ne: Int + true_incident_number: Int _id: ObjectId - date_submitted: String! - incident_id: Long - source_domain: String - url: String! + duplicate_incident_number_exists: Boolean + true_incident_number_nin: [Int] + _id_lte: ObjectId + duplicate_incident_number_gt: Int + duplicate_incident_number_gte: Int + duplicate_incident_number_nin: [Int] + _id_in: [ObjectId] + _id_lt: ObjectId + OR: [DuplicateQueryInput!] + AND: [DuplicateQueryInput!] + _id_gt: ObjectId + duplicate_incident_number_in: [Int] } input CandidateEmbeddingQueryInput { - vector_in: [Float] OR: [CandidateEmbeddingQueryInput!] - from_text_hash_nin: [String] + from_text_hash_gt: String vector_exists: Boolean - from_text_hash: String - from_text_hash_exists: Boolean - from_text_hash_lte: String - from_text_hash_in: [String] - vector: [Float] - vector_nin: [Float] + AND: [CandidateEmbeddingQueryInput!] from_text_hash_gte: String - from_text_hash_gt: String from_text_hash_ne: String - AND: [CandidateEmbeddingQueryInput!] + from_text_hash_nin: [String] from_text_hash_lt: String -} - -input IncidentEmbeddingQueryInput { - from_reports: [Int] - from_reports_in: [Int] - vector: [Float] vector_nin: [Float] + vector: [Float] + from_text_hash: String + from_text_hash_lte: String + from_text_hash_exists: Boolean vector_in: [Float] - AND: [IncidentEmbeddingQueryInput!] - from_reports_nin: [Int] - from_reports_exists: Boolean - vector_exists: Boolean - OR: [IncidentEmbeddingQueryInput!] + from_text_hash_in: [String] } -input SubscriptionUserIdRelationInput { - create: UserInsertInput +input SubscriptionInsertInput { + _id: ObjectId + entityId: SubscriptionEntityIdRelationInput + incident_id: SubscriptionIncident_idRelationInput + type: String! + userId: SubscriptionUserIdRelationInput! +} + +input SubscriptionEntityIdRelationInput { link: String + create: EntityInsertInput } -input TaxaField_listItem_fieldComplete_fromInsertInput { - entities: Boolean - all: [String] - current: [String] +input CandidateEmbeddingInsertInput { + vector: [Float] + from_text_hash: String } -input History_incidentTsneQueryInput { - x_gte: Float - y_exists: Boolean - AND: [History_incidentTsneQueryInput!] - x_exists: Boolean - x_in: [Float] - y_in: [Float] +input IncidentTsneInsertInput { x: Float - x_ne: Float - x_gt: Float - y_ne: Float - x_lte: Float - y_nin: [Float] y: Float - y_lt: Float - x_lt: Float - y_gt: Float - y_gte: Float - y_lte: Float - OR: [History_incidentTsneQueryInput!] - x_nin: [Float] } -input TaxaField_listItem_fieldInsertInput { - mongo_type: String - weight: Int - field_number: String +input TaxaField_listInsertInput { permitted_values: [String] - default: String - instant_facet: Boolean - short_description: String - complete_from: TaxaField_listItem_fieldComplete_fromInsertInput - public: Boolean - display_type: String - placeholder: String - short_name: String long_name: String + weight: Int long_description: String - required: Boolean -} - -input SubscriptionQueryInput { - _id_nin: [ObjectId] - _id_gt: ObjectId - type_exists: Boolean - _id_ne: ObjectId - type: String - _id_gte: ObjectId - _id_lt: ObjectId - _id_exists: Boolean - incident_id: IncidentQueryInput - type_gt: String - type_lt: String - type_in: [String] - entityId: EntityQueryInput - type_nin: [String] - incident_id_exists: Boolean - type_ne: String - AND: [SubscriptionQueryInput!] - _id_in: [ObjectId] - type_lte: String - _id_lte: ObjectId - userId: UserQueryInput - userId_exists: Boolean - type_gte: String - OR: [SubscriptionQueryInput!] - _id: ObjectId - entityId_exists: Boolean -} - -input ChecklistQueryInput { - _id_lte: ObjectId - about_gte: String - id_ne: String - risks_exists: Boolean - owner_id_gte: String - entity_id_gt: String - date_created_gt: DateTime - date_updated_nin: [DateTime] - date_created_lte: DateTime - tags_methods_exists: Boolean - owner_id_lt: String - AND: [ChecklistQueryInput!] - risks_in: [ChecklistRiskQueryInput] - tags_other_in: [String] - about_exists: Boolean - name_in: [String] - entity_id_exists: Boolean - owner_id: String - _id_gt: ObjectId - owner_id_in: [String] - tags_other_exists: Boolean - date_updated_ne: DateTime - name_gte: String - tags_methods_nin: [String] - date_updated_exists: Boolean - entity_id_nin: [String] - date_updated_lte: DateTime - owner_id_lte: String - id: String - id_gt: String - date_updated: DateTime - entity_id_lt: String - date_created_in: [DateTime] - _id_exists: Boolean - date_created_exists: Boolean - name_gt: String - name_lt: String - about_gt: String - entity_id_ne: String - tags_goals_exists: Boolean - id_in: [String] - owner_id_exists: Boolean - tags_goals_in: [String] - date_updated_gte: DateTime - date_created: DateTime - _id_nin: [ObjectId] - date_created_lt: DateTime - risks: [ChecklistRiskQueryInput] - tags_goals: [String] - about: String - _id_ne: ObjectId - tags_methods: [String] - tags_methods_in: [String] - tags_other_nin: [String] - id_lte: String - date_updated_in: [DateTime] - id_exists: Boolean - entity_id_in: [String] - name_ne: String - _id_gte: ObjectId - owner_id_ne: String - tags_other: [String] - owner_id_gt: String - date_updated_gt: DateTime - risks_nin: [ChecklistRiskQueryInput] - date_updated_lt: DateTime - name_lte: String - _id_lt: ObjectId - id_gte: String - id_lt: String - entity_id_gte: String - _id: ObjectId - about_lte: String - name: String - date_created_gte: DateTime - date_created_ne: DateTime - name_nin: [String] - about_in: [String] - owner_id_nin: [String] - about_ne: String - about_nin: [String] - entity_id: String - date_created_nin: [DateTime] - id_nin: [String] - _id_in: [ObjectId] - name_exists: Boolean - entity_id_lte: String - tags_goals_nin: [String] - about_lt: String - OR: [ChecklistQueryInput!] -} - -type TaxaField_listItem_field { - complete_from: TaxaField_listItem_fieldComplete_from - default: String display_type: String field_number: String - instant_facet: Boolean - long_description: String - long_name: String + required: Boolean mongo_type: String - permitted_values: [String] placeholder: String - public: Boolean - required: Boolean short_description: String + item_fields: TaxaField_listItem_fieldInsertInput short_name: String - weight: Int + default: String + complete_from: TaxaField_listComplete_fromInsertInput + hide_search: Boolean + instant_facet: Boolean + public: Boolean } -input NotificationUpdateInput { - incident_id_unset: Boolean - processed_unset: Boolean - type: String - incident_id: Int - incident_id_inc: Int - sentDate: DateTime - type_unset: Boolean - sentDate_unset: Boolean - userId_unset: Boolean - _id_unset: Boolean - userId: NotificationUserIdRelationInput +input ClassificationUpdateInput { _id: ObjectId - processed: Boolean + _id_unset: Boolean + incidents: ClassificationIncidentsRelationInput + publish_unset: Boolean + reports_unset: Boolean + attributes_unset: Boolean + publish: Boolean + attributes: [ClassificationAttributeUpdateInput] + namespace_unset: Boolean + notes: String + incidents_unset: Boolean + namespace: String + reports: ClassificationReportsRelationInput + notes_unset: Boolean } -type Incident { - AllegedDeployerOfAISystem: [Entity] - AllegedDeveloperOfAISystem: [Entity] - AllegedHarmedOrNearlyHarmedParties: [Entity] - _id: ObjectId - date: String! - description: String - editor_dissimilar_incidents: [Int] - editor_notes: String - editor_similar_incidents: [Int] - editors: [User]! - embedding: IncidentEmbedding - epoch_date_modified: Int - flagged_dissimilar_incidents: [Int] - incident_id: Int! - nlp_similar_incidents: [IncidentNlp_similar_incident] - reports: [Report]! - title: String! - tsne: IncidentTsne +type IncidentNlp_similar_incident { + incident_id: Int + similarity: Float } -input History_incidentQueryInput { - modifiedBy_in: [String] - description_ne: String - editors: [String] - epoch_date_modified_ne: Int - flagged_dissimilar_incidents_in: [Int] - description_gte: String - OR: [History_incidentQueryInput!] - AllegedDeveloperOfAISystem: [String] - incident_id: Int - incident_id_exists: Boolean - date_lt: String - incident_id_nin: [Int] - editor_notes: String - modifiedBy_ne: String - title_gt: String - _id_ne: ObjectId - _id_gte: ObjectId - AllegedDeveloperOfAISystem_exists: Boolean - AllegedHarmedOrNearlyHarmedParties_in: [String] - editor_notes_in: [String] - AllegedHarmedOrNearlyHarmedParties_exists: Boolean - flagged_dissimilar_incidents_nin: [Int] - title_lt: String - title_ne: String - editor_similar_incidents_nin: [Int] - AllegedDeveloperOfAISystem_nin: [String] - _id: ObjectId - flagged_dissimilar_incidents_exists: Boolean - epoch_date_modified_gte: Int - _id_exists: Boolean - editors_in: [String] - modifiedBy: String - editor_notes_exists: Boolean - AllegedDeployerOfAISystem: [String] - incident_id_ne: Int - editor_dissimilar_incidents_in: [Int] - date_in: [String] - AllegedDeployerOfAISystem_exists: Boolean - reports_in: [Int] - description_lte: String - _id_nin: [ObjectId] - epoch_date_modified_exists: Boolean - AllegedDeveloperOfAISystem_in: [String] - nlp_similar_incidents_exists: Boolean - editors_exists: Boolean - AllegedHarmedOrNearlyHarmedParties_nin: [String] - title_in: [String] - incident_id_lt: Int - description_gt: String - nlp_similar_incidents: [History_incidentNlp_similar_incidentQueryInput] - epoch_date_modified_in: [Int] - description: String - reports_nin: [Int] - _id_in: [ObjectId] - description_in: [String] - editor_dissimilar_incidents: [Int] - modifiedBy_lt: String - editor_notes_lte: String - incident_id_gte: Int - title: String - title_nin: [String] - AllegedDeployerOfAISystem_in: [String] - nlp_similar_incidents_nin: [History_incidentNlp_similar_incidentQueryInput] - epoch_date_modified_lte: Int - title_gte: String - date_ne: String - editor_similar_incidents_exists: Boolean - incident_id_in: [Int] - _id_lte: ObjectId - reports: [Int] - incident_id_lte: Int - epoch_date_modified_lt: Int - date_gte: String - editor_dissimilar_incidents_nin: [Int] - editor_similar_incidents_in: [Int] - title_lte: String - date: String - date_exists: Boolean - modifiedBy_gt: String - editor_notes_lt: String - editor_similar_incidents: [Int] - _id_lt: ObjectId - epoch_date_modified_nin: [Int] - embedding: History_incidentEmbeddingQueryInput - AND: [History_incidentQueryInput!] - flagged_dissimilar_incidents: [Int] - incident_id_gt: Int - editor_notes_ne: String - modifiedBy_lte: String - modifiedBy_exists: Boolean - modifiedBy_nin: [String] - _id_gt: ObjectId - date_gt: String - editor_notes_nin: [String] - tsne_exists: Boolean - date_lte: String - epoch_date_modified_gt: Int - editors_nin: [String] - title_exists: Boolean - reports_exists: Boolean - AllegedDeployerOfAISystem_nin: [String] - editor_notes_gt: String - AllegedHarmedOrNearlyHarmedParties: [String] - description_lt: String - embedding_exists: Boolean - nlp_similar_incidents_in: [History_incidentNlp_similar_incidentQueryInput] - tsne: History_incidentTsneQueryInput - epoch_date_modified: Int - date_nin: [String] - description_exists: Boolean - description_nin: [String] - editor_notes_gte: String - editor_dissimilar_incidents_exists: Boolean - modifiedBy_gte: String +input History_incidentEmbeddingQueryInput { + from_reports: [Int] + vector: [Float] + from_reports_nin: [Int] + from_reports_exists: Boolean + AND: [History_incidentEmbeddingQueryInput!] + OR: [History_incidentEmbeddingQueryInput!] + from_reports_in: [Int] + vector_nin: [Float] + vector_exists: Boolean + vector_in: [Float] } -input RisksInput { - tags: [String] +type InsertManyPayload { + insertedIds: [ObjectId]! } -input QuickaddInsertInput { - source_domain: String - url: String! +input QuickaddUpdateInput { + source_domain_unset: Boolean _id: ObjectId - date_submitted: String! + url_unset: Boolean + _id_unset: Boolean + url: String + date_submitted_unset: Boolean + incident_id_unset: Boolean + source_domain: String + date_submitted: String incident_id: Long } -input History_reportEmbeddingQueryInput { - AND: [History_reportEmbeddingQueryInput!] - vector_in: [Float] - vector_exists: Boolean - from_text_hash_in: [String] - from_text_hash_ne: String - from_text_hash: String - OR: [History_reportEmbeddingQueryInput!] - from_text_hash_gt: String - from_text_hash_lt: String - from_text_hash_gte: String - from_text_hash_nin: [String] - from_text_hash_lte: String - vector_nin: [Float] - vector: [Float] - from_text_hash_exists: Boolean +input DuplicateInsertInput { + duplicate_incident_number: Int + true_incident_number: Int + _id: ObjectId } -type IncidentTsne { - x: Float - y: Float +enum ClassificationSortByInput { + NAMESPACE_DESC + NOTES_ASC + NOTES_DESC + _ID_ASC + _ID_DESC + NAMESPACE_ASC } -input IncidentAllegedDeployerOfAISystemRelationInput { - link: [String] - create: [EntityInsertInput] +input ChecklistRiskPrecedentInsertInput { + description: String + incident_id: Int + tags: [String] + title: String } -input SubmissionIncident_editorsRelationInput { - link: [String] - create: [UserInsertInput] +input History_incidentTsneInsertInput { + x: Float + y: Float } -input ChecklistRiskPrecedentInsertInput { - title: String - description: String +input IncidentNlp_similar_incidentInsertInput { incident_id: Int - tags: [String] + similarity: Float } -input QuickaddUpdateInput { - _id_unset: Boolean +input History_incidentUpdateInput { + title: String + date_unset: Boolean + embedding_unset: Boolean + epoch_date_modified_unset: Boolean + modifiedBy: String + AllegedHarmedOrNearlyHarmedParties: [String] + title_unset: Boolean + AllegedDeployerOfAISystem_unset: Boolean + AllegedDeveloperOfAISystem: [String] + implicated_systems_unset: Boolean + editors: [String] + description_unset: Boolean + flagged_dissimilar_incidents: [Int] incident_id_unset: Boolean - source_domain_unset: Boolean - url: String - url_unset: Boolean + editor_dissimilar_incidents: [Int] + editors_unset: Boolean + reports: [Int] _id: ObjectId - date_submitted: String - date_submitted_unset: Boolean - incident_id: Long - source_domain: String + nlp_similar_incidents_unset: Boolean + tsne_unset: Boolean + editor_notes_unset: Boolean + date: String + incident_id_inc: Int + incident_id: Int + editor_similar_incidents: [Int] + editor_similar_incidents_unset: Boolean + reports_unset: Boolean + _id_unset: Boolean + epoch_date_modified_inc: Int + AllegedDeveloperOfAISystem_unset: Boolean + epoch_date_modified: Int + AllegedDeployerOfAISystem: [String] + description: String + editor_notes: String + modifiedBy_unset: Boolean + nlp_similar_incidents: [History_incidentNlp_similar_incidentUpdateInput] + editor_dissimilar_incidents_unset: Boolean + embedding: History_incidentEmbeddingUpdateInput + tsne: History_incidentTsneUpdateInput + flagged_dissimilar_incidents_unset: Boolean + AllegedHarmedOrNearlyHarmedParties_unset: Boolean + implicated_systems: [String] } -input TaxaField_listItem_fieldComplete_fromQueryInput { - all_nin: [String] - current_in: [String] - entities_exists: Boolean - AND: [TaxaField_listItem_fieldComplete_fromQueryInput!] - OR: [TaxaField_listItem_fieldComplete_fromQueryInput!] - all_in: [String] - current: [String] - entities: Boolean - all_exists: Boolean - current_exists: Boolean - all: [String] - current_nin: [String] - entities_ne: Boolean -} - -type IncidentEmbedding { - from_reports: [Int] - vector: [Float] +input ReportUpdateInput { + date_modified_unset: Boolean + flag_unset: Boolean + user: ReportUserRelationInput + date_submitted: DateTime + _id: ObjectId + editor_notes_unset: Boolean + epoch_date_published: Int + description: String + report_number_inc: Int + cloudinary_id_unset: Boolean + inputs_outputs: [String] + authors: [String] + url: String + epoch_date_modified_unset: Boolean + plain_text_unset: Boolean + epoch_date_published_inc: Int + epoch_date_submitted_unset: Boolean + language_unset: Boolean + date_modified: DateTime + url_unset: Boolean + epoch_date_submitted_inc: Int + cloudinary_id: String + submitters_unset: Boolean + image_url: String + embedding: ReportEmbeddingUpdateInput + text: String + tags: [String] + text_unset: Boolean + image_url_unset: Boolean + date_published: DateTime + is_incident_report: Boolean + language: String + is_incident_report_unset: Boolean + date_published_unset: Boolean + quiet: Boolean + plain_text: String + submitters: [String] + description_unset: Boolean + report_number_unset: Boolean + embedding_unset: Boolean + epoch_date_downloaded: Int + epoch_date_submitted: Int + inputs_outputs_unset: Boolean + user_unset: Boolean + epoch_date_published_unset: Boolean + quiet_unset: Boolean + report_number: Int + title: String + date_downloaded_unset: Boolean + date_downloaded: DateTime + source_domain_unset: Boolean + epoch_date_downloaded_inc: Int + source_domain: String + tags_unset: Boolean + epoch_date_modified: Int + editor_notes: String + title_unset: Boolean + flag: Boolean + authors_unset: Boolean + _id_unset: Boolean + epoch_date_modified_inc: Int + date_submitted_unset: Boolean + epoch_date_downloaded_unset: Boolean } -input DuplicateQueryInput { - true_incident_number_exists: Boolean - duplicate_incident_number_lte: Int - true_incident_number_gt: Int - true_incident_number_lte: Int +input History_incidentQueryInput { + editor_similar_incidents_exists: Boolean + AllegedDeployerOfAISystem: [String] + description_ne: String + title_gte: String + _id_gt: ObjectId + modifiedBy_nin: [String] + title_in: [String] + date: String + editor_dissimilar_incidents_in: [Int] + editor_notes_ne: String + title_ne: String + editor_similar_incidents_in: [Int] + _id_in: [ObjectId] + incident_id_exists: Boolean + editor_notes_lte: String + AllegedHarmedOrNearlyHarmedParties_in: [String] + editor_dissimilar_incidents: [Int] + implicated_systems_nin: [String] + description_gte: String + date_gt: String + epoch_date_modified: Int + modifiedBy_gt: String + tsne: History_incidentTsneQueryInput + flagged_dissimilar_incidents_in: [Int] + tsne_exists: Boolean + flagged_dissimilar_incidents_exists: Boolean + editor_notes_exists: Boolean + implicated_systems: [String] + editor_similar_incidents: [Int] + epoch_date_modified_exists: Boolean + description: String + epoch_date_modified_lte: Int + AllegedDeveloperOfAISystem_exists: Boolean + title_nin: [String] + implicated_systems_in: [String] + modifiedBy_exists: Boolean + date_in: [String] + epoch_date_modified_in: [Int] + title_exists: Boolean + incident_id_lte: Int _id_ne: ObjectId - true_incident_number_in: [Int] - _id_exists: Boolean - true_incident_number_lt: Int - true_incident_number_nin: [Int] - AND: [DuplicateQueryInput!] - _id_nin: [ObjectId] - true_incident_number: Int - _id_gte: ObjectId - duplicate_incident_number_gt: Int _id_lt: ObjectId - duplicate_incident_number_gte: Int - duplicate_incident_number_nin: [Int] - _id_in: [ObjectId] - true_incident_number_ne: Int + implicated_systems_exists: Boolean + date_gte: String + AllegedHarmedOrNearlyHarmedParties: [String] + description_in: [String] + AllegedDeployerOfAISystem_exists: Boolean + editors_exists: Boolean + description_nin: [String] + editor_notes_gt: String + reports_nin: [Int] + description_gt: String + epoch_date_modified_ne: Int + flagged_dissimilar_incidents: [Int] + incident_id_lt: Int + embedding_exists: Boolean + _id: ObjectId + incident_id_gt: Int + description_lte: String + AllegedDeveloperOfAISystem_in: [String] + nlp_similar_incidents: [History_incidentNlp_similar_incidentQueryInput] + editor_dissimilar_incidents_nin: [Int] _id_lte: ObjectId - duplicate_incident_number_lt: Int - duplicate_incident_number_ne: Int + date_lt: String + incident_id_in: [Int] + title_lte: String + incident_id_nin: [Int] + editor_notes_in: [String] + _id_nin: [ObjectId] + epoch_date_modified_gt: Int + reports_exists: Boolean + title: String + incident_id: Int + _id_gte: ObjectId + AllegedDeployerOfAISystem_in: [String] + modifiedBy_gte: String + nlp_similar_incidents_exists: Boolean + flagged_dissimilar_incidents_nin: [Int] + date_ne: String + modifiedBy_ne: String + AND: [History_incidentQueryInput!] + nlp_similar_incidents_nin: [History_incidentNlp_similar_incidentQueryInput] + editor_notes: String + editor_notes_gte: String + AllegedDeployerOfAISystem_nin: [String] + title_gt: String + modifiedBy_in: [String] + incident_id_ne: Int + OR: [History_incidentQueryInput!] + editor_dissimilar_incidents_exists: Boolean + editors: [String] + epoch_date_modified_nin: [Int] + AllegedHarmedOrNearlyHarmedParties_exists: Boolean + date_nin: [String] + reports: [Int] + editors_nin: [String] + editor_notes_lt: String + embedding: History_incidentEmbeddingQueryInput + date_exists: Boolean + editor_similar_incidents_nin: [Int] + editor_notes_nin: [String] + _id_exists: Boolean + reports_in: [Int] + nlp_similar_incidents_in: [History_incidentNlp_similar_incidentQueryInput] + description_exists: Boolean + description_lt: String + AllegedHarmedOrNearlyHarmedParties_nin: [String] + epoch_date_modified_gte: Int + editors_in: [String] + epoch_date_modified_lt: Int + modifiedBy_lte: String + title_lt: String + modifiedBy_lt: String + date_lte: String + AllegedDeveloperOfAISystem_nin: [String] + AllegedDeveloperOfAISystem: [String] + incident_id_gte: Int + modifiedBy: String +} + +input SubscriptionUpdateInput { + _id_unset: Boolean + entityId_unset: Boolean + userId: SubscriptionUserIdRelationInput + userId_unset: Boolean + entityId: SubscriptionEntityIdRelationInput + incident_id: SubscriptionIncident_idRelationInput + incident_id_unset: Boolean + type: String _id: ObjectId - duplicate_incident_number_exists: Boolean - _id_gt: ObjectId - true_incident_number_gte: Int - OR: [DuplicateQueryInput!] - duplicate_incident_number: Int - duplicate_incident_number_in: [Int] + type_unset: Boolean } -input IncidentTsneInsertInput { - x: Float - y: Float +input ChecklistRiskUpdateInput { + likelihood_unset: Boolean + risk_notes: String + title: String + generated_unset: Boolean + severity_unset: Boolean + tags: [String] + generated: Boolean + touched: Boolean + tags_unset: Boolean + likelihood: String + risk_status_unset: Boolean + severity: String + title_unset: Boolean + precedents_unset: Boolean + risk_notes_unset: Boolean + touched_unset: Boolean + id_unset: Boolean + risk_status: String + id: String + precedents: [ChecklistRiskPrecedentUpdateInput] } -input IncidentTsneUpdateInput { - y_unset: Boolean - x: Float - x_inc: Float - x_unset: Boolean - y: Float - y_inc: Float +input ClassificationAttributeQueryInput { + value_json_lt: String + value_json_in: [String] + short_name: String + short_name_nin: [String] + short_name_lte: String + value_json_nin: [String] + value_json_lte: String + AND: [ClassificationAttributeQueryInput!] + short_name_ne: String + value_json_ne: String + OR: [ClassificationAttributeQueryInput!] + value_json_gte: String + short_name_lt: String + value_json_gt: String + value_json_exists: Boolean + short_name_gte: String + value_json: String + short_name_gt: String + short_name_in: [String] + short_name_exists: Boolean } -type LogReportHistoryPayload { - report_number: Int +type Candidate { + _id: ObjectId + authors: [String] + classification_similarity: [CandidateClassification_similarity] + date_downloaded: String + date_published: String + dismissed: Boolean + embedding: CandidateEmbedding + epoch_date_downloaded: Int + epoch_date_published: Int + image_url: String + language: String + match: Boolean! + matching_entities: [String] + matching_harm_keywords: [String] + matching_keywords: [String] + plain_text: String + similarity: Float + source_domain: String + text: String + title: String + url: String! } -input TaxaField_listInsertInput { - mongo_type: String - display_type: String - hide_search: Boolean - short_name: String - permitted_values: [String] - field_number: String - item_fields: TaxaField_listItem_fieldInsertInput - default: String - long_name: String - long_description: String - short_description: String - instant_facet: Boolean - public: Boolean - weight: Int - placeholder: String - required: Boolean - complete_from: TaxaField_listComplete_fromInsertInput +type Subscription { + _id: ObjectId + entityId: Entity + incident_id: Incident + type: String! + userId: User! } -input TaxaDummy_fieldUpdateInput { - field_number: String - field_number_unset: Boolean - short_name: String - short_name_unset: Boolean +enum NotificationSortByInput { + TYPE_ASC + USERID_ASC + USERID_DESC + _ID_DESC + INCIDENT_ID_ASC + INCIDENT_ID_DESC + SENTDATE_DESC + _ID_ASC + SENTDATE_ASC + TYPE_DESC } -type Entity { +input QuickaddInsertInput { _id: ObjectId - created_at: DateTime - date_modified: DateTime - entity_id: String! - name: String! + date_submitted: String! + incident_id: Long + source_domain: String + url: String! } -input ClassificationReportsRelationInput { - create: [ReportInsertInput] - link: [Int] +input History_incidentNlp_similar_incidentInsertInput { + incident_id: Int + similarity: Float } -type UpdateManyPayload { - matchedCount: Int! - modifiedCount: Int! +input ChecklistUpdateInput { + risks: [ChecklistRiskUpdateInput] + date_updated_unset: Boolean + id_unset: Boolean + tags_methods: [String] + entity_id: String + date_created: DateTime + date_updated: DateTime + owner_id_unset: Boolean + tags_methods_unset: Boolean + id: String + owner_id: String + _id_unset: Boolean + tags_goals: [String] + tags_other_unset: Boolean + about: String + risks_unset: Boolean + entity_id_unset: Boolean + date_created_unset: Boolean + tags_goals_unset: Boolean + tags_other: [String] + about_unset: Boolean + name_unset: Boolean + name: String + _id: ObjectId } -input EntityQueryInput { - date_modified_in: [DateTime] - created_at_nin: [DateTime] - _id_in: [ObjectId] - entity_id_exists: Boolean +input NotificationQueryInput { + _id_gte: ObjectId + type: String + sentDate_exists: Boolean + incident_id_in: [Int] + processed_ne: Boolean + sentDate_gte: DateTime + type_in: [String] + sentDate_nin: [DateTime] _id_ne: ObjectId - name_exists: Boolean - date_modified_ne: DateTime - date_modified_gt: DateTime - entity_id: String - date_modified_nin: [DateTime] - name: String - OR: [EntityQueryInput!] - entity_id_in: [String] - entity_id_ne: String - created_at_gte: DateTime - AND: [EntityQueryInput!] - _id_lt: ObjectId - created_at_ne: DateTime - date_modified_gte: DateTime - name_gt: String - name_in: [String] - date_modified_exists: Boolean - created_at_in: [DateTime] + processed: Boolean + type_exists: Boolean + AND: [NotificationQueryInput!] + incident_id_lt: Int + OR: [NotificationQueryInput!] + sentDate_lte: DateTime + incident_id_ne: Int + type_gt: String + incident_id_exists: Boolean + incident_id_gte: Int + userId_exists: Boolean + incident_id: Int _id_lte: ObjectId - entity_id_nin: [String] - entity_id_gt: String - name_lt: String - date_modified_lte: DateTime _id_gt: ObjectId - _id: ObjectId - entity_id_gte: String - name_nin: [String] - name_ne: String - name_lte: String - _id_nin: [ObjectId] + sentDate: DateTime _id_exists: Boolean - created_at_lte: DateTime - date_modified: DateTime - date_modified_lt: DateTime - entity_id_lt: String - created_at_gt: DateTime - created_at_exists: Boolean - entity_id_lte: String - created_at: DateTime - name_gte: String - _id_gte: ObjectId - created_at_lt: DateTime + incident_id_lte: Int + type_lt: String + incident_id_nin: [Int] + sentDate_gt: DateTime + type_gte: String + type_lte: String + processed_exists: Boolean + userId: UserQueryInput + sentDate_ne: DateTime + sentDate_lt: DateTime + _id_in: [ObjectId] + type_ne: String + _id_nin: [ObjectId] + _id: ObjectId + _id_lt: ObjectId + type_nin: [String] + incident_id_gt: Int + sentDate_in: [DateTime] } -type RisksPayloadPrecedent { - AllegedDeployerOfAISystem: [String] - AllegedDeveloperOfAISystem: [String] - AllegedHarmedOrNearlyHarmedParties: [String] +type TaxaField_listItem_fieldComplete_from { + all: [String] + current: [String] + entities: Boolean +} + +input NotificationUpdateInput { _id: ObjectId - date: String - description: String - editor_dissimilar_incidents: [Int] - editor_notes: String - editor_similar_incidents: [Int] - editors: [String] - embedding: RisksPayloadPrecedentEmbedding - epoch_date_modified: Int - flagged_dissimilar_incidents: [Int] + incident_id_unset: Boolean + userId: NotificationUserIdRelationInput + processed: Boolean + processed_unset: Boolean + type: String + type_unset: Boolean + userId_unset: Boolean incident_id: Int - nlp_similar_incidents: [RisksPayloadPrecedentNlp_similar_incident] - reports: [Int] - tags: [String] - title: String - tsne: RisksPayloadPrecedentTsne + sentDate_unset: Boolean + _id_unset: Boolean + incident_id_inc: Int + sentDate: DateTime } -input CandidateClassification_similarityUpdateInput { - similarity: Float - similarity_inc: Float - similarity_unset: Boolean - classification: String - classification_unset: Boolean +input CandidateEmbeddingUpdateInput { + vector: [Float] + vector_unset: Boolean + from_text_hash: String + from_text_hash_unset: Boolean } -input TaxaDummy_fieldInsertInput { +input History_reportInsertInput { + authors: [String]! + title: String! + date_published: DateTime! + epoch_date_submitted: Int! + flag: Boolean + submitters: [String]! + editor_notes: String + is_incident_report: Boolean + quiet: Boolean + tags: [String]! + text: String! + source_domain: String! + cloudinary_id: String! + epoch_date_downloaded: Int! + inputs_outputs: [String] + epoch_date_published: Int! + user: String + embedding: History_reportEmbeddingInsertInput + date_modified: DateTime! + plain_text: String! + report_number: Int! + date_submitted: DateTime! + image_url: String! + language: String! + description: String + epoch_date_modified: Int! + date_downloaded: DateTime! + _id: ObjectId + url: String! + modifiedBy: String +} + +input SubmissionNlp_similar_incidentQueryInput { + incident_id_gt: Int + similarity_lte: Float + incident_id_lt: Int + similarity_exists: Boolean + incident_id_exists: Boolean + incident_id_gte: Int + incident_id: Int + similarity_ne: Float + incident_id_ne: Int + similarity: Float + similarity_gt: Float + similarity_lt: Float + OR: [SubmissionNlp_similar_incidentQueryInput!] + incident_id_in: [Int] + incident_id_lte: Int + incident_id_nin: [Int] + similarity_in: [Float] + similarity_gte: Float + similarity_nin: [Float] + AND: [SubmissionNlp_similar_incidentQueryInput!] +} + +input TaxaField_listQueryInput { + field_number_gte: String field_number: String + mongo_type_gte: String + mongo_type_lt: String + short_name_in: [String] + long_name_ne: String + weight_gte: Int + default_in: [String] + mongo_type_nin: [String] + required_exists: Boolean + short_description: String + default_gte: String + permitted_values_nin: [String] + long_name_nin: [String] + field_number_lt: String + weight_gt: Int + display_type_gt: String + long_description_lt: String + weight: Int + long_description_in: [String] + placeholder_nin: [String] + complete_from_exists: Boolean + display_type: String + placeholder_lte: String + display_type_exists: Boolean + long_description: String + weight_lt: Int + default_ne: String + public_exists: Boolean + short_description_nin: [String] + permitted_values_exists: Boolean + mongo_type_gt: String + long_description_ne: String + placeholder_lt: String + AND: [TaxaField_listQueryInput!] + required_ne: Boolean + required: Boolean + weight_in: [Int] + display_type_in: [String] + display_type_nin: [String] + short_name_lte: String + short_description_in: [String] + display_type_lt: String + field_number_lte: String + mongo_type: String + default_gt: String + placeholder_exists: Boolean + short_name_lt: String + long_description_lte: String + placeholder_ne: String + mongo_type_exists: Boolean + instant_facet_exists: Boolean + long_name_gte: String + short_name_nin: [String] + long_name_lt: String + mongo_type_lte: String + instant_facet_ne: Boolean short_name: String + OR: [TaxaField_listQueryInput!] + placeholder_in: [String] + hide_search_exists: Boolean + long_name_in: [String] + default_lt: String + item_fields: TaxaField_listItem_fieldQueryInput + long_description_gt: String + placeholder: String + default_lte: String + long_description_nin: [String] + long_name_gt: String + mongo_type_in: [String] + short_description_gte: String + default: String + display_type_lte: String + long_name_exists: Boolean + hide_search_ne: Boolean + field_number_nin: [String] + long_name: String + permitted_values_in: [String] + weight_ne: Int + field_number_in: [String] + weight_nin: [Int] + short_description_lte: String + short_name_exists: Boolean + weight_lte: Int + short_description_ne: String + long_description_gte: String + field_number_ne: String + permitted_values: [String] + short_description_exists: Boolean + public_ne: Boolean + instant_facet: Boolean + short_name_ne: String + short_description_gt: String + default_nin: [String] + weight_exists: Boolean + complete_from: TaxaField_listComplete_fromQueryInput + hide_search: Boolean + short_name_gt: String + short_name_gte: String + public: Boolean + default_exists: Boolean + long_name_lte: String + placeholder_gte: String + display_type_ne: String + short_description_lt: String + item_fields_exists: Boolean + field_number_gt: String + display_type_gte: String + placeholder_gt: String + field_number_exists: Boolean + long_description_exists: Boolean + mongo_type_ne: String } -input CandidateQueryInput { - _id_in: [ObjectId] - matching_entities_in: [String] - plain_text_exists: Boolean - url_gte: String - similarity_lt: Float - matching_harm_keywords_exists: Boolean - date_downloaded_nin: [String] - epoch_date_downloaded_nin: [Int] - image_url_gt: String - language: String - text_lt: String - date_downloaded: String - date_downloaded_lte: String - epoch_date_downloaded_in: [Int] - source_domain_nin: [String] - date_downloaded_lt: String - _id_gte: ObjectId - language_gt: String - match_ne: Boolean - text: String - title: String - text_lte: String +input History_incidentInsertInput { + tsne: History_incidentTsneInsertInput + incident_id: Int! + title: String! + AllegedDeveloperOfAISystem: [String] + _id: ObjectId + modifiedBy: String + editor_notes: String + description: String + editors: [String]! + AllegedDeployerOfAISystem: [String] + implicated_systems: [String] + date: String! + editor_similar_incidents: [Int] + nlp_similar_incidents: [History_incidentNlp_similar_incidentInsertInput] + AllegedHarmedOrNearlyHarmedParties: [String] + embedding: History_incidentEmbeddingInsertInput + epoch_date_modified: Int + flagged_dissimilar_incidents: [Int] + editor_dissimilar_incidents: [Int] + reports: [Int]! +} + +input History_incidentEmbeddingInsertInput { + vector: [Float] + from_reports: [Int] +} + +input SubscriptionIncident_idRelationInput { + create: IncidentInsertInput + link: Int +} + +input TaxaField_listItem_fieldUpdateInput { + required_unset: Boolean + complete_from_unset: Boolean + instant_facet: Boolean + weight_inc: Int + long_description_unset: Boolean + short_name_unset: Boolean + display_type_unset: Boolean + short_description: String + short_name: String + permitted_values_unset: Boolean + public_unset: Boolean + mongo_type_unset: Boolean + long_name_unset: Boolean + short_description_unset: Boolean + mongo_type: String + placeholder_unset: Boolean + instant_facet_unset: Boolean + field_number_unset: Boolean + long_name: String + display_type: String + placeholder: String + public: Boolean + field_number: String + default_unset: Boolean + complete_from: TaxaField_listItem_fieldComplete_fromUpdateInput + weight_unset: Boolean + long_description: String + default: String + permitted_values: [String] + weight: Int + required: Boolean +} + +input TaxaField_listComplete_fromUpdateInput { + all: [String] + all_unset: Boolean + current: [String] + current_unset: Boolean +} + +input TaxaQueryInput { + namespace: String + namespace_gt: String + _id: ObjectId _id_exists: Boolean - matching_entities_exists: Boolean - _id_nin: [ObjectId] - date_downloaded_ne: String - date_published: String - date_downloaded_gt: String - date_downloaded_exists: Boolean + weight_gt: Int + weight_ne: Int + namespace_in: [String] + namespace_nin: [String] _id_ne: ObjectId - title_nin: [String] - date_published_exists: Boolean - image_url_gte: String - plain_text_lt: String - epoch_date_published_nin: [Int] + dummy_fields_exists: Boolean + weight_exists: Boolean + namespace_gte: String + description_nin: [String] + field_list_in: [TaxaField_listQueryInput] + description_lt: String + _id_gte: ObjectId + namespace_ne: String _id_gt: ObjectId - title_lte: String - authors_nin: [String] - source_domain_exists: Boolean - classification_similarity_exists: Boolean - authors_exists: Boolean - image_url_exists: Boolean - url_nin: [String] - source_domain_lt: String - language_gte: String - plain_text_in: [String] - epoch_date_published_in: [Int] + complete_entities_exists: Boolean + weight_in: [Int] _id_lte: ObjectId - title_in: [String] - epoch_date_downloaded_exists: Boolean - match: Boolean - plain_text: String - source_domain_ne: String + description_gte: String + OR: [TaxaQueryInput!] + description_in: [String] + dummy_fields_nin: [TaxaDummy_fieldQueryInput] + field_list_exists: Boolean + description_exists: Boolean + dummy_fields: [TaxaDummy_fieldQueryInput] + description_ne: String + description_gt: String + _id_in: [ObjectId] + dummy_fields_in: [TaxaDummy_fieldQueryInput] + field_list_nin: [TaxaField_listQueryInput] _id_lt: ObjectId - classification_similarity_in: [CandidateClassification_similarityQueryInput] - date_published_lte: String - matching_keywords: [String] - _id: ObjectId - text_gt: String - epoch_date_downloaded_ne: Int - date_published_ne: String - source_domain: String - plain_text_ne: String - title_gte: String - epoch_date_downloaded_lte: Int - matching_entities_nin: [String] - language_nin: [String] - AND: [CandidateQueryInput!] - plain_text_nin: [String] - url_ne: String - source_domain_gt: String - image_url_lte: String - date_published_gte: String - image_url_ne: String - title_lt: String - matching_harm_keywords_in: [String] - epoch_date_downloaded_lt: Int - authors: [String] - text_gte: String - url_lte: String - similarity: Float - epoch_date_published_gte: Int - source_domain_gte: String - date_downloaded_gte: String + namespace_lte: String + complete_entities_ne: Boolean + _id_nin: [ObjectId] + namespace_lt: String + AND: [TaxaQueryInput!] + complete_entities: Boolean + description: String + weight: Int + namespace_exists: Boolean + weight_lt: Int + weight_nin: [Int] + weight_lte: Int + weight_gte: Int + description_lte: String + field_list: [TaxaField_listQueryInput] +} + +input CandidateClassification_similarityQueryInput { + classification_gte: String + similarity_in: [Float] + similarity_gt: Float + classification_lte: String + classification: String + classification_gt: String + similarity_lt: Float similarity_nin: [Float] - url_in: [String] - source_domain_lte: String - epoch_date_downloaded_gte: Int + similarity_exists: Boolean similarity_gte: Float - similarity_ne: Float - date_downloaded_in: [String] - matching_harm_keywords: [String] - matching_harm_keywords_nin: [String] - text_exists: Boolean - dismissed: Boolean - epoch_date_downloaded: Int - epoch_date_published_exists: Boolean - epoch_date_published: Int - image_url: String - image_url_in: [String] - authors_in: [String] - matching_entities: [String] - matching_keywords_exists: Boolean - epoch_date_published_lt: Int - matching_keywords_nin: [String] - date_published_in: [String] - source_domain_in: [String] - language_ne: String - similarity_gt: Float - plain_text_gte: String - epoch_date_published_ne: Int - epoch_date_downloaded_gt: Int - embedding: CandidateEmbeddingQueryInput - text_in: [String] - epoch_date_published_gt: Int + OR: [CandidateClassification_similarityQueryInput!] similarity_lte: Float - match_exists: Boolean - language_exists: Boolean - text_ne: String - plain_text_lte: String - embedding_exists: Boolean - date_published_nin: [String] - image_url_nin: [String] - language_in: [String] - url: String - date_published_lt: String - date_published_gt: String - similarity_exists: Boolean - dismissed_exists: Boolean - text_nin: [String] - image_url_lt: String - url_lt: String - language_lt: String - classification_similarity: [CandidateClassification_similarityQueryInput] - matching_keywords_in: [String] - epoch_date_published_lte: Int - dismissed_ne: Boolean - classification_similarity_nin: [CandidateClassification_similarityQueryInput] - language_lte: String - plain_text_gt: String - OR: [CandidateQueryInput!] - url_exists: Boolean + classification_ne: String + classification_lt: String + similarity_ne: Float + classification_nin: [String] + classification_in: [String] + AND: [CandidateClassification_similarityQueryInput!] + classification_exists: Boolean + similarity: Float +} + +input ChecklistRiskQueryInput { + title_nin: [String] + id_ne: String + risk_notes_gte: String + risk_status_lte: String + generated: Boolean + touched: Boolean + severity_lt: String + AND: [ChecklistRiskQueryInput!] + severity: String + risk_status_in: [String] + severity_ne: String + touched_ne: Boolean + likelihood_in: [String] + risk_status_gt: String + risk_status_exists: Boolean + likelihood_gte: String + likelihood_lte: String + risk_status_gte: String + severity_exists: Boolean + likelihood: String + id_gt: String + risk_notes_exists: Boolean + id_in: [String] + risk_status_nin: [String] + severity_nin: [String] + likelihood_lt: String + severity_in: [String] + precedents_in: [ChecklistRiskPrecedentQueryInput] + generated_exists: Boolean + touched_exists: Boolean + likelihood_ne: String + id_nin: [String] + title_lt: String + id: String + likelihood_exists: Boolean + risk_notes: String + title_lte: String + generated_ne: Boolean + risk_notes_in: [String] + severity_lte: String + risk_notes_nin: [String] + title_in: [String] title_ne: String - similarity_in: [Float] - url_gt: String - title_exists: Boolean + risk_notes_ne: String + risk_notes_lt: String + tags: [String] + id_exists: Boolean + id_lte: String + OR: [ChecklistRiskQueryInput!] + severity_gt: String + risk_status: String + id_gte: String title_gt: String -} - -enum TaxaSortByInput { - NAMESPACE_ASC - NAMESPACE_DESC - WEIGHT_ASC - WEIGHT_DESC - _ID_ASC - _ID_DESC - DESCRIPTION_ASC - DESCRIPTION_DESC -} - -input CandidateUpdateInput { - classification_similarity_unset: Boolean - source_domain_unset: Boolean - epoch_date_published_unset: Boolean - match_unset: Boolean - matching_keywords_unset: Boolean - matching_harm_keywords_unset: Boolean - similarity_unset: Boolean - _id: ObjectId + precedents_nin: [ChecklistRiskPrecedentQueryInput] title: String - similarity_inc: Float - epoch_date_downloaded: Int - language_unset: Boolean - date_downloaded: String - matching_keywords: [String] - date_published_unset: Boolean - authors_unset: Boolean - authors: [String] - matching_entities_unset: Boolean - source_domain: String - language: String - title_unset: Boolean - date_published: String - plain_text: String - dismissed_unset: Boolean - embedding_unset: Boolean - url: String - text: String - dismissed: Boolean - plain_text_unset: Boolean - match: Boolean - matching_entities: [String] - epoch_date_published_inc: Int - similarity: Float - date_downloaded_unset: Boolean - epoch_date_downloaded_unset: Boolean - matching_harm_keywords: [String] - classification_similarity: [CandidateClassification_similarityUpdateInput] - url_unset: Boolean - image_url: String - image_url_unset: Boolean - text_unset: Boolean - _id_unset: Boolean - embedding: CandidateEmbeddingUpdateInput - epoch_date_downloaded_inc: Int - epoch_date_published: Int + risk_status_lt: String + risk_notes_gt: String + tags_exists: Boolean + title_exists: Boolean + risk_status_ne: String + risk_notes_lte: String + precedents_exists: Boolean + likelihood_gt: String + id_lt: String + tags_nin: [String] + severity_gte: String + tags_in: [String] + title_gte: String + precedents: [ChecklistRiskPrecedentQueryInput] + likelihood_nin: [String] } -input IncidentEmbeddingUpdateInput { +type RisksPayloadPrecedentEmbedding { from_reports: [Int] - from_reports_unset: Boolean vector: [Float] - vector_unset: Boolean } -type ReportEmbedding { - from_text_hash: String - vector: [Float] +input UserInsertInput { + _id: ObjectId + first_name: String + last_name: String + roles: [String]! + userId: String! } -enum ReportSortByInput { - CLOUDINARY_ID_DESC - EPOCH_DATE_PUBLISHED_DESC - EPOCH_DATE_SUBMITTED_DESC - IMAGE_URL_DESC - LANGUAGE_ASC - REPORT_NUMBER_DESC - TEXT_ASC - DATE_DOWNLOADED_ASC - DATE_MODIFIED_ASC - DATE_MODIFIED_DESC - DATE_SUBMITTED_DESC - EPOCH_DATE_DOWNLOADED_ASC - IMAGE_URL_ASC - LANGUAGE_DESC - DATE_DOWNLOADED_DESC - PLAIN_TEXT_DESC - URL_DESC - USER_ASC - TITLE_ASC - DATE_PUBLISHED_DESC - DESCRIPTION_ASC - EDITOR_NOTES_ASC - EDITOR_NOTES_DESC - EPOCH_DATE_MODIFIED_DESC - EPOCH_DATE_SUBMITTED_ASC - REPORT_NUMBER_ASC - URL_ASC - EPOCH_DATE_MODIFIED_ASC - TEXT_DESC - CLOUDINARY_ID_ASC - DATE_SUBMITTED_ASC - PLAIN_TEXT_ASC - SOURCE_DOMAIN_ASC - SOURCE_DOMAIN_DESC - TITLE_DESC - USER_DESC - DATE_PUBLISHED_ASC - DESCRIPTION_DESC - EPOCH_DATE_DOWNLOADED_DESC - EPOCH_DATE_PUBLISHED_ASC - _ID_ASC - _ID_DESC +input ClassificationIncidentsRelationInput { + create: [IncidentInsertInput] + link: [Int] +} + +input TaxaField_listItem_fieldComplete_fromUpdateInput { + all_unset: Boolean + current: [String] + current_unset: Boolean + entities: Boolean + entities_unset: Boolean + all: [String] } input History_reportQueryInput { - _id_lt: ObjectId - tags: [String] - epoch_date_downloaded_ne: Int - date_published_ne: DateTime - date_published_exists: Boolean - epoch_date_submitted_ne: Int - epoch_date_published_lte: Int - inputs_outputs_exists: Boolean - cloudinary_id_in: [String] - AND: [History_reportQueryInput!] - cloudinary_id_lt: String - epoch_date_modified_ne: Int - editor_notes_nin: [String] - epoch_date_modified_exists: Boolean - url_gt: String - image_url_lte: String - epoch_date_downloaded_exists: Boolean - embedding_exists: Boolean - date_downloaded_gt: DateTime text: String - title: String - epoch_date_modified_gte: Int - epoch_date_submitted_lt: Int - plain_text_gte: String - date_downloaded_nin: [DateTime] - epoch_date_modified_in: [Int] - date_submitted_ne: DateTime - epoch_date_downloaded_nin: [Int] - source_domain: String - user_ne: String - image_url_nin: [String] - _id_gte: ObjectId - date_modified_exists: Boolean - url_exists: Boolean - user_gt: String - modifiedBy_lt: String - report_number_gt: Int - date_downloaded_in: [DateTime] - language_gt: String - inputs_outputs_nin: [String] - user_nin: [String] - description_lt: String - epoch_date_submitted_lte: Int - epoch_date_published: Int - editor_notes_ne: String - _id_lte: ObjectId - quiet_exists: Boolean - date_published_lt: DateTime - epoch_date_downloaded_gt: Int - report_number_exists: Boolean - source_domain_ne: String - epoch_date_modified_gt: Int - text_ne: String - plain_text_ne: String - title_lte: String + editor_notes_in: [String] plain_text_nin: [String] + epoch_date_modified_lt: Int + date_published_gt: DateTime + _id_gt: ObjectId + plain_text_gt: String cloudinary_id_lte: String - source_domain_in: [String] - date_submitted_lt: DateTime - epoch_date_submitted: Int - authors_exists: Boolean - url_lt: String - modifiedBy_gt: String - title_nin: [String] - image_url_exists: Boolean + epoch_date_modified_exists: Boolean + modifiedBy_ne: String + report_number_lte: Int + title_ne: String + epoch_date_published_lt: Int + modifiedBy_nin: [String] language_nin: [String] - source_domain_nin: [String] date_modified_ne: DateTime - text_nin: [String] - modifiedBy_gte: String + description: String + plain_text: String + image_url: String report_number_in: [Int] - _id_ne: ObjectId - date_submitted_exists: Boolean - _id_gt: ObjectId + report_number_lt: Int + epoch_date_modified_gte: Int + editor_notes: String cloudinary_id_gt: String - user_lt: String - date_published_lte: DateTime - tags_in: [String] - plain_text_lt: String - source_domain_exists: Boolean - date_submitted_gte: DateTime - date_downloaded_ne: DateTime - user_exists: Boolean - url: String - is_incident_report_exists: Boolean - title_ne: String + title_gt: String + source_domain_gte: String + _id_lte: ObjectId + language_exists: Boolean + date_published_gte: DateTime + language_gt: String + date_modified_gt: DateTime + language_ne: String + title_lte: String + epoch_date_published_lte: Int + epoch_date_submitted_nin: [Int] + user_gt: String + report_number: Int + source_domain_gt: String modifiedBy: String - epoch_date_published_ne: Int - user_gte: String - submitters_nin: [String] - OR: [History_reportQueryInput!] - description_gte: String - source_domain_gte: String - _id_exists: Boolean + text_ne: String text_lte: String - authors_nin: [String] - cloudinary_id_gte: String - description_lte: String - flag: Boolean - date_modified_lte: DateTime + cloudinary_id_lt: String cloudinary_id_ne: String - date_submitted_in: [DateTime] - submitters_in: [String] - text_lt: String - date_downloaded_lte: DateTime - date_published_in: [DateTime] - date_downloaded: DateTime - report_number_gte: Int - image_url: String - description_ne: String - language_in: [String] - epoch_date_downloaded_lt: Int - language: String - description: String - date_modified_gte: DateTime - image_url_gte: String - image_url_ne: String - date_downloaded_exists: Boolean - date_submitted_lte: DateTime - epoch_date_published_exists: Boolean - epoch_date_downloaded_gte: Int + epoch_date_submitted_lte: Int + _id: ObjectId user: String - date_published_gte: DateTime + report_number_nin: [Int] + date_modified_in: [DateTime] + description_lt: String + modifiedBy_lte: String + epoch_date_downloaded_exists: Boolean + description_ne: String inputs_outputs_in: [String] - quiet: Boolean - language_lt: String - url_in: [String] - date_modified_lt: DateTime - is_incident_report_ne: Boolean - text_exists: Boolean - epoch_date_modified_nin: [Int] - language_gte: String - date_downloaded_lt: DateTime + submitters: [String] + embedding_exists: Boolean date_downloaded_gte: DateTime - plain_text_gt: String - epoch_date_submitted_gt: Int - url_lte: String - date_submitted_nin: [DateTime] - text_in: [String] - epoch_date_submitted_exists: Boolean - report_number_lte: Int - modifiedBy_ne: String - is_incident_report: Boolean - submitters_exists: Boolean + _id_in: [ObjectId] + image_url_in: [String] + authors_exists: Boolean + description_lte: String + image_url_lte: String + is_incident_report_exists: Boolean + description_gt: String + modifiedBy_gt: String + date_submitted_exists: Boolean + modifiedBy_exists: Boolean + report_number_gte: Int + submitters_in: [String] + date_submitted_lte: DateTime + epoch_date_modified_nin: [Int] epoch_date_modified_lte: Int - embedding: History_reportEmbeddingQueryInput - editor_notes_gt: String - quiet_ne: Boolean - editor_notes_gte: String - authors_in: [String] - plain_text_lte: String - editor_notes_exists: Boolean - url_nin: [String] - authors: [String] - cloudinary_id: String - report_number_nin: [Int] - report_number_lt: Int - url_ne: String - title_gt: String - plain_text: String - epoch_date_published_gt: Int - epoch_date_published_in: [Int] text_gte: String - flag_exists: Boolean + cloudinary_id_gte: String + source_domain_exists: Boolean + editor_notes_nin: [String] + modifiedBy_lt: String + date_published_in: [DateTime] + title_in: [String] + epoch_date_modified_in: [Int] + title: String + epoch_date_downloaded_in: [Int] + date_downloaded_ne: DateTime + date_published_exists: Boolean + modifiedBy_gte: String + image_url_exists: Boolean + date_downloaded_in: [DateTime] + epoch_date_submitted_lt: Int + text_lt: String + cloudinary_id_nin: [String] + date_submitted: DateTime + epoch_date_submitted_ne: Int + image_url_gt: String + epoch_date_modified: Int + source_domain_ne: String + epoch_date_downloaded: Int + user_ne: String + epoch_date_downloaded_lte: Int + source_domain_in: [String] + date_published: DateTime + text_nin: [String] report_number_ne: Int - date_published_gt: DateTime - source_domain_gt: String - description_gt: String - modifiedBy_in: [String] - title_gte: String + source_domain_nin: [String] + description_gte: String + tags_exists: Boolean + date_modified_lte: DateTime + epoch_date_published_exists: Boolean epoch_date_submitted_in: [Int] - epoch_date_submitted_nin: [Int] - date_submitted_gt: DateTime - epoch_date_modified_lt: Int - image_url_gt: String - _id_in: [ObjectId] + _id_gte: ObjectId + epoch_date_published_ne: Int + url_lte: String + text_in: [String] + date_downloaded_lt: DateTime + report_number_exists: Boolean + url_exists: Boolean + AND: [History_reportQueryInput!] + _id_lt: ObjectId + submitters_nin: [String] + date_submitted_gte: DateTime + OR: [History_reportQueryInput!] + source_domain: String + modifiedBy_in: [String] + date_downloaded_exists: Boolean + date_modified_exists: Boolean + epoch_date_published_in: [Int] plain_text_exists: Boolean - language_exists: Boolean + language_gte: String editor_notes_lte: String - epoch_date_published_lt: Int - date_published_nin: [DateTime] - modifiedBy_nin: [String] - epoch_date_downloaded_lte: Int - title_lt: String - editor_notes_lt: String - url_gte: String - modifiedBy_lte: String - report_number: Int - date_submitted: DateTime + description_in: [String] + language_in: [String] + url_ne: String + date_published_lte: DateTime + tags: [String] + epoch_date_submitted_gte: Int + flag_exists: Boolean + is_incident_report_ne: Boolean + url_gt: String + epoch_date_downloaded_gte: Int + inputs_outputs: [String] + language_lt: String + date_modified_nin: [DateTime] + editor_notes_exists: Boolean + epoch_date_published_gt: Int + date_modified_gte: DateTime + language_lte: String source_domain_lte: String - date_modified_gt: DateTime - description_exists: Boolean + cloudinary_id_in: [String] + image_url_lt: String + url_nin: [String] + tags_in: [String] + url_lt: String + epoch_date_published: Int + inputs_outputs_nin: [String] + editor_notes_gte: String + user_exists: Boolean + url: String + date_modified: DateTime + user_in: [String] + authors: [String] + plain_text_gte: String + language: String + date_published_lt: DateTime + epoch_date_modified_gt: Int + submitters_exists: Boolean _id_nin: [ObjectId] - text_gt: String - epoch_date_published_nin: [Int] - description_in: [String] + url_gte: String + epoch_date_published_gte: Int + date_downloaded_gt: DateTime description_nin: [String] - editor_notes_in: [String] - date_published: DateTime - flag_ne: Boolean - source_domain_lt: String - title_in: [String] - epoch_date_modified: Int + plain_text_ne: String + inputs_outputs_exists: Boolean + quiet_exists: Boolean + user_lt: String + authors_in: [String] + date_submitted_lt: DateTime + image_url_gte: String + title_lt: String + quiet_ne: Boolean + title_gte: String plain_text_in: [String] - epoch_date_published_gte: Int - image_url_lt: String - cloudinary_id_exists: Boolean - editor_notes: String - user_in: [String] - tags_exists: Boolean + date_submitted_in: [DateTime] + epoch_date_downloaded_ne: Int + cloudinary_id: String + source_domain_lt: String + tags_nin: [String] + epoch_date_submitted: Int + plain_text_lte: String + image_url_nin: [String] user_lte: String - inputs_outputs: [String] - epoch_date_downloaded_in: [Int] - date_modified_nin: [DateTime] - cloudinary_id_nin: [String] - date_modified: DateTime + epoch_date_published_nin: [Int] + image_url_ne: String + date_published_nin: [DateTime] + cloudinary_id_exists: Boolean + quiet: Boolean + title_nin: [String] + _id_ne: ObjectId + date_downloaded: DateTime + epoch_date_downloaded_gt: Int + text_exists: Boolean + epoch_date_submitted_gt: Int + editor_notes_lt: String + url_in: [String] title_exists: Boolean - modifiedBy_exists: Boolean - epoch_date_submitted_gte: Int - language_lte: String - date_modified_in: [DateTime] + user_nin: [String] + _id_exists: Boolean + text_gt: String + user_gte: String + epoch_date_downloaded_lt: Int + date_submitted_nin: [DateTime] + flag: Boolean + epoch_date_downloaded_nin: [Int] + description_exists: Boolean + date_submitted_ne: DateTime + is_incident_report: Boolean + plain_text_lt: String + editor_notes_gt: String + date_downloaded_nin: [DateTime] + embedding: History_reportEmbeddingQueryInput + date_submitted_gt: DateTime + report_number_gt: Int + authors_nin: [String] + editor_notes_ne: String + flag_ne: Boolean + epoch_date_modified_ne: Int + date_modified_lt: DateTime + date_published_ne: DateTime + date_downloaded_lte: DateTime + epoch_date_submitted_exists: Boolean +} + +type TaxaDummy_field { + field_number: String + short_name: String +} + +type TaxaField_listItem_field { + complete_from: TaxaField_listItem_fieldComplete_from + default: String + display_type: String + field_number: String + instant_facet: Boolean + long_description: String + long_name: String + mongo_type: String + permitted_values: [String] + placeholder: String + public: Boolean + required: Boolean + short_description: String + short_name: String + weight: Int +} + +type TaxaField_list { + complete_from: TaxaField_listComplete_from + default: String + display_type: String + field_number: String + hide_search: Boolean + instant_facet: Boolean + item_fields: TaxaField_listItem_field + long_description: String + long_name: String + mongo_type: String + permitted_values: [String] + placeholder: String + public: Boolean + required: Boolean + short_description: String + short_name: String + weight: Int +} + +input ClassificationQueryInput { + notes_gte: String + reports_nin: [ReportQueryInput] _id: ObjectId - submitters: [String] - epoch_date_downloaded: Int - tags_nin: [String] - image_url_in: [String] - language_ne: String + namespace_gte: String + notes_exists: Boolean + attributes_in: [ClassificationAttributeQueryInput] + publish_ne: Boolean + namespace: String + namespace_nin: [String] + incidents_nin: [IncidentQueryInput] + _id_gte: ObjectId + notes: String + AND: [ClassificationQueryInput!] + _id_gt: ObjectId + publish: Boolean + attributes: [ClassificationAttributeQueryInput] + reports_in: [ReportQueryInput] + _id_in: [ObjectId] + notes_lt: String + _id_exists: Boolean + namespace_lte: String + _id_lte: ObjectId + _id_ne: ObjectId + incidents: [IncidentQueryInput] + OR: [ClassificationQueryInput!] + incidents_in: [IncidentQueryInput] + incidents_exists: Boolean + notes_ne: String + notes_nin: [String] + notes_gt: String + attributes_exists: Boolean + namespace_in: [String] + namespace_exists: Boolean + notes_in: [String] + namespace_ne: String + publish_exists: Boolean + namespace_gt: String + reports_exists: Boolean + _id_nin: [ObjectId] + reports: [ReportQueryInput] + _id_lt: ObjectId + namespace_lt: String + notes_lte: String + attributes_nin: [ClassificationAttributeQueryInput] } -input IncidentInsertInput { - title: String! - editor_notes: String - editor_dissimilar_incidents: [Int] - tsne: IncidentTsneInsertInput - description: String - AllegedHarmedOrNearlyHarmedParties: IncidentAllegedHarmedOrNearlyHarmedPartiesRelationInput - nlp_similar_incidents: [IncidentNlp_similar_incidentInsertInput] - editors: IncidentEditorsRelationInput! - reports: IncidentReportsRelationInput! - flagged_dissimilar_incidents: [Int] - editor_similar_incidents: [Int] - AllegedDeveloperOfAISystem: IncidentAllegedDeveloperOfAISystemRelationInput +input DuplicateUpdateInput { _id: ObjectId - epoch_date_modified: Int - embedding: IncidentEmbeddingInsertInput - incident_id: Int! - AllegedDeployerOfAISystem: IncidentAllegedDeployerOfAISystemRelationInput - date: String! + _id_unset: Boolean + duplicate_incident_number: Int + duplicate_incident_number_inc: Int + duplicate_incident_number_unset: Boolean + true_incident_number: Int + true_incident_number_inc: Int + true_incident_number_unset: Boolean } -input IncidentEditorsRelationInput { - create: [UserInsertInput] - link: [String] +input TaxaInsertInput { + description: String + dummy_fields: [TaxaDummy_fieldInsertInput] + field_list: [TaxaField_listInsertInput] + namespace: String + weight: Int + _id: ObjectId + complete_entities: Boolean } -type ReportTranslation { - text: String - title: String +type IncidentTsne { + x: Float + y: Float } -enum SubscriptionSortByInput { - TYPE_ASC - TYPE_DESC - USERID_ASC +enum QuickaddSortByInput { + SOURCE_DOMAIN_ASC + SOURCE_DOMAIN_DESC + URL_DESC _ID_ASC - ENTITYID_ASC - ENTITYID_DESC - USERID_DESC _ID_DESC + DATE_SUBMITTED_DESC INCIDENT_ID_ASC INCIDENT_ID_DESC + DATE_SUBMITTED_ASC + URL_ASC } -enum ChecklistSortByInput { - DATE_CREATED_DESC - ID_DESC - NAME_DESC +enum History_reportSortByInput { + DATE_SUBMITTED_ASC + DATE_SUBMITTED_DESC + DESCRIPTION_DESC + MODIFIEDBY_ASC + MODIFIEDBY_DESC + DATE_MODIFIED_ASC + EPOCH_DATE_SUBMITTED_DESC + IMAGE_URL_ASC + LANGUAGE_ASC + PLAIN_TEXT_DESC + URL_DESC _ID_DESC - ABOUT_DESC - ENTITY_ID_ASC - ENTITY_ID_DESC - ID_ASC - _ID_ASC - DATE_UPDATED_ASC - DATE_UPDATED_DESC - OWNER_ID_ASC - OWNER_ID_DESC - ABOUT_ASC - DATE_CREATED_ASC - NAME_ASC -} - -input IncidentNlp_similar_incidentUpdateInput { - similarity_unset: Boolean - incident_id: Int - incident_id_inc: Int - incident_id_unset: Boolean - similarity: Float - similarity_inc: Float -} - -input CandidateEmbeddingUpdateInput { - vector_unset: Boolean - from_text_hash: String - from_text_hash_unset: Boolean - vector: [Float] -} - -input IncidentTsneQueryInput { - y_gte: Float - x_nin: [Float] - y_nin: [Float] - x_in: [Float] - x_ne: Float - x_gt: Float - y_in: [Float] - OR: [IncidentTsneQueryInput!] - y_lt: Float - x: Float - y_exists: Boolean - y: Float - y_lte: Float - x_gte: Float - x_lt: Float - y_gt: Float - y_ne: Float - x_exists: Boolean - x_lte: Float - AND: [IncidentTsneQueryInput!] + DATE_DOWNLOADED_ASC + EPOCH_DATE_PUBLISHED_ASC + EPOCH_DATE_PUBLISHED_DESC + REPORT_NUMBER_DESC + EPOCH_DATE_DOWNLOADED_ASC + PLAIN_TEXT_ASC + SOURCE_DOMAIN_ASC + TEXT_DESC + CLOUDINARY_ID_DESC + DATE_PUBLISHED_DESC + IMAGE_URL_DESC + REPORT_NUMBER_ASC + TITLE_ASC + TITLE_DESC + CLOUDINARY_ID_ASC + DATE_DOWNLOADED_DESC + DATE_PUBLISHED_ASC + EDITOR_NOTES_DESC + EPOCH_DATE_DOWNLOADED_DESC + EPOCH_DATE_MODIFIED_DESC + EPOCH_DATE_SUBMITTED_ASC + DESCRIPTION_ASC + EPOCH_DATE_MODIFIED_ASC + LANGUAGE_DESC + SOURCE_DOMAIN_DESC + URL_ASC + USER_ASC + _ID_ASC + DATE_MODIFIED_DESC + EDITOR_NOTES_ASC + TEXT_ASC + USER_DESC } -input ChecklistRiskPrecedentQueryInput { - title_nin: [String] - description_lt: String - title_gt: String - description: String - incident_id_lt: Int - incident_id_gt: Int - description_in: [String] - title_exists: Boolean - tags_exists: Boolean - title_lte: String - title_ne: String - description_lte: String - incident_id_lte: Int +input ChecklistRiskInsertInput { + severity: String title: String - description_gt: String - description_ne: String - description_nin: [String] - incident_id_gte: Int - description_gte: String - OR: [ChecklistRiskPrecedentQueryInput!] + id: String + generated: Boolean + risk_notes: String + risk_status: String + likelihood: String + precedents: [ChecklistRiskPrecedentInsertInput] tags: [String] - AND: [ChecklistRiskPrecedentQueryInput!] - incident_id_exists: Boolean - tags_nin: [String] - title_lt: String - tags_in: [String] - incident_id_in: [Int] - incident_id_ne: Int - incident_id: Int - incident_id_nin: [Int] - title_in: [String] - description_exists: Boolean - title_gte: String -} - -input History_incidentNlp_similar_incidentQueryInput { - OR: [History_incidentNlp_similar_incidentQueryInput!] - incident_id_lte: Int - incident_id_in: [Int] - similarity_gt: Float - incident_id_gte: Int - similarity_nin: [Float] - incident_id_lt: Int - similarity_lte: Float - similarity: Float - incident_id_gt: Int - similarity_lt: Float - similarity_ne: Float - similarity_in: [Float] - incident_id_exists: Boolean - similarity_gte: Float - incident_id: Int - incident_id_ne: Int - similarity_exists: Boolean - AND: [History_incidentNlp_similar_incidentQueryInput!] - incident_id_nin: [Int] + touched: Boolean } -type History_incidentTsne { +input History_incidentTsneUpdateInput { + y_inc: Float + y_unset: Boolean x: Float + x_inc: Float + x_unset: Boolean y: Float } -input History_incidentNlp_similar_incidentUpdateInput { - similarity_inc: Float - similarity_unset: Boolean - incident_id: Int - incident_id_inc: Int - incident_id_unset: Boolean - similarity: Float -} - -input ChecklistInsertInput { - about: String - date_updated: DateTime - id: String - tags_other: [String] - name: String - risks: [ChecklistRiskInsertInput] - tags_goals: [String] - _id: ObjectId - owner_id: String - tags_methods: [String] - date_created: DateTime - entity_id: String +type LogReportHistoryPayload { + report_number: Int } -input SubmissionEmbeddingQueryInput { - from_text_hash_gt: String - from_text_hash_ne: String - vector_exists: Boolean - from_text_hash: String - from_text_hash_gte: String - from_text_hash_lt: String - from_text_hash_in: [String] - from_text_hash_lte: String - vector_nin: [Float] - vector_in: [Float] - OR: [SubmissionEmbeddingQueryInput!] - from_text_hash_nin: [String] - from_text_hash_exists: Boolean - AND: [SubmissionEmbeddingQueryInput!] - vector: [Float] +input GetUserInput { + userId: ObjectId } -input IncidentEmbeddingInsertInput { - from_reports: [Int] - vector: [Float] +input IncidentAllegedDeployerOfAISystemRelationInput { + link: [String] + create: [EntityInsertInput] } -input History_incidentNlp_similar_incidentInsertInput { - incident_id: Int - similarity: Float +type Query { + candidate(query: CandidateQueryInput): Candidate + candidates(query: CandidateQueryInput, limit: Int = 100, sortBy: CandidateSortByInput): [Candidate]! + checklist(query: ChecklistQueryInput): Checklist + checklists(query: ChecklistQueryInput, limit: Int = 100, sortBy: ChecklistSortByInput): [Checklist]! + classification(query: ClassificationQueryInput): Classification + classifications(query: ClassificationQueryInput, limit: Int = 100, sortBy: ClassificationSortByInput): [Classification]! + duplicate(query: DuplicateQueryInput): Duplicate + duplicates(query: DuplicateQueryInput, limit: Int = 100, sortBy: DuplicateSortByInput): [Duplicate]! + entities(query: EntityQueryInput, limit: Int = 100, sortBy: EntitySortByInput): [Entity]! + entity(query: EntityQueryInput): Entity + history_incident(query: History_incidentQueryInput): History_incident + history_incidents(query: History_incidentQueryInput, limit: Int = 100, sortBy: History_incidentSortByInput): [History_incident]! + history_report(query: History_reportQueryInput): History_report + history_reports(limit: Int = 100, sortBy: History_reportSortByInput, query: History_reportQueryInput): [History_report]! + incident(query: IncidentQueryInput): Incident + incidents(query: IncidentQueryInput, limit: Int = 100, sortBy: IncidentSortByInput): [Incident]! + notification(query: NotificationQueryInput): Notification + notifications(query: NotificationQueryInput, limit: Int = 100, sortBy: NotificationSortByInput): [Notification]! + quickadd(query: QuickaddQueryInput): Quickadd + quickadds(query: QuickaddQueryInput, limit: Int = 100, sortBy: QuickaddSortByInput): [Quickadd]! + report(query: ReportQueryInput): Report + reports(query: ReportQueryInput, limit: Int = 100, sortBy: ReportSortByInput): [Report]! + risks(input: RisksInput): [RisksPayloadItem] + submission(query: SubmissionQueryInput): Submission + submissions(limit: Int = 100, sortBy: SubmissionSortByInput, query: SubmissionQueryInput): [Submission]! + subscription(query: SubscriptionQueryInput): Subscription + subscriptions(query: SubscriptionQueryInput, limit: Int = 100, sortBy: SubscriptionSortByInput): [Subscription]! + taxa(query: TaxaQueryInput): Taxa + taxas(query: TaxaQueryInput, limit: Int = 100, sortBy: TaxaSortByInput): [Taxa]! + user(query: UserQueryInput): User + users(query: UserQueryInput, limit: Int = 100, sortBy: UserSortByInput): [User]! } -input TaxaField_listQueryInput { - display_type_in: [String] - short_name_lt: String +input TaxaField_listItem_fieldQueryInput { + default_in: [String] + display_type_ne: String + display_type_lte: String + field_number_lte: String short_description_gt: String - required_exists: Boolean - mongo_type: String - AND: [TaxaField_listQueryInput!] - field_number_ne: String - placeholder_lte: String - long_name_lt: String - weight_lte: Int + required_ne: Boolean + weight_lt: Int default_lte: String - long_name_gt: String + mongo_type_lt: String display_type_nin: [String] - weight_ne: Int - instant_facet_ne: Boolean - short_description_exists: Boolean + weight_gt: Int + field_number_nin: [String] + display_type: String + long_description_in: [String] + mongo_type_exists: Boolean + placeholder_gte: String + default_ne: String + default_lt: String + placeholder_ne: String + placeholder_gt: String + short_name: String + long_description_exists: Boolean + field_number_in: [String] + instant_facet_exists: Boolean field_number_gte: String + long_description_nin: [String] + long_description_lte: String + short_name_lte: String field_number_exists: Boolean - permitted_values_nin: [String] - weight_nin: [Int] + short_description_gte: String + placeholder_lt: String + long_name_gte: String + mongo_type: String + display_type_lt: String + default_nin: [String] + mongo_type_ne: String + short_name_lt: String + long_name_exists: Boolean + mongo_type_lte: String + OR: [TaxaField_listItem_fieldQueryInput!] + long_name_lte: String + weight_gte: Int + default_exists: Boolean + default_gte: String + display_type_gte: String + instant_facet: Boolean + field_number_ne: String + long_name_ne: String + default_gt: String + permitted_values: [String] + long_description_ne: String + public: Boolean + mongo_type_nin: [String] + default: String + short_name_gt: String + weight: Int + short_name_exists: Boolean + long_description_gt: String + long_name_nin: [String] + long_description_gte: String + public_exists: Boolean + long_description: String + required: Boolean placeholder: String - short_name_gte: String + display_type_exists: Boolean mongo_type_in: [String] - item_fields: TaxaField_listItem_fieldQueryInput - instant_facet_exists: Boolean + weight_exists: Boolean + weight_nin: [Int] + placeholder_lte: String + short_description_exists: Boolean + long_name_gt: String weight_in: [Int] - placeholder_nin: [String] - long_description_in: [String] - required_ne: Boolean + short_description: String mongo_type_gt: String - default_in: [String] - display_type_ne: String + instant_facet_ne: Boolean + field_number_lt: String short_name_ne: String - public_exists: Boolean - long_name_exists: Boolean - instant_facet: Boolean - hide_search_ne: Boolean - short_description_gte: String - short_name: String - field_number_lte: String - long_description_nin: [String] - long_name_gte: String + long_name: String short_name_nin: [String] - weight_gt: Int - public: Boolean - OR: [TaxaField_listQueryInput!] - long_description_ne: String - field_number_in: [String] - weight_lt: Int - short_description_lt: String - long_description_gt: String - short_name_exists: Boolean - default_ne: String - weight_gte: Int - long_description_exists: Boolean - default: String + AND: [TaxaField_listItem_fieldQueryInput!] + long_name_in: [String] + placeholder_in: [String] + field_number_gt: String + long_description_lt: String short_description_ne: String - long_description: String - default_gte: String - long_description_lte: String - permitted_values_exists: Boolean - short_description_nin: [String] - default_exists: Boolean - short_description_lte: String - complete_from: TaxaField_listComplete_fromQueryInput - permitted_values_in: [String] + placeholder_nin: [String] + complete_from_exists: Boolean + short_description_lt: String mongo_type_gte: String - long_name_ne: String - mongo_type_lte: String - placeholder_ne: String - placeholder_exists: Boolean - field_number_gt: String - long_description_gte: String - placeholder_gt: String - permitted_values: [String] + weight_ne: Int + short_description_nin: [String] + display_type_in: [String] short_description_in: [String] - long_description_lt: String public_ne: Boolean - default_lt: String - default_nin: [String] - placeholder_in: [String] - long_name_in: [String] - display_type_lte: String - weight_exists: Boolean - item_fields_exists: Boolean - display_type_lt: String - mongo_type_lt: String - default_gt: String - display_type_exists: Boolean - placeholder_gte: String - short_name_gt: String - field_number_nin: [String] - display_type_gte: String - display_type: String - field_number_lt: String - complete_from_exists: Boolean - long_name_lte: String - short_name_lte: String - long_name_nin: [String] - long_name: String - required: Boolean + complete_from: TaxaField_listItem_fieldComplete_fromQueryInput + long_name_lt: String + required_exists: Boolean + permitted_values_nin: [String] + permitted_values_exists: Boolean + weight_lte: Int + short_name_gte: String short_name_in: [String] - field_number: String + permitted_values_in: [String] + short_description_lte: String display_type_gt: String - weight: Int - hide_search: Boolean - mongo_type_nin: [String] - mongo_type_exists: Boolean - placeholder_lt: String - short_description: String - hide_search_exists: Boolean - mongo_type_ne: String -} - -input UserInsertInput { - userId: String! - _id: ObjectId - first_name: String - last_name: String - roles: [String]! -} - -input IncidentNlp_similar_incidentInsertInput { - incident_id: Int - similarity: Float -} - -input ClassificationAttributeUpdateInput { - value_json_unset: Boolean - short_name: String - short_name_unset: Boolean - value_json: String -} - -input SubmissionUpdateInput { - plain_text_unset: Boolean - plain_text: String - developers_unset: Boolean - date_published: String - description_unset: Boolean - incident_title_unset: Boolean - user_unset: Boolean - embedding_unset: Boolean - image_url_unset: Boolean - date_downloaded: String - editor_dissimilar_incidents: [Int] - cloudinary_id: String - editor_similar_incidents_unset: Boolean - date_modified_unset: Boolean - language: String - nlp_similar_incidents_unset: Boolean - incident_editors_unset: Boolean - date_downloaded_unset: Boolean - status: String - embedding: SubmissionEmbeddingUpdateInput - url: String - editor_similar_incidents: [Int] - source_domain: String - text_unset: Boolean - harmed_parties_unset: Boolean - url_unset: Boolean - _id: ObjectId - title: String - authors: [String] - authors_unset: Boolean - cloudinary_id_unset: Boolean - language_unset: Boolean - status_unset: Boolean - developers: SubmissionDevelopersRelationInput - user: SubmissionUserRelationInput - title_unset: Boolean - incident_date_unset: Boolean - source_domain_unset: Boolean - submitters: [String] - incident_editors: SubmissionIncident_editorsRelationInput - editor_notes: String - deployers: SubmissionDeployersRelationInput - incident_ids: [Int] - incident_ids_unset: Boolean - epoch_date_modified: Int - editor_dissimilar_incidents_unset: Boolean - epoch_date_modified_inc: Int - tags_unset: Boolean - editor_notes_unset: Boolean - epoch_date_modified_unset: Boolean - submitters_unset: Boolean - date_submitted: String - deployers_unset: Boolean - image_url: String - date_published_unset: Boolean - incident_date: String - date_submitted_unset: Boolean - nlp_similar_incidents: [SubmissionNlp_similar_incidentUpdateInput] - text: String - incident_title: String - date_modified: String - quiet: Boolean - description: String - tags: [String] - quiet_unset: Boolean - harmed_parties: SubmissionHarmed_partiesRelationInput - _id_unset: Boolean -} - -enum DuplicateSortByInput { - DUPLICATE_INCIDENT_NUMBER_ASC - DUPLICATE_INCIDENT_NUMBER_DESC - TRUE_INCIDENT_NUMBER_ASC - TRUE_INCIDENT_NUMBER_DESC - _ID_ASC - _ID_DESC -} - -input CandidateClassification_similarityQueryInput { - classification_lt: String - similarity_gt: Float - OR: [CandidateClassification_similarityQueryInput!] - classification_ne: String - similarity_in: [Float] - similarity_exists: Boolean - classification_lte: String - classification_gt: String - classification_in: [String] - similarity: Float - similarity_lte: Float - classification_gte: String - similarity_nin: [Float] - classification_exists: Boolean - similarity_lt: Float - similarity_gte: Float - AND: [CandidateClassification_similarityQueryInput!] - classification: String - classification_nin: [String] - similarity_ne: Float + field_number: String + placeholder_exists: Boolean } input ReportQueryInput { - title_exists: Boolean - report_number_in: [Int] - flag: Boolean - epoch_date_submitted_nin: [Int] - report_number: Int - date_submitted_nin: [DateTime] source_domain_exists: Boolean - url_lte: String + cloudinary_id_exists: Boolean + image_url_gt: String + text: String + date_downloaded_ne: DateTime + plain_text_ne: String + date_downloaded_gte: DateTime + title_gt: String + date_published_gte: DateTime + date_submitted: DateTime + image_url_lte: String + _id_ne: ObjectId + epoch_date_downloaded: Int + report_number_gte: Int + source_domain_gt: String + date_downloaded_gt: DateTime + language_in: [String] + user_exists: Boolean + date_published_lte: DateTime + language_lte: String + url_in: [String] + epoch_date_published_gte: Int + epoch_date_published_exists: Boolean + inputs_outputs: [String] + epoch_date_downloaded_gte: Int + language_gte: String + epoch_date_submitted_nin: [Int] + date_modified_gte: DateTime + epoch_date_submitted_lt: Int + date_submitted_in: [DateTime] + epoch_date_published_lte: Int + inputs_outputs_nin: [String] date_published: DateTime - text: String + date_published_nin: [DateTime] + submitters_in: [String] embedding: ReportEmbeddingQueryInput - language_gt: String - language_lte: String - epoch_date_published_exists: Boolean - date_downloaded_lt: DateTime - flag_ne: Boolean - cloudinary_id_gt: String - tags_exists: Boolean - date_modified_lt: DateTime - _id_nin: [ObjectId] - editor_notes_ne: String - date_submitted_lt: DateTime - _id_gte: ObjectId + report_number_nin: [Int] + plain_text_lt: String + source_domain: String + date_submitted_lte: DateTime + date_published_ne: DateTime + url_exists: Boolean + epoch_date_modified_gte: Int + epoch_date_submitted_exists: Boolean + quiet_ne: Boolean + source_domain_lt: String + submitters_nin: [String] + title_nin: [String] + cloudinary_id_lt: String + editor_notes_gt: String + language_lt: String + description_nin: [String] + epoch_date_submitted_ne: Int + date_modified_in: [DateTime] + language: String + submitters_exists: Boolean _id: ObjectId - epoch_date_downloaded_gt: Int - epoch_date_published_in: [Int] + url: String + text_in: [String] + epoch_date_modified: Int + quiet: Boolean + url_gte: String + date_downloaded_lte: DateTime epoch_date_published_lt: Int - authors_exists: Boolean + language_nin: [String] + quiet_exists: Boolean + date_submitted_gt: DateTime + epoch_date_downloaded_nin: [Int] + url_gt: String + report_number_exists: Boolean + cloudinary_id_lte: String + source_domain_gte: String + editor_notes_gte: String + epoch_date_submitted_lte: Int + is_incident_report: Boolean + plain_text_exists: Boolean + url_lt: String + submitters: [String] + date_published_lt: DateTime epoch_date_published: Int - date_published_ne: DateTime - text_exists: Boolean - date_modified_gt: DateTime - inputs_outputs_in: [String] + description_lte: String + language_exists: Boolean + editor_notes: String + tags_exists: Boolean + _id_gt: ObjectId + date_submitted_nin: [DateTime] + cloudinary_id_nin: [String] + plain_text_gt: String epoch_date_submitted: Int - date_modified_gte: DateTime - image_url_lte: String - epoch_date_submitted_exists: Boolean description: String - epoch_date_downloaded_gte: Int + flag_ne: Boolean + title_lt: String + title_ne: String + date_submitted_ne: DateTime + editor_notes_lt: String + date_submitted_lt: DateTime + tags_in: [String] + report_number_in: [Int] + flag_exists: Boolean + cloudinary_id_gte: String + AND: [ReportQueryInput!] + epoch_date_submitted_gte: Int + date_modified_lte: DateTime epoch_date_downloaded_lte: Int - image_url_exists: Boolean + epoch_date_published_nin: [Int] + image_url_nin: [String] + date_modified_gt: DateTime + url_ne: String + report_number_lte: Int + image_url_lt: String + user: UserQueryInput + authors_exists: Boolean + date_modified_lt: DateTime + date_modified: DateTime + plain_text: String + tags_nin: [String] + editor_notes_exists: Boolean + epoch_date_downloaded_exists: Boolean + _id_lte: ObjectId + date_published_gt: DateTime + epoch_date_submitted_gt: Int + epoch_date_submitted_in: [Int] + date_downloaded_in: [DateTime] + date_submitted_exists: Boolean + cloudinary_id_in: [String] + date_modified_nin: [DateTime] + _id_in: [ObjectId] + date_downloaded_lt: DateTime + plain_text_in: [String] text_gt: String + text_exists: Boolean + epoch_date_modified_in: [Int] + epoch_date_published_gt: Int + title: String + description_in: [String] + description_ne: String + date_modified_exists: Boolean + text_nin: [String] + epoch_date_downloaded_gt: Int image_url_in: [String] - epoch_date_modified_nin: [Int] - epoch_date_downloaded_exists: Boolean - date_downloaded: DateTime - epoch_date_modified_lte: Int - title_gte: String - date_downloaded_gte: DateTime + source_domain_lte: String + authors: [String] image_url_ne: String - tags_in: [String] - language_lt: String - title: String + cloudinary_id: String + image_url_gte: String + date_downloaded_exists: Boolean + url_lte: String + epoch_date_modified_lte: Int + epoch_date_modified_nin: [Int] + text_ne: String + epoch_date_published_in: [Int] + epoch_date_modified_lt: Int epoch_date_modified_ne: Int - editor_notes_exists: Boolean - report_number_gte: Int - editor_notes_lte: String - date_published_gt: DateTime + date_submitted_gte: DateTime + _id_exists: Boolean + language_gt: String epoch_date_published_ne: Int - epoch_date_submitted_lte: Int - epoch_date_downloaded: Int - language_exists: Boolean - title_nin: [String] - source_domain_gte: String - source_domain_lte: String - date_modified_in: [DateTime] - epoch_date_modified: Int - cloudinary_id_lte: String - _id_lte: ObjectId - cloudinary_id_nin: [String] - epoch_date_modified_gte: Int - url_gt: String description_gt: String + image_url: String + is_incident_report_exists: Boolean + report_number_ne: Int + flag: Boolean + title_in: [String] + plain_text_lte: String + tags: [String] + date_published_in: [DateTime] url_nin: [String] - report_number_lt: Int - text_gte: String - inputs_outputs_nin: [String] - report_number_gt: Int - user: UserQueryInput - source_domain_nin: [String] - OR: [ReportQueryInput!] + date_published_exists: Boolean + plain_text_gte: String + report_number: Int + editor_notes_ne: String + epoch_date_modified_exists: Boolean authors_in: [String] - epoch_date_submitted_ne: Int - editor_notes_in: [String] - editor_notes_lt: String - cloudinary_id_lt: String - report_number_lte: Int - epoch_date_published_gt: Int - tags: [String] - user_exists: Boolean - language_in: [String] + image_url_exists: Boolean + editor_notes_lte: String + language_ne: String + description_gte: String + OR: [ReportQueryInput!] + text_lte: String embedding_exists: Boolean - submitters_nin: [String] - title_lte: String - submitters: [String] - plain_text_gt: String - plain_text_lte: String - date_published_in: [DateTime] - cloudinary_id: String - tags_nin: [String] - editor_notes_gte: String - date_modified: DateTime - title_ne: String - date_submitted_gte: DateTime - date_submitted_gt: DateTime - date_downloaded_nin: [DateTime] - language_nin: [String] + date_downloaded: DateTime + text_gte: String _id_lt: ObjectId - report_number_ne: Int - date_submitted_ne: DateTime - image_url_lt: String - url_lt: String - date_submitted_exists: Boolean - plain_text_nin: [String] - _id_in: [ObjectId] - epoch_date_modified_lt: Int - report_number_exists: Boolean - flag_exists: Boolean - epoch_date_downloaded_nin: [Int] - inputs_outputs_exists: Boolean - image_url: String - description_in: [String] + editor_notes_in: [String] + date_downloaded_nin: [DateTime] + _id_nin: [ObjectId] source_domain_in: [String] - url_ne: String - _id_gt: ObjectId - epoch_date_modified_gt: Int - text_lt: String - description_gte: String - date_downloaded_ne: DateTime - plain_text: String - cloudinary_id_in: [String] - epoch_date_published_gte: Int + source_domain_nin: [String] + report_number_lt: Int + title_lte: String + cloudinary_id_gt: String + source_domain_ne: String + inputs_outputs_exists: Boolean + title_gte: String description_exists: Boolean - date_downloaded_exists: Boolean - plain_text_in: [String] - url_exists: Boolean - plain_text_ne: String - epoch_date_published_nin: [Int] - report_number_nin: [Int] + description_lt: String + epoch_date_downloaded_in: [Int] + text_lt: String + plain_text_nin: [String] + title_exists: Boolean epoch_date_downloaded_lt: Int - editor_notes: String - quiet: Boolean - cloudinary_id_exists: Boolean - epoch_date_downloaded_ne: Int - url: String - image_url_gte: String - plain_text_exists: Boolean - language_ne: String - epoch_date_submitted_in: [Int] - editor_notes_gt: String - is_incident_report_ne: Boolean - date_submitted_lte: DateTime - text_in: [String] - plain_text_lt: String - image_url_gt: String - date_submitted: DateTime - title_in: [String] - _id_ne: ObjectId - date_submitted_in: [DateTime] - submitters_exists: Boolean date_modified_ne: DateTime - epoch_date_submitted_lt: Int - date_downloaded_lte: DateTime - description_ne: String - submitters_in: [String] - text_lte: String - language_gte: String - date_downloaded_gt: DateTime - AND: [ReportQueryInput!] - _id_exists: Boolean - source_domain_gt: String - epoch_date_downloaded_in: [Int] - date_published_gte: DateTime - date_downloaded_in: [DateTime] - description_lte: String - title_gt: String - title_lt: String - epoch_date_modified_exists: Boolean - epoch_date_submitted_gt: Int - inputs_outputs: [String] - cloudinary_id_gte: String - epoch_date_submitted_gte: Int - image_url_nin: [String] - date_published_lte: DateTime - text_nin: [String] - url_gte: String authors_nin: [String] - is_incident_report: Boolean - is_incident_report_exists: Boolean - date_modified_lte: DateTime - description_nin: [String] - date_modified_nin: [DateTime] - source_domain_lt: String + inputs_outputs_in: [String] cloudinary_id_ne: String - date_modified_exists: Boolean - url_in: [String] - source_domain_ne: String + _id_gte: ObjectId editor_notes_nin: [String] - source_domain: String - description_lt: String - date_published_exists: Boolean - quiet_exists: Boolean - language: String - quiet_ne: Boolean - epoch_date_modified_in: [Int] - plain_text_gte: String - text_ne: String - epoch_date_published_lte: Int - date_published_nin: [DateTime] - date_published_lt: DateTime - authors: [String] -} - -input CreateVariantInputVariant { - date_published: String - inputs_outputs: [String] - submitters: [String] - text: String + report_number_gt: Int + is_incident_report_ne: Boolean + epoch_date_modified_gt: Int + epoch_date_downloaded_ne: Int } -input History_reportEmbeddingUpdateInput { - from_text_hash: String - from_text_hash_unset: Boolean +type IncidentEmbedding { + from_reports: [Int] vector: [Float] - vector_unset: Boolean -} - -input TaxaUpdateInput { - description: String - description_unset: Boolean - field_list: [TaxaField_listUpdateInput] - weight: Int - weight_inc: Int - _id_unset: Boolean - field_list_unset: Boolean - _id: ObjectId - dummy_fields_unset: Boolean - complete_entities_unset: Boolean - weight_unset: Boolean - namespace: String - complete_entities: Boolean - namespace_unset: Boolean - dummy_fields: [TaxaDummy_fieldUpdateInput] -} - -type Report { - _id: ObjectId - authors: [String]! - cloudinary_id: String! - date_downloaded: DateTime! - date_modified: DateTime! - date_published: DateTime! - date_submitted: DateTime! - description: String - editor_notes: String - embedding: ReportEmbedding - epoch_date_downloaded: Int! - epoch_date_modified: Int! - epoch_date_published: Int! - epoch_date_submitted: Int! - flag: Boolean - image_url: String! - inputs_outputs: [String] - is_incident_report: Boolean - language: String! - plain_text: String! - quiet: Boolean - report_number: Int! - source_domain: String! - submitters: [String]! - tags: [String]! - text: String! - title: String! - translations(input: String): ReportTranslation - url: String! - user: User -} - -input TaxaField_listItem_fieldQueryInput { - short_name_gt: String - long_name_gt: String - weight_exists: Boolean - weight_lte: Int - field_number_gte: String - field_number_exists: Boolean - long_name_exists: Boolean - required_exists: Boolean - short_name_exists: Boolean - weight: Int - field_number_gt: String - mongo_type_ne: String - default_gte: String - long_description_lte: String - short_name_gte: String - mongo_type_lt: String - field_number_in: [String] - long_name_in: [String] - long_description_gte: String - long_name_lt: String - short_description_lt: String - required_ne: Boolean - public_ne: Boolean - display_type_exists: Boolean - default_exists: Boolean - long_description_exists: Boolean - placeholder_gte: String - weight_gte: Int - short_name_ne: String - public: Boolean - long_name_lte: String - complete_from_exists: Boolean - long_description_ne: String - weight_in: [Int] - short_name_nin: [String] - display_type_ne: String - complete_from: TaxaField_listItem_fieldComplete_fromQueryInput - default_in: [String] - long_description: String - long_name: String - display_type_lt: String - permitted_values_in: [String] - mongo_type_lte: String - long_name_ne: String - OR: [TaxaField_listItem_fieldQueryInput!] - short_description_ne: String - display_type_lte: String - short_description: String - instant_facet_ne: Boolean - field_number: String - field_number_ne: String - default_lte: String - display_type_nin: [String] - public_exists: Boolean - weight_nin: [Int] - field_number_lte: String - short_name: String - instant_facet: Boolean - short_name_in: [String] - display_type: String - default_nin: [String] - placeholder_lte: String - placeholder_gt: String - default: String - mongo_type: String - long_name_gte: String - short_description_gte: String - short_description_lte: String - placeholder_exists: Boolean - long_description_in: [String] - default_lt: String - weight_lt: Int - permitted_values_exists: Boolean - mongo_type_in: [String] - placeholder_lt: String - mongo_type_nin: [String] - long_description_lt: String - field_number_lt: String - permitted_values_nin: [String] - default_ne: String - short_description_gt: String - short_description_nin: [String] - display_type_gte: String - weight_ne: Int - short_name_lte: String - AND: [TaxaField_listItem_fieldQueryInput!] - mongo_type_exists: Boolean - field_number_nin: [String] - permitted_values: [String] - placeholder_in: [String] - weight_gt: Int - short_description_exists: Boolean - short_name_lt: String - placeholder: String - long_description_gt: String - short_description_in: [String] - long_name_nin: [String] - mongo_type_gt: String - display_type_gt: String - placeholder_ne: String - long_description_nin: [String] - required: Boolean - instant_facet_exists: Boolean - default_gt: String - mongo_type_gte: String - display_type_in: [String] - placeholder_nin: [String] } -input History_incidentTsneInsertInput { - x: Float - y: Float +type ChecklistRiskPrecedent { + description: String + incident_id: Int + tags: [String] + title: String } -input GetUserInput { - userId: ObjectId +input IncidentReportsRelationInput { + create: [ReportInsertInput] + link: [Int] } -type AppUser { - email: String +input IncidentEmbeddingInsertInput { + vector: [Float] + from_reports: [Int] } -input ChecklistRiskInsertInput { - generated: Boolean - id: String - likelihood: String - risk_status: String - touched: Boolean - risk_notes: String - severity: String - tags: [String] +input CandidateInsertInput { + embedding: CandidateEmbeddingInsertInput + plain_text: String + language: String + classification_similarity: [CandidateClassification_similarityInsertInput] + epoch_date_published: Int + match: Boolean! + authors: [String] + date_published: String + image_url: String + text: String + matching_harm_keywords: [String] + url: String! title: String - precedents: [ChecklistRiskPrecedentInsertInput] + dismissed: Boolean + _id: ObjectId + source_domain: String + similarity: Float + date_downloaded: String + matching_keywords: [String] + epoch_date_downloaded: Int + matching_entities: [String] } -input History_incidentTsneUpdateInput { - x_inc: Float - x_unset: Boolean - y: Float - y_inc: Float - y_unset: Boolean - x: Float +input TaxaUpdateInput { + weight_inc: Int + _id: ObjectId + complete_entities: Boolean + weight_unset: Boolean + _id_unset: Boolean + description: String + weight: Int + dummy_fields_unset: Boolean + field_list: [TaxaField_listUpdateInput] + namespace: String + description_unset: Boolean + dummy_fields: [TaxaDummy_fieldUpdateInput] + namespace_unset: Boolean + complete_entities_unset: Boolean + field_list_unset: Boolean } -type SubmissionNlp_similar_incident { +input ReportEmbeddingUpdateInput { + from_text_hash: String + from_text_hash_unset: Boolean + vector: [Float] + vector_unset: Boolean +} + +input UserQueryInput { + last_name_exists: Boolean + userId_nin: [String] + _id_in: [ObjectId] + first_name_in: [String] + roles_in: [String] + AND: [UserQueryInput!] + last_name_gte: String + last_name_nin: [String] + first_name_lt: String + _id_gte: ObjectId + OR: [UserQueryInput!] + last_name_ne: String + _id_nin: [ObjectId] + first_name_nin: [String] + userId_exists: Boolean + _id_gt: ObjectId + last_name_lt: String + userId_lte: String + userId_ne: String + last_name: String + last_name_lte: String + roles: [String] + _id_lt: ObjectId + first_name: String + last_name_gt: String + first_name_exists: Boolean + first_name_gt: String + userId_gte: String + roles_exists: Boolean + userId_in: [String] + userId: String + userId_gt: String + last_name_in: [String] + _id_exists: Boolean + first_name_gte: String + first_name_lte: String + first_name_ne: String + userId_lt: String + _id_ne: ObjectId + _id_lte: ObjectId + roles_nin: [String] + _id: ObjectId +} + +enum History_incidentSortByInput { + _ID_ASC + _ID_DESC + EDITOR_NOTES_ASC + EDITOR_NOTES_DESC + EPOCH_DATE_MODIFIED_DESC + MODIFIEDBY_DESC + MODIFIEDBY_ASC + DATE_DESC + DESCRIPTION_DESC + INCIDENT_ID_ASC + INCIDENT_ID_DESC + DATE_ASC + DESCRIPTION_ASC + EPOCH_DATE_MODIFIED_ASC + TITLE_ASC + TITLE_DESC +} + +type RisksPayloadPrecedent { + AllegedDeployerOfAISystem: [String] + AllegedDeveloperOfAISystem: [String] + AllegedHarmedOrNearlyHarmedParties: [String] + _id: ObjectId + date: String + description: String + editor_dissimilar_incidents: [Int] + editor_notes: String + editor_similar_incidents: [Int] + editors: [String] + embedding: RisksPayloadPrecedentEmbedding + epoch_date_modified: Int + flagged_dissimilar_incidents: [Int] + implicated_systems: [String] incident_id: Int - similarity: Float + nlp_similar_incidents: [RisksPayloadPrecedentNlp_similar_incident] + reports: [Int] + tags: [String] + title: String + tsne: RisksPayloadPrecedentTsne } -type Checklist { +input IncidentEmbeddingUpdateInput { + from_reports: [Int] + from_reports_unset: Boolean + vector: [Float] + vector_unset: Boolean +} + +input ClassificationInsertInput { + incidents: ClassificationIncidentsRelationInput! + namespace: String! + notes: String + publish: Boolean + reports: ClassificationReportsRelationInput! _id: ObjectId - about: String - date_created: DateTime - date_updated: DateTime - entity_id: String - id: String - name: String - owner_id: String - risks: [ChecklistRisk] - tags_goals: [String] - tags_methods: [String] - tags_other: [String] + attributes: [ClassificationAttributeInsertInput] +} + +type History_reportEmbedding { + from_text_hash: String + vector: [Float] +} + +input TaxaDummy_fieldQueryInput { + field_number_nin: [String] + short_name: String + OR: [TaxaDummy_fieldQueryInput!] + short_name_in: [String] + short_name_ne: String + AND: [TaxaDummy_fieldQueryInput!] + short_name_gte: String + short_name_exists: Boolean + field_number_lte: String + field_number_exists: Boolean + field_number_ne: String + field_number_in: [String] + short_name_nin: [String] + short_name_lt: String + field_number_lt: String + short_name_gt: String + field_number_gte: String + short_name_lte: String + field_number: String + field_number_gt: String +} + +enum SubmissionSortByInput { + SOURCE_DOMAIN_ASC + STATUS_DESC + CLOUDINARY_ID_DESC + DATE_DOWNLOADED_ASC + IMAGE_URL_DESC + INCIDENT_TITLE_ASC + _ID_DESC + DATE_SUBMITTED_DESC + SOURCE_DOMAIN_DESC + TEXT_ASC + TEXT_DESC + DATE_PUBLISHED_ASC + DESCRIPTION_DESC + EPOCH_DATE_MODIFIED_DESC + LANGUAGE_ASC + PLAIN_TEXT_ASC + USER_ASC + USER_DESC + DATE_DOWNLOADED_DESC + EDITOR_NOTES_DESC + INCIDENT_DATE_ASC + INCIDENT_DATE_DESC + INCIDENT_TITLE_DESC + PLAIN_TEXT_DESC + DATE_MODIFIED_ASC + DATE_SUBMITTED_ASC + EDITOR_NOTES_ASC + IMAGE_URL_ASC + DATE_PUBLISHED_DESC + TITLE_ASC + _ID_ASC + DESCRIPTION_ASC + EPOCH_DATE_MODIFIED_ASC + LANGUAGE_DESC + URL_ASC + URL_DESC + CLOUDINARY_ID_ASC + DATE_MODIFIED_DESC + STATUS_ASC + TITLE_DESC +} + +type RisksPayloadPrecedentNlp_similar_incident { + incident_id: Int + similarity: Float } input EntityInsertInput { - created_at: DateTime date_modified: DateTime entity_id: String! name: String! _id: ObjectId + created_at: DateTime } -input CandidateClassification_similarityInsertInput { - classification: String - similarity: Float -} - -input TaxaInsertInput { - weight: Int - _id: ObjectId - complete_entities: Boolean - description: String - dummy_fields: [TaxaDummy_fieldInsertInput] - field_list: [TaxaField_listInsertInput] - namespace: String +input IncidentEditorsRelationInput { + link: [String] + create: [UserInsertInput] } -scalar ObjectId - -type History_report { +type Report { _id: ObjectId authors: [String]! cloudinary_id: String! @@ -2589,7 +2474,7 @@ type History_report { date_submitted: DateTime! description: String editor_notes: String - embedding: History_reportEmbedding + embedding: ReportEmbedding epoch_date_downloaded: Int! epoch_date_modified: Int! epoch_date_published: Int! @@ -2599,7 +2484,6 @@ type History_report { inputs_outputs: [String] is_incident_report: Boolean language: String! - modifiedBy: String plain_text: String! quiet: Boolean report_number: Int! @@ -2609,56 +2493,209 @@ type History_report { text: String! title: String! url: String! - user: String + user: User } -input DuplicateInsertInput { +type ReportEmbedding { + from_text_hash: String + vector: [Float] +} + +type History_incident { + AllegedDeployerOfAISystem: [String] + AllegedDeveloperOfAISystem: [String] + AllegedHarmedOrNearlyHarmedParties: [String] _id: ObjectId - duplicate_incident_number: Int - true_incident_number: Int + date: String! + description: String + editor_dissimilar_incidents: [Int] + editor_notes: String + editor_similar_incidents: [Int] + editors: [String]! + embedding: History_incidentEmbedding + epoch_date_modified: Int + flagged_dissimilar_incidents: [Int] + implicated_systems: [String] + incident_id: Int! + modifiedBy: String + nlp_similar_incidents: [History_incidentNlp_similar_incident] + reports: [Int]! + title: String! + tsne: History_incidentTsne } -input CandidateInsertInput { - dismissed: Boolean - similarity: Float - matching_harm_keywords: [String] - url: String! +type History_incidentEmbedding { + from_reports: [Int] + vector: [Float] +} + +enum EntitySortByInput { + _ID_ASC + ENTITY_ID_ASC + NAME_ASC + NAME_DESC + _ID_DESC + CREATED_AT_ASC + CREATED_AT_DESC + DATE_MODIFIED_ASC + DATE_MODIFIED_DESC + ENTITY_ID_DESC +} + +input ChecklistQueryInput { + OR: [ChecklistQueryInput!] + about_exists: Boolean + name_gt: String + date_updated_ne: DateTime + about_ne: String + name_exists: Boolean + date_updated_nin: [DateTime] + date_created_gt: DateTime + tags_goals_in: [String] + date_updated_gte: DateTime + about_lt: String + entity_id_gte: String + _id_exists: Boolean _id: ObjectId - date_downloaded: String - match: Boolean! - title: String - source_domain: String - language: String - classification_similarity: [CandidateClassification_similarityInsertInput] - authors: [String] - embedding: CandidateEmbeddingInsertInput - date_published: String - text: String - image_url: String - epoch_date_downloaded: Int - plain_text: String - matching_entities: [String] - matching_keywords: [String] - epoch_date_published: Int + about_nin: [String] + date_created_lt: DateTime + tags_methods: [String] + tags_methods_in: [String] + id: String + id_in: [String] + owner_id_gte: String + tags_methods_nin: [String] + about_gt: String + tags_goals_nin: [String] + name_nin: [String] + id_gt: String + tags_goals_exists: Boolean + date_updated_exists: Boolean + risks_nin: [ChecklistRiskQueryInput] + owner_id_lte: String + date_created_in: [DateTime] + name_lt: String + date_created_exists: Boolean + tags_other_exists: Boolean + risks_exists: Boolean + id_lte: String + date_updated_in: [DateTime] + id_ne: String + owner_id_nin: [String] + date_created_lte: DateTime + name_ne: String + about: String + entity_id_lte: String + owner_id_ne: String + date_updated_lt: DateTime + tags_other: [String] + id_lt: String + AND: [ChecklistQueryInput!] + entity_id: String + name_gte: String + date_updated_lte: DateTime + _id_gte: ObjectId + date_updated: DateTime + name_lte: String + owner_id_in: [String] + tags_methods_exists: Boolean + about_in: [String] + owner_id_exists: Boolean + _id_gt: ObjectId + entity_id_ne: String + date_updated_gt: DateTime + _id_nin: [ObjectId] + _id_ne: ObjectId + date_created: DateTime + _id_in: [ObjectId] + owner_id: String + owner_id_gt: String + about_gte: String + tags_goals: [String] + name_in: [String] + date_created_nin: [DateTime] + entity_id_in: [String] + entity_id_nin: [String] + about_lte: String + name: String + id_nin: [String] + entity_id_lt: String + owner_id_lt: String + date_created_gte: DateTime + id_gte: String + tags_other_nin: [String] + _id_lte: ObjectId + tags_other_in: [String] + risks: [ChecklistRiskQueryInput] + entity_id_gt: String + date_created_ne: DateTime + _id_lt: ObjectId + risks_in: [ChecklistRiskQueryInput] + entity_id_exists: Boolean + id_exists: Boolean } -enum History_incidentSortByInput { - EDITOR_NOTES_DESC - TITLE_DESC - DESCRIPTION_ASC - EDITOR_NOTES_ASC - INCIDENT_ID_DESC - MODIFIEDBY_DESC - INCIDENT_ID_ASC +enum CandidateSortByInput { + IMAGE_URL_DESC + SOURCE_DOMAIN_ASC + DATE_PUBLISHED_ASC + DATE_PUBLISHED_DESC + EPOCH_DATE_PUBLISHED_DESC + LANGUAGE_DESC + PLAIN_TEXT_ASC + PLAIN_TEXT_DESC + TEXT_ASC + TEXT_DESC + DATE_DOWNLOADED_ASC + EPOCH_DATE_DOWNLOADED_ASC + EPOCH_DATE_DOWNLOADED_DESC TITLE_ASC - DATE_ASC - DESCRIPTION_DESC - DATE_DESC - EPOCH_DATE_MODIFIED_ASC - EPOCH_DATE_MODIFIED_DESC - MODIFIEDBY_ASC + URL_ASC + SOURCE_DOMAIN_DESC + TITLE_DESC _ID_ASC + IMAGE_URL_ASC + LANGUAGE_ASC + SIMILARITY_ASC + SIMILARITY_DESC + URL_DESC _ID_DESC + DATE_DOWNLOADED_DESC + EPOCH_DATE_PUBLISHED_ASC +} + +type Quickadd { + _id: ObjectId + date_submitted: String! + incident_id: Long + source_domain: String + url: String! +} + +type Duplicate { + _id: ObjectId + duplicate_incident_number: Int + true_incident_number: Int +} + +input ChecklistInsertInput { + id: String + name: String + _id: ObjectId + entity_id: String + tags_other: [String] + date_created: DateTime + about: String + owner_id: String + tags_goals: [String] + tags_methods: [String] + risks: [ChecklistRiskInsertInput] + date_updated: DateTime +} + +input CandidateClassification_similarityInsertInput { + similarity: Float + classification: String } input CreateDefaultAdminUserInput { @@ -2666,1353 +2703,1402 @@ input CreateDefaultAdminUserInput { password: String } -input QuickaddQueryInput { - date_submitted_lt: String - date_submitted_ne: String - OR: [QuickaddQueryInput!] - _id_gt: ObjectId - source_domain_gte: String - date_submitted_gte: String - date_submitted_lte: String - source_domain_lt: String - incident_id_nin: [Long] - date_submitted_gt: String - source_domain_in: [String] - url: String - url_lte: String - incident_id_gt: Long - _id_lt: ObjectId - url_in: [String] - incident_id_lte: Long - source_domain_exists: Boolean - date_submitted_in: [String] - url_gte: String - date_submitted_nin: [String] - source_domain_ne: String - source_domain_gt: String - url_exists: Boolean - url_nin: [String] - _id_nin: [ObjectId] - _id_in: [ObjectId] - date_submitted_exists: Boolean - incident_id_in: [Long] - source_domain_nin: [String] - AND: [QuickaddQueryInput!] - _id_gte: ObjectId - _id: ObjectId - incident_id_exists: Boolean - date_submitted: String - url_ne: String - incident_id: Long - incident_id_ne: Long - incident_id_lt: Long - source_domain_lte: String - _id_ne: ObjectId - url_lt: String - incident_id_gte: Long - url_gt: String - _id_exists: Boolean - source_domain: String - _id_lte: ObjectId +type AppUser { + email: String } -type DeleteManyPayload { - deletedCount: Int! +type UpdateManyPayload { + matchedCount: Int! + modifiedCount: Int! } -input DuplicateUpdateInput { - true_incident_number_inc: Int - true_incident_number_unset: Boolean - _id: ObjectId - _id_unset: Boolean - duplicate_incident_number: Int - duplicate_incident_number_inc: Int - duplicate_incident_number_unset: Boolean - true_incident_number: Int +input NotificationUserIdRelationInput { + create: UserInsertInput + link: String } -input IncidentNlp_similar_incidentQueryInput { - incident_id_in: [Int] - similarity_lt: Float - incident_id_lt: Int - similarity_in: [Float] - similarity_gt: Float - similarity_nin: [Float] - AND: [IncidentNlp_similar_incidentQueryInput!] - OR: [IncidentNlp_similar_incidentQueryInput!] +input History_reportEmbeddingUpdateInput { + from_text_hash_unset: Boolean + vector: [Float] + vector_unset: Boolean + from_text_hash: String +} + +input IncidentQueryInput { + description_lt: String + description_lte: String + epoch_date_modified_lte: Int incident_id: Int - similarity_lte: Float - similarity_exists: Boolean - incident_id_gt: Int - similarity: Float + editor_notes_in: [String] + tsne_exists: Boolean + editor_similar_incidents_nin: [Int] + date_exists: Boolean + description_ne: String + editors_exists: Boolean + editor_notes_lte: String + date_in: [String] + _id_gte: ObjectId + editor_dissimilar_incidents_exists: Boolean + flagged_dissimilar_incidents: [Int] + editor_similar_incidents_exists: Boolean + incident_id_nin: [Int] + editor_notes_gt: String + embedding: IncidentEmbeddingQueryInput + epoch_date_modified_nin: [Int] + title_in: [String] + reports_exists: Boolean + title_gt: String + tsne: IncidentTsneQueryInput + AllegedDeveloperOfAISystem_in: [EntityQueryInput] + nlp_similar_incidents_in: [IncidentNlp_similar_incidentQueryInput] + editor_dissimilar_incidents_in: [Int] + title_nin: [String] + epoch_date_modified: Int + editor_similar_incidents: [Int] + AllegedDeployerOfAISystem_nin: [EntityQueryInput] + reports: [ReportQueryInput] + embedding_exists: Boolean + editor_dissimilar_incidents: [Int] + incident_id_in: [Int] incident_id_exists: Boolean + nlp_similar_incidents: [IncidentNlp_similar_incidentQueryInput] + AllegedDeployerOfAISystem_in: [EntityQueryInput] + _id: ObjectId + date: String incident_id_ne: Int - incident_id_nin: [Int] - similarity_gte: Float - similarity_ne: Float + AllegedHarmedOrNearlyHarmedParties: [EntityQueryInput] + title_lte: String + incident_id_lt: Int + nlp_similar_incidents_nin: [IncidentNlp_similar_incidentQueryInput] + AllegedDeveloperOfAISystem: [EntityQueryInput] + epoch_date_modified_in: [Int] + editors_in: [UserQueryInput] + description: String + AND: [IncidentQueryInput!] + editors: [UserQueryInput] + description_gte: String + date_gte: String + editor_notes: String + _id_lte: ObjectId + AllegedDeployerOfAISystem: [EntityQueryInput] + AllegedDeployerOfAISystem_exists: Boolean incident_id_lte: Int + date_lte: String + editors_nin: [UserQueryInput] + editor_notes_nin: [String] + epoch_date_modified_lt: Int + date_nin: [String] + flagged_dissimilar_incidents_nin: [Int] + nlp_similar_incidents_exists: Boolean + _id_lt: ObjectId + editor_dissimilar_incidents_nin: [Int] + reports_nin: [ReportQueryInput] + AllegedHarmedOrNearlyHarmedParties_nin: [EntityQueryInput] + title_lt: String + editor_notes_gte: String + _id_nin: [ObjectId] + AllegedHarmedOrNearlyHarmedParties_in: [EntityQueryInput] + epoch_date_modified_gte: Int + AllegedDeveloperOfAISystem_nin: [EntityQueryInput] + description_in: [String] + date_gt: String + title: String + editor_similar_incidents_in: [Int] + _id_gt: ObjectId incident_id_gte: Int + date_ne: String + AllegedDeveloperOfAISystem_exists: Boolean + flagged_dissimilar_incidents_exists: Boolean + description_exists: Boolean + title_gte: String + epoch_date_modified_gt: Int + date_lt: String + title_exists: Boolean + editor_notes_exists: Boolean + OR: [IncidentQueryInput!] + epoch_date_modified_exists: Boolean + reports_in: [ReportQueryInput] + epoch_date_modified_ne: Int + title_ne: String + description_nin: [String] + flagged_dissimilar_incidents_in: [Int] + description_gt: String + AllegedHarmedOrNearlyHarmedParties_exists: Boolean + _id_in: [ObjectId] + incident_id_gt: Int + _id_ne: ObjectId + editor_notes_lt: String + editor_notes_ne: String + _id_exists: Boolean + implicated_systems: [EntityQueryInput] +} + +enum DuplicateSortByInput { + _ID_ASC + _ID_DESC + DUPLICATE_INCIDENT_NUMBER_ASC + DUPLICATE_INCIDENT_NUMBER_DESC + TRUE_INCIDENT_NUMBER_ASC + TRUE_INCIDENT_NUMBER_DESC +} + +enum ReportSortByInput { + DATE_DOWNLOADED_ASC + DESCRIPTION_DESC + REPORT_NUMBER_DESC + TITLE_DESC + IMAGE_URL_ASC + IMAGE_URL_DESC + LANGUAGE_ASC + LANGUAGE_DESC + PLAIN_TEXT_DESC + REPORT_NUMBER_ASC + SOURCE_DOMAIN_ASC + URL_DESC + _ID_ASC + CLOUDINARY_ID_ASC + DATE_DOWNLOADED_DESC + DATE_PUBLISHED_ASC + DATE_PUBLISHED_DESC + DATE_SUBMITTED_DESC + TITLE_ASC + DATE_SUBMITTED_ASC + DESCRIPTION_ASC + EPOCH_DATE_DOWNLOADED_ASC + TEXT_ASC + PLAIN_TEXT_ASC + TEXT_DESC + USER_ASC + _ID_DESC + DATE_MODIFIED_DESC + EPOCH_DATE_MODIFIED_DESC + EPOCH_DATE_SUBMITTED_ASC + SOURCE_DOMAIN_DESC + URL_ASC + CLOUDINARY_ID_DESC + EDITOR_NOTES_ASC + EPOCH_DATE_MODIFIED_ASC + EPOCH_DATE_PUBLISHED_DESC + EPOCH_DATE_SUBMITTED_DESC + USER_DESC + DATE_MODIFIED_ASC + EDITOR_NOTES_DESC + EPOCH_DATE_DOWNLOADED_DESC + EPOCH_DATE_PUBLISHED_ASC +} + +type DeleteManyPayload { + deletedCount: Int! } -type IncidentNlp_similar_incident { - incident_id: Int - similarity: Float +input IncidentTsneUpdateInput { + y_inc: Float + y_unset: Boolean + x: Float + x_unset: Boolean + x_inc: Float + y: Float } -input IncidentAllegedHarmedOrNearlyHarmedPartiesRelationInput { +input SubmissionDevelopersRelationInput { create: [EntityInsertInput] link: [String] } -input SubscriptionInsertInput { - incident_id: SubscriptionIncident_idRelationInput - type: String! - userId: SubscriptionUserIdRelationInput! - _id: ObjectId - entityId: SubscriptionEntityIdRelationInput -} - -type History_incident { - AllegedDeployerOfAISystem: [String] - AllegedDeveloperOfAISystem: [String] - AllegedHarmedOrNearlyHarmedParties: [String] +type User { _id: ObjectId - date: String! - description: String - editor_dissimilar_incidents: [Int] - editor_notes: String - editor_similar_incidents: [Int] - editors: [String]! - embedding: History_incidentEmbedding - epoch_date_modified: Int - flagged_dissimilar_incidents: [Int] - incident_id: Int! - modifiedBy: String - nlp_similar_incidents: [History_incidentNlp_similar_incident] - reports: [Int]! - title: String! - tsne: History_incidentTsne + first_name: String + last_name: String + roles: [String]! + userId: String! } -input TaxaDummy_fieldQueryInput { - field_number: String - OR: [TaxaDummy_fieldQueryInput!] - field_number_gte: String - short_name_lte: String - field_number_ne: String - field_number_lte: String - short_name_gte: String - short_name_lt: String - field_number_gt: String - short_name_gt: String - field_number_nin: [String] - field_number_exists: Boolean - field_number_lt: String - short_name_exists: Boolean - AND: [TaxaDummy_fieldQueryInput!] - short_name_ne: String - short_name_nin: [String] - short_name: String - short_name_in: [String] - field_number_in: [String] +input History_reportEmbeddingQueryInput { + from_text_hash_nin: [String] + from_text_hash_in: [String] + from_text_hash_exists: Boolean + from_text_hash_gt: String + from_text_hash_gte: String + from_text_hash_ne: String + vector: [Float] + from_text_hash_lte: String + vector_nin: [Float] + vector_exists: Boolean + OR: [History_reportEmbeddingQueryInput!] + vector_in: [Float] + from_text_hash_lt: String + AND: [History_reportEmbeddingQueryInput!] + from_text_hash: String } -type Taxa { +type History_report { _id: ObjectId - complete_entities: Boolean + authors: [String]! + cloudinary_id: String! + date_downloaded: DateTime! + date_modified: DateTime! + date_published: DateTime! + date_submitted: DateTime! description: String - dummy_fields: [TaxaDummy_field] - field_list: [TaxaField_list] - namespace: String - weight: Int -} - -input History_reportUpdateInput { - description_unset: Boolean - tags: [String] - date_submitted: DateTime - report_number_inc: Int - url: String - title: String - text: String + editor_notes: String + embedding: History_reportEmbedding + epoch_date_downloaded: Int! + epoch_date_modified: Int! + epoch_date_published: Int! + epoch_date_submitted: Int! + flag: Boolean + image_url: String! + inputs_outputs: [String] is_incident_report: Boolean - date_downloaded: DateTime - embedding_unset: Boolean - epoch_date_downloaded_inc: Int - tags_unset: Boolean + language: String! modifiedBy: String - source_domain: String + plain_text: String! quiet: Boolean - language: String - epoch_date_downloaded_unset: Boolean - report_number_unset: Boolean - epoch_date_published: Int - plain_text: String - cloudinary_id: String - epoch_date_modified_unset: Boolean - is_incident_report_unset: Boolean - source_domain_unset: Boolean - submitters: [String] - epoch_date_modified: Int - flag: Boolean - date_published: DateTime - epoch_date_downloaded: Int + report_number: Int! + source_domain: String! + submitters: [String]! + tags: [String]! + text: String! + title: String! + url: String! user: String - date_published_unset: Boolean - _id: ObjectId - embedding: History_reportEmbeddingUpdateInput - date_downloaded_unset: Boolean - editor_notes_unset: Boolean - flag_unset: Boolean - authors: [String] - plain_text_unset: Boolean - date_submitted_unset: Boolean - epoch_date_published_inc: Int - epoch_date_submitted: Int - modifiedBy_unset: Boolean - _id_unset: Boolean - epoch_date_modified_inc: Int - quiet_unset: Boolean - submitters_unset: Boolean - image_url: String - text_unset: Boolean - authors_unset: Boolean - cloudinary_id_unset: Boolean - epoch_date_published_unset: Boolean - url_unset: Boolean - date_modified_unset: Boolean - epoch_date_submitted_unset: Boolean - inputs_outputs_unset: Boolean - description: String - user_unset: Boolean - language_unset: Boolean - title_unset: Boolean - inputs_outputs: [String] - epoch_date_submitted_inc: Int - editor_notes: String - report_number: Int - date_modified: DateTime - image_url_unset: Boolean } -type Classification { - _id: ObjectId - attributes: [ClassificationAttribute] - incidents: [Incident]! - namespace: String! - notes: String - publish: Boolean - reports: [Report]! +enum TaxaSortByInput { + _ID_ASC + _ID_DESC + DESCRIPTION_ASC + DESCRIPTION_DESC + NAMESPACE_ASC + NAMESPACE_DESC + WEIGHT_ASC + WEIGHT_DESC } -type InsertManyPayload { - insertedIds: [ObjectId]! +input IncidentTsneQueryInput { + y_ne: Float + y_lt: Float + y: Float + y_in: [Float] + x_ne: Float + x_lt: Float + x_in: [Float] + x: Float + y_lte: Float + y_gte: Float + AND: [IncidentTsneQueryInput!] + x_gt: Float + x_lte: Float + y_exists: Boolean + OR: [IncidentTsneQueryInput!] + x_gte: Float + x_exists: Boolean + x_nin: [Float] + y_gt: Float + y_nin: [Float] } -input ReportUpdateInput { - url_unset: Boolean - date_downloaded_unset: Boolean - epoch_date_downloaded_unset: Boolean - date_downloaded: DateTime - epoch_date_submitted: Int - _id: ObjectId - cloudinary_id_unset: Boolean - cloudinary_id: String - text_unset: Boolean - date_published_unset: Boolean - url: String - epoch_date_modified_unset: Boolean - epoch_date_modified_inc: Int - date_modified_unset: Boolean - image_url_unset: Boolean - authors_unset: Boolean - epoch_date_modified: Int - is_incident_report_unset: Boolean - tags: [String] +input CandidateQueryInput { + matching_entities_exists: Boolean + epoch_date_published_gt: Int + image_url_exists: Boolean + text_ne: String + _id_nin: [ObjectId] + language_lte: String + source_domain_in: [String] + image_url: String + text_lt: String + title_nin: [String] + image_url_lte: String + _id_lte: ObjectId + date_published_in: [String] + source_domain_lte: String + date_downloaded_gt: String + _id_gte: ObjectId + text_nin: [String] + title_lte: String + classification_similarity_exists: Boolean + dismissed_ne: Boolean + plain_text_lte: String + embedding: CandidateEmbeddingQueryInput + language_exists: Boolean + plain_text_lt: String + similarity_gte: Float + epoch_date_downloaded_ne: Int + title_lt: String + epoch_date_published_nin: [Int] + matching_keywords_nin: [String] + authors_exists: Boolean + title_ne: String + classification_similarity_nin: [CandidateClassification_similarityQueryInput] + dismissed_exists: Boolean + matching_harm_keywords_exists: Boolean + classification_similarity: [CandidateClassification_similarityQueryInput] + source_domain_exists: Boolean epoch_date_published: Int - title_unset: Boolean - _id_unset: Boolean - flag: Boolean - plain_text: String - date_modified: DateTime + matching_entities: [String] + epoch_date_downloaded_nin: [Int] + authors_in: [String] + source_domain_ne: String + source_domain_lt: String + similarity: Float + language_nin: [String] + match_exists: Boolean + language_ne: String + date_downloaded_lt: String + date_downloaded_ne: String + OR: [CandidateQueryInput!] epoch_date_downloaded: Int - epoch_date_submitted_unset: Boolean - is_incident_report: Boolean - date_published: DateTime - description_unset: Boolean - date_submitted: DateTime - epoch_date_published_unset: Boolean + similarity_lt: Float + url_in: [String] + date_downloaded_nin: [String] + _id_gt: ObjectId + image_url_ne: String + text_exists: Boolean + text_gt: String authors: [String] - editor_notes_unset: Boolean - inputs_outputs_unset: Boolean - embedding_unset: Boolean - user_unset: Boolean - report_number: Int - report_number_inc: Int - language: String - inputs_outputs: [String] + language_in: [String] + title_exists: Boolean + url_exists: Boolean + date_published_exists: Boolean + text_lte: String + title_gte: String + url: String text: String - user: ReportUserRelationInput - submitters_unset: Boolean - tags_unset: Boolean - embedding: ReportEmbeddingUpdateInput - source_domain: String - submitters: [String] - quiet_unset: Boolean - report_number_unset: Boolean - quiet: Boolean - date_submitted_unset: Boolean - plain_text_unset: Boolean - epoch_date_downloaded_inc: Int - image_url: String - language_unset: Boolean - epoch_date_submitted_inc: Int - description: String - source_domain_unset: Boolean - epoch_date_published_inc: Int - flag_unset: Boolean - editor_notes: String - title: String -} - -input UserUpdateInput { - roles_unset: Boolean - userId: String - _id_unset: Boolean - userId_unset: Boolean + similarity_gt: Float + plain_text_in: [String] + epoch_date_published_lt: Int + classification_similarity_in: [CandidateClassification_similarityQueryInput] + date_published_lte: String + language: String + date_downloaded_gte: String + date_downloaded_in: [String] + epoch_date_downloaded_lte: Int + date_published_nin: [String] + matching_harm_keywords: [String] + language_lt: String + url_ne: String + epoch_date_published_gte: Int + source_domain_nin: [String] + matching_harm_keywords_nin: [String] + date_published_gte: String + epoch_date_downloaded_lt: Int + _id_exists: Boolean + date_published: String + matching_keywords_in: [String] _id: ObjectId - first_name: String - first_name_unset: Boolean - last_name: String - last_name_unset: Boolean - roles: [String] -} - -type Query { - candidate(query: CandidateQueryInput): Candidate - candidates(sortBy: CandidateSortByInput, query: CandidateQueryInput, limit: Int = 100): [Candidate]! - checklist(query: ChecklistQueryInput): Checklist - checklists(limit: Int = 100, sortBy: ChecklistSortByInput, query: ChecklistQueryInput): [Checklist]! - classification(query: ClassificationQueryInput): Classification - classifications(sortBy: ClassificationSortByInput, query: ClassificationQueryInput, limit: Int = 100): [Classification]! - duplicate(query: DuplicateQueryInput): Duplicate - duplicates(query: DuplicateQueryInput, limit: Int = 100, sortBy: DuplicateSortByInput): [Duplicate]! - entities(query: EntityQueryInput, limit: Int = 100, sortBy: EntitySortByInput): [Entity]! - entity(query: EntityQueryInput): Entity - history_incident(query: History_incidentQueryInput): History_incident - history_incidents(query: History_incidentQueryInput, limit: Int = 100, sortBy: History_incidentSortByInput): [History_incident]! - history_report(query: History_reportQueryInput): History_report - history_reports(sortBy: History_reportSortByInput, query: History_reportQueryInput, limit: Int = 100): [History_report]! - incident(query: IncidentQueryInput): Incident - incidents(query: IncidentQueryInput, limit: Int = 100, sortBy: IncidentSortByInput): [Incident]! - notification(query: NotificationQueryInput): Notification - notifications(query: NotificationQueryInput, limit: Int = 100, sortBy: NotificationSortByInput): [Notification]! - quickadd(query: QuickaddQueryInput): Quickadd - quickadds(query: QuickaddQueryInput, limit: Int = 100, sortBy: QuickaddSortByInput): [Quickadd]! - report(query: ReportQueryInput): Report - reports(query: ReportQueryInput, limit: Int = 100, sortBy: ReportSortByInput): [Report]! - risks(input: RisksInput): [RisksPayloadItem] - submission(query: SubmissionQueryInput): Submission - submissions(limit: Int = 100, sortBy: SubmissionSortByInput, query: SubmissionQueryInput): [Submission]! - subscription(query: SubscriptionQueryInput): Subscription - subscriptions(sortBy: SubscriptionSortByInput, query: SubscriptionQueryInput, limit: Int = 100): [Subscription]! - taxa(query: TaxaQueryInput): Taxa - taxas(limit: Int = 100, sortBy: TaxaSortByInput, query: TaxaQueryInput): [Taxa]! - user(query: UserQueryInput): User - users(query: UserQueryInput, limit: Int = 100, sortBy: UserSortByInput): [User]! + url_lte: String + url_gt: String + epoch_date_downloaded_in: [Int] + language_gt: String + url_nin: [String] + matching_harm_keywords_in: [String] + plain_text_exists: Boolean + epoch_date_downloaded_exists: Boolean + embedding_exists: Boolean + plain_text: String + epoch_date_downloaded_gt: Int + similarity_nin: [Float] + image_url_gt: String + title_in: [String] + source_domain_gt: String + epoch_date_published_in: [Int] + matching_entities_nin: [String] + date_downloaded_lte: String + similarity_in: [Float] + language_gte: String + url_gte: String + match_ne: Boolean + matching_keywords_exists: Boolean + image_url_gte: String + text_gte: String + plain_text_gte: String + _id_lt: ObjectId + image_url_nin: [String] + title: String + date_downloaded_exists: Boolean + title_gt: String + authors_nin: [String] + source_domain_gte: String + date_downloaded: String + similarity_ne: Float + date_published_ne: String + match: Boolean + text_in: [String] + similarity_lte: Float + plain_text_nin: [String] + image_url_in: [String] + date_published_lt: String + dismissed: Boolean + matching_keywords: [String] + source_domain: String + epoch_date_downloaded_gte: Int + _id_in: [ObjectId] + epoch_date_published_ne: Int + matching_entities_in: [String] + epoch_date_published_lte: Int + image_url_lt: String + _id_ne: ObjectId + plain_text_ne: String + AND: [CandidateQueryInput!] + url_lt: String + epoch_date_published_exists: Boolean + plain_text_gt: String + similarity_exists: Boolean + date_published_gt: String } -input ReportEmbeddingQueryInput { - vector_in: [Float] - AND: [ReportEmbeddingQueryInput!] - from_text_hash_lte: String - from_text_hash_lt: String - from_text_hash_gte: String - vector: [Float] - vector_exists: Boolean - from_text_hash: String - from_text_hash_nin: [String] - from_text_hash_gt: String - from_text_hash_exists: Boolean - from_text_hash_in: [String] - vector_nin: [Float] - OR: [ReportEmbeddingQueryInput!] - from_text_hash_ne: String +enum SubscriptionSortByInput { + _ID_DESC + ENTITYID_ASC + INCIDENT_ID_DESC + USERID_ASC + USERID_DESC + _ID_ASC + ENTITYID_DESC + INCIDENT_ID_ASC + TYPE_ASC + TYPE_DESC } -input SubmissionNlp_similar_incidentUpdateInput { +input IncidentNlp_similar_incidentUpdateInput { incident_id: Int - incident_id_unset: Boolean incident_id_inc: Int + incident_id_unset: Boolean similarity: Float - similarity_inc: Float similarity_unset: Boolean + similarity_inc: Float +} + +input TaxaField_listItem_fieldInsertInput { + long_description: String + mongo_type: String + short_name: String + display_type: String + instant_facet: Boolean + required: Boolean + field_number: String + complete_from: TaxaField_listItem_fieldComplete_fromInsertInput + placeholder: String + long_name: String + public: Boolean + permitted_values: [String] + short_description: String + default: String + weight: Int } -input SubmissionEmbeddingInsertInput { - from_text_hash: String - vector: [Float] +input TaxaField_listUpdateInput { + hide_search_unset: Boolean + placeholder_unset: Boolean + placeholder: String + long_description_unset: Boolean + item_fields: TaxaField_listItem_fieldUpdateInput + short_name: String + hide_search: Boolean + long_name: String + mongo_type: String + public_unset: Boolean + short_description: String + instant_facet: Boolean + long_description: String + weight_inc: Int + complete_from: TaxaField_listComplete_fromUpdateInput + field_number: String + field_number_unset: Boolean + required: Boolean + display_type: String + complete_from_unset: Boolean + long_name_unset: Boolean + mongo_type_unset: Boolean + weight_unset: Boolean + short_name_unset: Boolean + required_unset: Boolean + default: String + default_unset: Boolean + instant_facet_unset: Boolean + weight: Int + display_type_unset: Boolean + permitted_values_unset: Boolean + short_description_unset: Boolean + item_fields_unset: Boolean + permitted_values: [String] + public: Boolean } -type UserAdminDatum { - creationDate: DateTime - disabled: Boolean - email: String - lastAuthenticationDate: DateTime +input ClassificationAttributeUpdateInput { + short_name: String + short_name_unset: Boolean + value_json: String + value_json_unset: Boolean } -enum ClassificationSortByInput { - NOTES_DESC - _ID_ASC - _ID_DESC - NAMESPACE_ASC - NAMESPACE_DESC - NOTES_ASC +scalar ObjectId + +input TaxaField_listItem_fieldComplete_fromQueryInput { + entities_exists: Boolean + current_nin: [String] + entities: Boolean + current: [String] + current_in: [String] + entities_ne: Boolean + OR: [TaxaField_listItem_fieldComplete_fromQueryInput!] + AND: [TaxaField_listItem_fieldComplete_fromQueryInput!] + all: [String] + all_nin: [String] + all_exists: Boolean + all_in: [String] + current_exists: Boolean } -type PromoteSubmissionToReportPayload { - incident_ids: [Int] - report_number: Int +input ClassificationReportsRelationInput { + link: [Int] + create: [ReportInsertInput] } -input ClassificationAttributeInsertInput { +input TaxaDummy_fieldUpdateInput { + short_name_unset: Boolean + field_number: String + field_number_unset: Boolean short_name: String - value_json: String } -input History_incidentUpdateInput { +input SubmissionIncident_editorsRelationInput { + create: [UserInsertInput] + link: [String] +} + +type Submission { + _id: ObjectId + authors: [String]! + cloudinary_id: String + date_downloaded: String! + date_modified: String! + date_published: String! + date_submitted: String! + deployers: [Entity] + description: String + developers: [Entity] + editor_dissimilar_incidents: [Int] editor_notes: String - modifiedBy: String - title: String - embedding_unset: Boolean editor_similar_incidents: [Int] - title_unset: Boolean - tsne: History_incidentTsneUpdateInput - editor_dissimilar_incidents: [Int] - _id: ObjectId - _id_unset: Boolean - AllegedDeployerOfAISystem_unset: Boolean - reports_unset: Boolean - epoch_date_modified_unset: Boolean - modifiedBy_unset: Boolean - editor_dissimilar_incidents_unset: Boolean - editors: [String] - AllegedDeployerOfAISystem: [String] - AllegedDeveloperOfAISystem: [String] - AllegedHarmedOrNearlyHarmedParties: [String] - editors_unset: Boolean - AllegedHarmedOrNearlyHarmedParties_unset: Boolean - editor_similar_incidents_unset: Boolean - date_unset: Boolean - date: String - epoch_date_modified_inc: Int - description_unset: Boolean - incident_id_unset: Boolean - nlp_similar_incidents: [History_incidentNlp_similar_incidentUpdateInput] - flagged_dissimilar_incidents: [Int] - incident_id: Int - reports: [Int] - editor_notes_unset: Boolean - tsne_unset: Boolean - flagged_dissimilar_incidents_unset: Boolean - embedding: History_incidentEmbeddingUpdateInput - incident_id_inc: Int + embedding: SubmissionEmbedding epoch_date_modified: Int - nlp_similar_incidents_unset: Boolean - AllegedDeveloperOfAISystem_unset: Boolean - description: String + harmed_parties: [Entity] + image_url: String! + incident_date: String + incident_editors: [User] + incident_ids: [Int] + incident_title: String + language: String! + nlp_similar_incidents: [SubmissionNlp_similar_incident] + plain_text: String + quiet: Boolean + source_domain: String! + status: String + submitters: [String]! + tags: [String]! + text: String! + title: String! + url: String! + user: User } -scalar DateTime - -type User { +type Entity { _id: ObjectId - adminData: UserAdminDatum - first_name: String - last_name: String - roles: [String]! - userId: String! -} - -type CandidateEmbedding { - from_text_hash: String - vector: [Float] -} - -type SubmissionEmbedding { - from_text_hash: String - vector: [Float] -} - -type History_reportEmbedding { - from_text_hash: String - vector: [Float] -} - -input SubmissionEmbeddingUpdateInput { - from_text_hash_unset: Boolean - vector: [Float] - vector_unset: Boolean - from_text_hash: String + created_at: DateTime + date_modified: DateTime + entity_id: String! + name: String! } -type TaxaField_list { - complete_from: TaxaField_listComplete_from - default: String - display_type: String - field_number: String - hide_search: Boolean - instant_facet: Boolean - item_fields: TaxaField_listItem_field - long_description: String - long_name: String - mongo_type: String - permitted_values: [String] - placeholder: String - public: Boolean - required: Boolean - short_description: String - short_name: String +type Taxa { + _id: ObjectId + complete_entities: Boolean + description: String + dummy_fields: [TaxaDummy_field] + field_list: [TaxaField_list] + namespace: String weight: Int } -enum QuickaddSortByInput { - INCIDENT_ID_DESC - SOURCE_DOMAIN_DESC - URL_ASC - URL_DESC - _ID_DESC - DATE_SUBMITTED_ASC - INCIDENT_ID_ASC - SOURCE_DOMAIN_ASC - _ID_ASC - DATE_SUBMITTED_DESC -} - -input TaxaField_listItem_fieldComplete_fromUpdateInput { +type TaxaField_listComplete_from { all: [String] - all_unset: Boolean current: [String] - current_unset: Boolean - entities: Boolean - entities_unset: Boolean } -type LogIncidentHistoryPayload { - incident_id: Int +type Incident { + AllegedDeployerOfAISystem: [Entity] + AllegedDeveloperOfAISystem: [Entity] + AllegedHarmedOrNearlyHarmedParties: [Entity] + implicated_systems: [Entity] + _id: ObjectId + date: String! + description: String + editor_dissimilar_incidents: [Int] + editor_notes: String + editor_similar_incidents: [Int] + editors: [User]! + embedding: IncidentEmbedding + epoch_date_modified: Int + flagged_dissimilar_incidents: [Int] + incident_id: Int! + nlp_similar_incidents: [IncidentNlp_similar_incident] + reports: [Report]! + title: String! + tsne: IncidentTsne +} + +type ClassificationAttribute { + short_name: String + value_json: String } -input ClassificationUpdateInput { - notes_unset: Boolean - attributes: [ClassificationAttributeUpdateInput] - reports_unset: Boolean - incidents: ClassificationIncidentsRelationInput - publish: Boolean - incidents_unset: Boolean - _id: ObjectId - namespace: String - _id_unset: Boolean - attributes_unset: Boolean - namespace_unset: Boolean - reports: ClassificationReportsRelationInput - notes: String - publish_unset: Boolean +scalar Long + +input History_incidentEmbeddingUpdateInput { + vector_unset: Boolean + from_reports: [Int] + from_reports_unset: Boolean + vector: [Float] } -input SubmissionHarmed_partiesRelationInput { - link: [String] - create: [EntityInsertInput] +input History_incidentNlp_similar_incidentUpdateInput { + incident_id: Int + incident_id_inc: Int + incident_id_unset: Boolean + similarity: Float + similarity_inc: Float + similarity_unset: Boolean } input TaxaField_listComplete_fromInsertInput { - all: [String] current: [String] + all: [String] } -input IncidentUpdateInput { - date: String - AllegedDeployerOfAISystem: IncidentAllegedDeployerOfAISystemRelationInput - AllegedDeveloperOfAISystem_unset: Boolean - description_unset: Boolean +input IncidentInsertInput { editor_notes: String - title: String - editor_similar_incidents_unset: Boolean _id: ObjectId - editors_unset: Boolean - epoch_date_modified_inc: Int - description: String - _id_unset: Boolean - editor_notes_unset: Boolean - reports_unset: Boolean - AllegedDeployerOfAISystem_unset: Boolean - editors: IncidentEditorsRelationInput - reports: IncidentReportsRelationInput - date_unset: Boolean - incident_id_unset: Boolean - AllegedDeveloperOfAISystem: IncidentAllegedDeveloperOfAISystemRelationInput - embedding_unset: Boolean + nlp_similar_incidents: [IncidentNlp_similar_incidentInsertInput] + date: String! flagged_dissimilar_incidents: [Int] + epoch_date_modified: Int editor_dissimilar_incidents: [Int] - tsne: IncidentTsneUpdateInput - AllegedHarmedOrNearlyHarmedParties: IncidentAllegedHarmedOrNearlyHarmedPartiesRelationInput - tsne_unset: Boolean - embedding: IncidentEmbeddingUpdateInput - epoch_date_modified_unset: Boolean - incident_id_inc: Int - nlp_similar_incidents_unset: Boolean - editor_dissimilar_incidents_unset: Boolean - AllegedHarmedOrNearlyHarmedParties_unset: Boolean editor_similar_incidents: [Int] - epoch_date_modified: Int - incident_id: Int - flagged_dissimilar_incidents_unset: Boolean - nlp_similar_incidents: [IncidentNlp_similar_incidentUpdateInput] - title_unset: Boolean -} - -input SubmissionDevelopersRelationInput { - link: [String] - create: [EntityInsertInput] -} - -type CandidateClassification_similarity { - classification: String - similarity: Float + reports: IncidentReportsRelationInput! + tsne: IncidentTsneInsertInput + AllegedDeveloperOfAISystem: IncidentAllegedDeveloperOfAISystemRelationInput + title: String! + AllegedHarmedOrNearlyHarmedParties: IncidentAllegedHarmedOrNearlyHarmedPartiesRelationInput + editors: IncidentEditorsRelationInput! + incident_id: Int! + embedding: IncidentEmbeddingInsertInput + AllegedDeployerOfAISystem: IncidentAllegedDeployerOfAISystemRelationInput + description: String + implicated_systems: IncidentImplicatedSystemsRelationInput } -enum SubmissionSortByInput { - DATE_SUBMITTED_DESC - EDITOR_NOTES_ASC - URL_DESC - TEXT_DESC - _ID_DESC - INCIDENT_DATE_DESC - INCIDENT_TITLE_DESC - LANGUAGE_ASC - TEXT_ASC - STATUS_DESC - DATE_DOWNLOADED_DESC - DESCRIPTION_ASC - IMAGE_URL_ASC - SOURCE_DOMAIN_DESC - STATUS_ASC - LANGUAGE_DESC - CLOUDINARY_ID_ASC - DATE_DOWNLOADED_ASC - DATE_MODIFIED_ASC - DATE_PUBLISHED_ASC - IMAGE_URL_DESC - EPOCH_DATE_MODIFIED_ASC - TITLE_DESC - USER_DESC - DATE_MODIFIED_DESC - INCIDENT_TITLE_ASC - URL_ASC - USER_ASC - DESCRIPTION_DESC - EPOCH_DATE_MODIFIED_DESC - INCIDENT_DATE_ASC - PLAIN_TEXT_DESC - SOURCE_DOMAIN_ASC - PLAIN_TEXT_ASC - TITLE_ASC - _ID_ASC - CLOUDINARY_ID_DESC - DATE_PUBLISHED_DESC - DATE_SUBMITTED_ASC - EDITOR_NOTES_DESC +type Notification { + _id: ObjectId + incident_id: Int + processed: Boolean + sentDate: DateTime + type: String + userId: User } -input ChecklistRiskQueryInput { - precedents_nin: [ChecklistRiskPrecedentQueryInput] - touched: Boolean - risk_status_lte: String - risk_status_in: [String] - id_lte: String - risk_status_gt: String - likelihood_gt: String - risk_notes_in: [String] - id_in: [String] - title_in: [String] - risk_status_ne: String - likelihood_gte: String - generated: Boolean - likelihood_in: [String] - tags_nin: [String] - title_gte: String - severity_exists: Boolean - severity_ne: String - title_nin: [String] - id_gt: String - precedents_exists: Boolean - generated_ne: Boolean - severity_gt: String - likelihood_nin: [String] - likelihood_exists: Boolean - id_ne: String - title_lt: String - title_lte: String - id_lt: String - risk_status_nin: [String] - severity_nin: [String] - touched_exists: Boolean - risk_notes_exists: Boolean - title_ne: String - likelihood_lt: String - id_gte: String - title_exists: Boolean - AND: [ChecklistRiskQueryInput!] - title_gt: String - severity_gte: String - risk_notes_gt: String - title: String - precedents_in: [ChecklistRiskPrecedentQueryInput] - severity_lt: String - tags_in: [String] - generated_exists: Boolean - likelihood: String - risk_status_lt: String - risk_status_gte: String - severity_lte: String - risk_notes_nin: [String] - tags_exists: Boolean - OR: [ChecklistRiskQueryInput!] - risk_notes_ne: String - risk_notes_lt: String - tags: [String] - risk_notes_gte: String - touched_ne: Boolean - severity: String - severity_in: [String] - likelihood_ne: String - risk_notes_lte: String - id: String - risk_status: String - likelihood_lte: String - risk_notes: String - precedents: [ChecklistRiskPrecedentQueryInput] - id_nin: [String] - id_exists: Boolean - risk_status_exists: Boolean +input IncidentEmbeddingQueryInput { + vector_exists: Boolean + from_reports_exists: Boolean + vector_in: [Float] + vector_nin: [Float] + OR: [IncidentEmbeddingQueryInput!] + from_reports: [Int] + from_reports_in: [Int] + AND: [IncidentEmbeddingQueryInput!] + from_reports_nin: [Int] + vector: [Float] } -type RisksPayloadItem { - precedents: [RisksPayloadPrecedent] - tag: String - tags: [String] - title: String +type Classification { + _id: ObjectId + attributes: [ClassificationAttribute] + incidents: [Incident]! + namespace: String! + notes: String + publish: Boolean + reports: [Report]! } -type RisksPayloadPrecedentTsne { +type History_incidentTsne { x: Float y: Float } -input TaxaField_listComplete_fromUpdateInput { - all_unset: Boolean - current: [String] - current_unset: Boolean - all: [String] -} - -input SubmissionNlp_similar_incidentInsertInput { - similarity: Float - incident_id: Int +type Checklist { + _id: ObjectId + about: String + date_created: DateTime + date_updated: DateTime + entity_id: String + id: String + name: String + owner_id: String + risks: [ChecklistRisk] + tags_goals: [String] + tags_methods: [String] + tags_other: [String] } -input LinkReportsToIncidentsInput { - report_numbers: [Int] - incident_ids: [Int] +type ChecklistRisk { + generated: Boolean + id: String + likelihood: String + precedents: [ChecklistRiskPrecedent] + risk_notes: String + risk_status: String + severity: String + tags: [String] + title: String + touched: Boolean } -scalar Long - -type ClassificationAttribute { - short_name: String - value_json: String +input SubmissionHarmed_partiesRelationInput { + create: [EntityInsertInput] + link: [String] } -input IncidentAllegedDeveloperOfAISystemRelationInput { - link: [String] - create: [EntityInsertInput] +input SubmissionEmbeddingUpdateInput { + vector_unset: Boolean + from_text_hash: String + from_text_hash_unset: Boolean + vector: [Float] } -input NotificationQueryInput { +input NotificationInsertInput { + _id: ObjectId incident_id: Int - type_in: [String] - _id_ne: ObjectId - type_lt: String - type_nin: [String] - sentDate: DateTime - sentDate_exists: Boolean - type_ne: String - type_gt: String - incident_id_lt: Int - incident_id_gte: Int - sentDate_lte: DateTime processed: Boolean - type_exists: Boolean - _id_gte: ObjectId - _id_lte: ObjectId - sentDate_lt: DateTime - incident_id_ne: Int - _id: ObjectId - sentDate_ne: DateTime - _id_exists: Boolean - sentDate_in: [DateTime] - incident_id_exists: Boolean - processed_exists: Boolean - sentDate_gte: DateTime - type_lte: String - _id_gt: ObjectId - sentDate_gt: DateTime - userId: UserQueryInput - sentDate_nin: [DateTime] - _id_in: [ObjectId] - _id_lt: ObjectId - userId_exists: Boolean - OR: [NotificationQueryInput!] - incident_id_nin: [Int] - _id_nin: [ObjectId] - AND: [NotificationQueryInput!] - incident_id_lte: Int - incident_id_gt: Int - processed_ne: Boolean - type_gte: String + sentDate: DateTime type: String - incident_id_in: [Int] + userId: NotificationUserIdRelationInput } -input IncidentQueryInput { - AllegedDeveloperOfAISystem_in: [EntityQueryInput] - embedding: IncidentEmbeddingQueryInput - AllegedHarmedOrNearlyHarmedParties_in: [EntityQueryInput] - _id_in: [ObjectId] - AllegedDeployerOfAISystem: [EntityQueryInput] - editor_notes: String - AND: [IncidentQueryInput!] - _id_gte: ObjectId - nlp_similar_incidents: [IncidentNlp_similar_incidentQueryInput] - date: String - title_in: [String] - editor_notes_nin: [String] - editor_similar_incidents_exists: Boolean - reports_nin: [ReportQueryInput] - editor_notes_lt: String - epoch_date_modified_nin: [Int] - AllegedDeveloperOfAISystem_exists: Boolean - AllegedHarmedOrNearlyHarmedParties_exists: Boolean - description_ne: String - editor_dissimilar_incidents_in: [Int] - epoch_date_modified_gte: Int - incident_id_lte: Int - editor_dissimilar_incidents_exists: Boolean - incident_id_nin: [Int] - editor_notes_exists: Boolean - AllegedDeployerOfAISystem_nin: [EntityQueryInput] - title_lt: String - AllegedDeveloperOfAISystem: [EntityQueryInput] - description_lt: String - epoch_date_modified_lt: Int - date_in: [String] - flagged_dissimilar_incidents_exists: Boolean - incident_id_ne: Int - _id_lte: ObjectId - date_nin: [String] - incident_id: Int - editor_similar_incidents: [Int] - date_lte: String - editor_dissimilar_incidents: [Int] - incident_id_lt: Int - reports: [ReportQueryInput] - tsne_exists: Boolean - date_gte: String - reports_exists: Boolean - _id_lt: ObjectId - date_lt: String - title: String - AllegedDeveloperOfAISystem_nin: [EntityQueryInput] - editor_notes_gte: String - editor_similar_incidents_nin: [Int] - description_in: [String] - incident_id_gte: Int - _id_ne: ObjectId - incident_id_gt: Int - description_exists: Boolean - epoch_date_modified_lte: Int - epoch_date_modified: Int - date_gt: String - title_gte: String - date_ne: String - incident_id_in: [Int] - date_exists: Boolean - _id_exists: Boolean - nlp_similar_incidents_in: [IncidentNlp_similar_incidentQueryInput] - _id_gt: ObjectId - title_gt: String - incident_id_exists: Boolean - description_gte: String - title_lte: String - epoch_date_modified_gt: Int - title_exists: Boolean - flagged_dissimilar_incidents: [Int] - flagged_dissimilar_incidents_nin: [Int] - editor_notes_lte: String - AllegedHarmedOrNearlyHarmedParties: [EntityQueryInput] +input History_reportUpdateInput { + epoch_date_downloaded: Int + date_submitted_unset: Boolean + embedding: History_reportEmbeddingUpdateInput + date_submitted: DateTime description: String - description_gt: String - editor_similar_incidents_in: [Int] - description_nin: [String] - embedding_exists: Boolean - description_lte: String - epoch_date_modified_ne: Int - editors_nin: [UserQueryInput] - flagged_dissimilar_incidents_in: [Int] - editor_notes_in: [String] - editors_exists: Boolean - OR: [IncidentQueryInput!] - title_nin: [String] - nlp_similar_incidents_nin: [IncidentNlp_similar_incidentQueryInput] - editor_notes_gt: String - editors: [UserQueryInput] - AllegedHarmedOrNearlyHarmedParties_nin: [EntityQueryInput] - epoch_date_modified_in: [Int] - editors_in: [UserQueryInput] - tsne: IncidentTsneQueryInput - title_ne: String - AllegedDeployerOfAISystem_in: [EntityQueryInput] - _id_nin: [ObjectId] - nlp_similar_incidents_exists: Boolean + title_unset: Boolean + _id_unset: Boolean + epoch_date_submitted_unset: Boolean + is_incident_report: Boolean _id: ObjectId - editor_dissimilar_incidents_nin: [Int] - reports_in: [ReportQueryInput] - AllegedDeployerOfAISystem_exists: Boolean - editor_notes_ne: String - epoch_date_modified_exists: Boolean -} - -input History_incidentEmbeddingQueryInput { - vector: [Float] - vector_in: [Float] - vector_exists: Boolean - OR: [History_incidentEmbeddingQueryInput!] - from_reports_exists: Boolean - AND: [History_incidentEmbeddingQueryInput!] - from_reports: [Int] - from_reports_in: [Int] - from_reports_nin: [Int] - vector_nin: [Float] + quiet: Boolean + url: String + epoch_date_published_unset: Boolean + user_unset: Boolean + date_modified_unset: Boolean + is_incident_report_unset: Boolean + date_modified: DateTime + modifiedBy_unset: Boolean + epoch_date_submitted_inc: Int + date_downloaded_unset: Boolean + report_number: Int + source_domain: String + flag: Boolean + tags_unset: Boolean + text: String + report_number_unset: Boolean + tags: [String] + source_domain_unset: Boolean + authors: [String] + image_url: String + submitters: [String] + epoch_date_modified: Int + language: String + date_published_unset: Boolean + description_unset: Boolean + submitters_unset: Boolean + editor_notes_unset: Boolean + language_unset: Boolean + text_unset: Boolean + embedding_unset: Boolean + authors_unset: Boolean + editor_notes: String + title: String + epoch_date_modified_unset: Boolean + user: String + flag_unset: Boolean + epoch_date_submitted: Int + date_downloaded: DateTime + image_url_unset: Boolean + cloudinary_id_unset: Boolean + modifiedBy: String + epoch_date_downloaded_inc: Int + epoch_date_published: Int + url_unset: Boolean + inputs_outputs_unset: Boolean + quiet_unset: Boolean + cloudinary_id: String + plain_text: String + epoch_date_published_inc: Int + epoch_date_downloaded_unset: Boolean + plain_text_unset: Boolean + report_number_inc: Int + epoch_date_modified_inc: Int + inputs_outputs: [String] + date_published: DateTime } -input IncidentReportsRelationInput { - create: [ReportInsertInput] - link: [Int] +input History_incidentNlp_similar_incidentQueryInput { + similarity_lt: Float + incident_id_nin: [Int] + AND: [History_incidentNlp_similar_incidentQueryInput!] + incident_id_gt: Int + similarity_gt: Float + incident_id_ne: Int + similarity_exists: Boolean + incident_id_lte: Int + incident_id_in: [Int] + incident_id_lt: Int + similarity_gte: Float + similarity_in: [Float] + similarity_ne: Float + similarity_lte: Float + similarity: Float + similarity_nin: [Float] + OR: [History_incidentNlp_similar_incidentQueryInput!] + incident_id: Int + incident_id_exists: Boolean + incident_id_gte: Int } -type Submission { +input ReportInsertInput { + description: String + tags: [String]! + text: String! + submitters: [String]! + image_url: String! + is_incident_report: Boolean + source_domain: String! + url: String! + cloudinary_id: String! + embedding: ReportEmbeddingInsertInput _id: ObjectId + date_submitted: DateTime! authors: [String]! - cloudinary_id: String - date_downloaded: String! - date_modified: String! - date_published: String! - date_submitted: String! - deployers: [Entity] - description: String - developers: [Entity] - editor_dissimilar_incidents: [Int] + date_downloaded: DateTime! editor_notes: String - editor_similar_incidents: [Int] - embedding: SubmissionEmbedding - epoch_date_modified: Int - harmed_parties: [Entity] - image_url: String! - incident_date: String - incident_editors: [User] - incident_ids: [Int] - incident_title: String - language: String! - nlp_similar_incidents: [SubmissionNlp_similar_incident] - plain_text: String + epoch_date_submitted: Int! + date_modified: DateTime! + epoch_date_modified: Int! quiet: Boolean - source_domain: String! - status: String - submitters: [String]! - tags: [String]! - text: String! + plain_text: String! title: String! - url: String! - user: User -} - -input ClassificationIncidentsRelationInput { - create: [IncidentInsertInput] - link: [Int] + language: String! + user: ReportUserRelationInput + epoch_date_downloaded: Int! + inputs_outputs: [String] + date_published: DateTime! + report_number: Int! + epoch_date_published: Int! + flag: Boolean } -input CandidateEmbeddingInsertInput { - from_text_hash: String - vector: [Float] +input TaxaDummy_fieldInsertInput { + short_name: String + field_number: String } -input ChecklistRiskPrecedentUpdateInput { - title: String - title_unset: Boolean - description: String - incident_id_unset: Boolean - tags: [String] - tags_unset: Boolean +input IncidentNlp_similar_incidentQueryInput { + similarity_lte: Float + similarity_nin: [Float] + similarity_ne: Float incident_id: Int - incident_id_inc: Int - description_unset: Boolean + incident_id_lte: Int + OR: [IncidentNlp_similar_incidentQueryInput!] + incident_id_gt: Int + incident_id_gte: Int + similarity_in: [Float] + similarity_gt: Float + similarity_lt: Float + incident_id_nin: [Int] + similarity_exists: Boolean + incident_id_exists: Boolean + incident_id_lt: Int + incident_id_in: [Int] + incident_id_ne: Int + similarity: Float + AND: [IncidentNlp_similar_incidentQueryInput!] + similarity_gte: Float } -type CreateVariantPayload { - incident_id: Int - report_number: Int +input QuickaddQueryInput { + incident_id: Long + url_exists: Boolean + date_submitted_lte: String + incident_id_lt: Long + source_domain_gt: String + date_submitted: String + AND: [QuickaddQueryInput!] + incident_id_ne: Long + url_gt: String + source_domain_lt: String + url_lt: String + date_submitted_nin: [String] + incident_id_nin: [Long] + _id_gte: ObjectId + OR: [QuickaddQueryInput!] + _id_lt: ObjectId + incident_id_in: [Long] + date_submitted_in: [String] + incident_id_gte: Long + date_submitted_ne: String + _id_ne: ObjectId + _id_gt: ObjectId + url: String + _id_exists: Boolean + date_submitted_gte: String + source_domain_ne: String + url_lte: String + date_submitted_lt: String + source_domain_in: [String] + source_domain: String + _id_nin: [ObjectId] + source_domain_exists: Boolean + date_submitted_exists: Boolean + source_domain_lte: String + source_domain_gte: String + incident_id_lte: Long + source_domain_nin: [String] + _id: ObjectId + date_submitted_gt: String + url_in: [String] + _id_in: [ObjectId] + incident_id_exists: Boolean + incident_id_gt: Long + url_ne: String + _id_lte: ObjectId + url_gte: String + url_nin: [String] } -input History_incidentEmbeddingUpdateInput { - vector: [Float] - vector_unset: Boolean - from_reports: [Int] - from_reports_unset: Boolean +type History_incidentNlp_similar_incident { + incident_id: Int + similarity: Float } -input PromoteSubmissionToReportInput { - is_incident_report: Boolean - submission_id: ObjectId - incident_ids: [Int] +type CandidateClassification_similarity { + classification: String + similarity: Float } -input SubmissionNlp_similar_incidentQueryInput { - AND: [SubmissionNlp_similar_incidentQueryInput!] - incident_id_lte: Int - similarity_ne: Float - incident_id_in: [Int] - incident_id: Int +input SubscriptionQueryInput { + entityId_exists: Boolean + type_gte: String + type: String + _id_lt: ObjectId + _id_exists: Boolean + _id_in: [ObjectId] + type_lte: String + userId: UserQueryInput + type_lt: String + entityId: EntityQueryInput + incident_id: IncidentQueryInput + _id_lte: ObjectId + type_gt: String + OR: [SubscriptionQueryInput!] + type_exists: Boolean + _id_gte: ObjectId + _id_gt: ObjectId + type_ne: String + type_in: [String] + AND: [SubscriptionQueryInput!] + _id_nin: [ObjectId] + userId_exists: Boolean incident_id_exists: Boolean - incident_id_lt: Int - similarity_lt: Float - similarity_in: [Float] - similarity_lte: Float - similarity: Float - incident_id_gte: Int - similarity_nin: [Float] - OR: [SubmissionNlp_similar_incidentQueryInput!] - incident_id_ne: Int - similarity_exists: Boolean - similarity_gte: Float - similarity_gt: Float - incident_id_nin: [Int] - incident_id_gt: Int + _id: ObjectId + _id_ne: ObjectId + type_nin: [String] } input SubmissionUserRelationInput { - create: UserInsertInput link: String + create: UserInsertInput } -input SubmissionDeployersRelationInput { - create: [EntityInsertInput] - link: [String] +input CandidateClassification_similarityUpdateInput { + similarity: Float + similarity_inc: Float + similarity_unset: Boolean + classification: String + classification_unset: Boolean } -input SubmissionQueryInput { - nlp_similar_incidents_in: [SubmissionNlp_similar_incidentQueryInput] - nlp_similar_incidents_nin: [SubmissionNlp_similar_incidentQueryInput] - deployers_exists: Boolean - title: String - status_gte: String - incident_editors: [UserQueryInput] - _id_gte: ObjectId - OR: [SubmissionQueryInput!] - incident_ids_exists: Boolean - incident_date_ne: String +type LogIncidentHistoryPayload { + incident_id: Int +} + +input SubmissionQueryInput { + date_downloaded_lte: String + _id_lte: ObjectId incident_title_lt: String - date_downloaded_gt: String - submitters_exists: Boolean - status_exists: Boolean - tags_nin: [String] - date_published_lt: String - language_gte: String - epoch_date_modified_gte: Int - date_downloaded_ne: String - editor_dissimilar_incidents_in: [Int] - editor_notes_lt: String - editor_similar_incidents_in: [Int] + description_nin: [String] + cloudinary_id_gt: String + _id_exists: Boolean + epoch_date_modified_gt: Int + language_nin: [String] + date_published_exists: Boolean + incident_date_gt: String + url_ne: String + user: UserQueryInput + epoch_date_modified_ne: Int + developers: [EntityQueryInput] + incident_title_in: [String] + plain_text_gt: String + harmed_parties_exists: Boolean + date_submitted_lte: String date_published: String - plain_text_in: [String] - quiet_ne: Boolean - text_gte: String - incident_title_ne: String - title_gt: String - date_modified_lt: String - title_nin: [String] - status_in: [String] - editor_similar_incidents_exists: Boolean - _id_lte: ObjectId - status_nin: [String] url_gt: String - date_modified_exists: Boolean - editor_notes_gte: String - date_submitted_gte: String - epoch_date_modified_nin: [Int] - date_submitted_exists: Boolean - _id_nin: [ObjectId] - status_gt: String - developers_exists: Boolean - url_in: [String] - embedding: SubmissionEmbeddingQueryInput + date_published_gte: String + submitters: [String] + incident_date_lte: String + editor_dissimilar_incidents_nin: [Int] + incident_title_exists: Boolean + date_downloaded_lt: String + text_ne: String + cloudinary_id_gte: String + text_exists: Boolean + source_domain_lte: String + editor_notes_ne: String + harmed_parties: [EntityQueryInput] + incident_date_nin: [String] + user_exists: Boolean + _id_gt: ObjectId + incident_title_nin: [String] + date_published_in: [String] + date_downloaded: String + status_nin: [String] + date_submitted_gt: String + url: String + nlp_similar_incidents: [SubmissionNlp_similar_incidentQueryInput] + _id: ObjectId + tags_nin: [String] + date_downloaded_nin: [String] date_submitted_lt: String - plain_text_gte: String + _id_lt: ObjectId + submitters_in: [String] date_published_ne: String - url_lte: String - url_exists: Boolean - date_modified_gte: String - plain_text: String source_domain_ne: String - image_url_gte: String - nlp_similar_incidents_exists: Boolean - source_domain_gte: String - incident_title_in: [String] - date_downloaded_lte: String - submitters: [String] - date_modified_lte: String - editor_dissimilar_incidents: [Int] + status_gte: String + url_nin: [String] + nlp_similar_incidents_nin: [SubmissionNlp_similar_incidentQueryInput] + description_exists: Boolean + incident_title_gte: String date_submitted_ne: String - incident_date_in: [String] - language_lte: String - quiet: Boolean - editor_dissimilar_incidents_exists: Boolean - description_lte: String - text_ne: String - editor_notes_lte: String + incident_date_exists: Boolean + _id_nin: [ObjectId] + date_modified_nin: [String] + incident_editors: [UserQueryInput] + title_ne: String + plain_text_nin: [String] + quiet_exists: Boolean + date_modified_exists: Boolean + image_url_ne: String + date_submitted: String + image_url_in: [String] + status_gt: String + status_lt: String + source_domain_nin: [String] plain_text_lt: String - incident_date_lt: String - source_domain_exists: Boolean - editor_notes_nin: [String] - embedding_exists: Boolean - _id: ObjectId - nlp_similar_incidents: [SubmissionNlp_similar_incidentQueryInput] - url: String - editor_dissimilar_incidents_nin: [Int] - _id_exists: Boolean - status_ne: String + editor_notes_lt: String + incident_title_gt: String + date_modified_gt: String + date_modified_lt: String image_url_gt: String - source_domain_lte: String + language_gte: String + image_url_nin: [String] + url_exists: Boolean + text_gt: String + title_gte: String language_in: [String] - editor_notes_in: [String] - source_domain_in: [String] - epoch_date_modified_lt: Int - plain_text_exists: Boolean - AND: [SubmissionQueryInput!] - incident_editors_exists: Boolean - cloudinary_id: String - source_domain_gt: String - user_exists: Boolean - epoch_date_modified_in: [Int] - _id_in: [ObjectId] - authors_nin: [String] - plain_text_nin: [String] - date_downloaded_gte: String - image_url_lte: String - date_published_nin: [String] - cloudinary_id_nin: [String] - deployers: [EntityQueryInput] - developers_nin: [EntityQueryInput] - date_published_in: [String] - date_published_lte: String - editor_notes_exists: Boolean - incident_ids_nin: [Int] - plain_text_ne: String - url_lt: String epoch_date_modified_exists: Boolean - language_exists: Boolean - image_url_ne: String - plain_text_lte: String - editor_notes_ne: String + date_modified_lte: String + epoch_date_modified_gte: Int + incident_date_gte: String + editor_notes_gt: String epoch_date_modified_lte: Int - description_exists: Boolean - authors: [String] - status_lt: String - submitters_nin: [String] - editor_similar_incidents: [Int] - url_nin: [String] - url_ne: String - _id_ne: ObjectId - plain_text_gt: String - deployers_in: [EntityQueryInput] - deployers_nin: [EntityQueryInput] - text: String - incident_title_exists: Boolean - title_exists: Boolean + editor_notes: String + date_downloaded_gt: String + embedding: SubmissionEmbeddingQueryInput + quiet: Boolean incident_title: String - date_published_gte: String - date_modified_ne: String - editor_notes_gt: String - authors_in: [String] - text_lt: String - incident_ids: [Int] - incident_date_exists: Boolean - epoch_date_modified_gt: Int - developers: [EntityQueryInput] - harmed_parties: [EntityQueryInput] - image_url_nin: [String] - incident_title_gte: String - incident_title_nin: [String] + developers_exists: Boolean + incident_date_lt: String + plain_text_lte: String + language_ne: String + language_lt: String + image_url_lte: String + title_exists: Boolean + image_url_lt: String + source_domain: String + description: String + embedding_exists: Boolean + editor_notes_nin: [String] + incident_ids_in: [Int] + date_published_lt: String + date_published_gt: String + status_exists: Boolean + description_lt: String tags_in: [String] - text_nin: [String] - date_modified_gt: String - incident_title_lte: String - text_lte: String - editor_notes: String - date_published_exists: Boolean - description_gte: String - status: String - description_in: [String] - language_gt: String - cloudinary_id_exists: Boolean - authors_exists: Boolean - cloudinary_id_gt: String - cloudinary_id_ne: String - title_lt: String - incident_title_gt: String - incident_date: String + cloudinary_id: String + plain_text_gte: String status_lte: String - description_lt: String - date_modified_nin: [String] - tags: [String] - image_url_in: [String] - date_downloaded_in: [String] - language_ne: String - description_ne: String - description_nin: [String] + developers_nin: [EntityQueryInput] + language: String + tags_exists: Boolean + deployers_in: [EntityQueryInput] + developers_in: [EntityQueryInput] + editor_similar_incidents_exists: Boolean + _id_in: [ObjectId] + url_in: [String] + _id_ne: ObjectId + incident_editors_in: [UserQueryInput] + nlp_similar_incidents_in: [SubmissionNlp_similar_incidentQueryInput] harmed_parties_in: [EntityQueryInput] - image_url: String - tags_exists: Boolean - harmed_parties_nin: [EntityQueryInput] - cloudinary_id_gte: String - text_in: [String] - date_submitted: String - epoch_date_modified: Int - _id_gt: ObjectId - text_gt: String - date_downloaded_exists: Boolean - date_modified: String - submitters_in: [String] - date_published_gt: String + description_in: [String] + submitters_nin: [String] cloudinary_id_lt: String - incident_date_lte: String - date_submitted_gt: String + OR: [SubmissionQueryInput!] + description_lte: String + text_gte: String + plain_text_in: [String] + language_gt: String + cloudinary_id_exists: Boolean + description_gt: String + title: String + date_downloaded_gte: String + date_modified: String + editor_dissimilar_incidents_in: [Int] + authors_nin: [String] + incident_title_lte: String + epoch_date_modified_in: [Int] + date_modified_ne: String + editor_notes_in: [String] + plain_text_ne: String + date_downloaded_exists: Boolean + date_submitted_exists: Boolean + incident_title_ne: String + date_submitted_nin: [String] + _id_gte: ObjectId + date_submitted_in: [String] + nlp_similar_incidents_exists: Boolean + status_ne: String + incident_editors_nin: [UserQueryInput] + description_ne: String + epoch_date_modified_nin: [Int] + authors: [String] + cloudinary_id_nin: [String] + editor_notes_lte: String + epoch_date_modified_lt: Int editor_similar_incidents_nin: [Int] - incident_date_gte: String - date_downloaded: String + title_lt: String + deployers_nin: [EntityQueryInput] + description_gte: String + text_nin: [String] + language_exists: Boolean + image_url: String + language_lte: String + editor_similar_incidents_in: [Int] + tags: [String] + incident_date_in: [String] + source_domain_lt: String + url_lt: String + cloudinary_id_ne: String + quiet_ne: Boolean + authors_in: [String] + title_nin: [String] + image_url_gte: String + source_domain_in: [String] + title_gt: String date_modified_in: [String] - epoch_date_modified_ne: Int - source_domain: String - language: String - description: String - text_exists: Boolean - date_submitted_nin: [String] - quiet_exists: Boolean - date_downloaded_lt: String + plain_text_exists: Boolean + incident_ids_exists: Boolean + image_url_exists: Boolean + AND: [SubmissionQueryInput!] + authors_exists: Boolean + deployers_exists: Boolean + cloudinary_id_in: [String] + url_gte: String + date_published_nin: [String] + date_modified_gte: String + editor_dissimilar_incidents: [Int] + status: String title_in: [String] - user: UserQueryInput - cloudinary_id_lte: String - date_downloaded_nin: [String] + date_submitted_gte: String + editor_dissimilar_incidents_exists: Boolean + incident_ids: [Int] + text_lt: String + incident_editors_exists: Boolean + submitters_exists: Boolean + plain_text: String + editor_notes_gte: String + incident_date_ne: String + epoch_date_modified: Int + editor_similar_incidents: [Int] + incident_date: String + url_lte: String + date_published_lte: String + status_in: [String] + incident_ids_nin: [Int] + date_downloaded_ne: String + date_downloaded_in: [String] + text_lte: String + deployers: [EntityQueryInput] + source_domain_gte: String + text: String + source_domain_exists: Boolean + source_domain_gt: String title_lte: String - source_domain_nin: [String] - incident_date_gt: String - cloudinary_id_in: [String] - title_ne: String - title_gte: String - source_domain_lt: String + editor_notes_exists: Boolean + text_in: [String] + cloudinary_id_lte: String + harmed_parties_nin: [EntityQueryInput] +} + +input EntityQueryInput { + AND: [EntityQueryInput!] + _id_gt: ObjectId + created_at_gte: DateTime + name: String + entity_id_gte: String + _id_lte: ObjectId + created_at_lt: DateTime + date_modified_lt: DateTime + date_modified_lte: DateTime + name_in: [String] + date_modified_gte: DateTime + date_modified_in: [DateTime] + date_modified_nin: [DateTime] + name_lt: String + entity_id_lt: String + name_gt: String + created_at_in: [DateTime] + created_at_exists: Boolean _id_lt: ObjectId - image_url_lt: String - language_nin: [String] - harmed_parties_exists: Boolean - date_submitted_lte: String - incident_date_nin: [String] - description_gt: String - incident_editors_nin: [UserQueryInput] - url_gte: String - image_url_exists: Boolean - incident_ids_in: [Int] - developers_in: [EntityQueryInput] - language_lt: String - incident_editors_in: [UserQueryInput] - date_submitted_in: [String] + entity_id_ne: String + OR: [EntityQueryInput!] + _id_gte: ObjectId + _id_ne: ObjectId + name_nin: [String] + name_ne: String + created_at: DateTime + entity_id_nin: [String] + name_gte: String + entity_id: String + name_lte: String + _id_exists: Boolean + date_modified: DateTime + created_at_lte: DateTime + _id: ObjectId + created_at_gt: DateTime + created_at_ne: DateTime + name_exists: Boolean + created_at_nin: [DateTime] + date_modified_exists: Boolean + entity_id_lte: String + entity_id_exists: Boolean + entity_id_gt: String + date_modified_ne: DateTime + date_modified_gt: DateTime + _id_in: [ObjectId] + _id_nin: [ObjectId] + entity_id_in: [String] } -enum History_reportSortByInput { - _ID_DESC - CLOUDINARY_ID_DESC - DATE_PUBLISHED_ASC - EDITOR_NOTES_DESC - EPOCH_DATE_DOWNLOADED_DESC - MODIFIEDBY_DESC - DATE_PUBLISHED_DESC - DESCRIPTION_ASC - IMAGE_URL_ASC - REPORT_NUMBER_DESC - EPOCH_DATE_SUBMITTED_ASC - LANGUAGE_ASC - PLAIN_TEXT_DESC - USER_DESC - DATE_DOWNLOADED_DESC - DATE_SUBMITTED_DESC - EPOCH_DATE_MODIFIED_ASC - MODIFIEDBY_ASC - URL_ASC - USER_ASC - TITLE_ASC - DATE_DOWNLOADED_ASC - DATE_MODIFIED_DESC - EPOCH_DATE_DOWNLOADED_ASC - EPOCH_DATE_PUBLISHED_ASC - EPOCH_DATE_SUBMITTED_DESC - SOURCE_DOMAIN_ASC - TEXT_DESC - URL_DESC - DATE_SUBMITTED_ASC - EPOCH_DATE_PUBLISHED_DESC - LANGUAGE_DESC - TEXT_ASC - TITLE_DESC - PLAIN_TEXT_ASC - _ID_ASC - CLOUDINARY_ID_ASC - DATE_MODIFIED_ASC - DESCRIPTION_DESC - EDITOR_NOTES_ASC - EPOCH_DATE_MODIFIED_DESC - IMAGE_URL_DESC - REPORT_NUMBER_ASC - SOURCE_DOMAIN_DESC +type SubmissionEmbedding { + from_text_hash: String + vector: [Float] +} + +type CandidateEmbedding { + from_text_hash: String + vector: [Float] +} + +input RisksInput { + tags: [String] +} + +type RisksPayloadItem { + precedents: [RisksPayloadPrecedent] + tag: String + tags: [String] + title: String +} + +input SubmissionNlp_similar_incidentInsertInput { + similarity: Float + incident_id: Int } -input SubscriptionIncident_idRelationInput { - link: Int - create: IncidentInsertInput +input History_incidentTsneQueryInput { + y_lt: Float + y_lte: Float + x_gte: Float + y_exists: Boolean + y_in: [Float] + x_ne: Float + y_gt: Float + y_ne: Float + x: Float + y_gte: Float + x_lt: Float + y: Float + x_in: [Float] + x_gt: Float + x_lte: Float + x_exists: Boolean + y_nin: [Float] + OR: [History_incidentTsneQueryInput!] + x_nin: [Float] + AND: [History_incidentTsneQueryInput!] } -type ChecklistRisk { - generated: Boolean - id: String - likelihood: String - precedents: [ChecklistRiskPrecedent] - risk_notes: String - risk_status: String - severity: String - tags: [String] - title: String - touched: Boolean +type RisksPayloadPrecedentTsne { + x: Float + y: Float } -input SubscriptionEntityIdRelationInput { - link: String - create: EntityInsertInput +input SubmissionEmbeddingInsertInput { + from_text_hash: String + vector: [Float] +} + +input SubmissionDeployersRelationInput { + create: [EntityInsertInput] + link: [String] +} + +input IncidentAllegedHarmedOrNearlyHarmedPartiesRelationInput { + link: [String] + create: [EntityInsertInput] +} + +input UserUpdateInput { + userId: String + first_name: String + roles_unset: Boolean + _id: ObjectId + last_name: String + roles: [String] + userId_unset: Boolean + _id_unset: Boolean + last_name_unset: Boolean + first_name_unset: Boolean } enum UserSortByInput { @@ -4026,311 +4112,196 @@ enum UserSortByInput { USERID_DESC } -input TaxaQueryInput { - namespace_lte: String - OR: [TaxaQueryInput!] - weight: Int - description_ne: String - _id_gte: ObjectId - description_lte: String - namespace_gt: String - description_lt: String - weight_gte: Int - complete_entities_exists: Boolean - _id_exists: Boolean - _id_gt: ObjectId - field_list: [TaxaField_listQueryInput] - dummy_fields_nin: [TaxaDummy_fieldQueryInput] - weight_ne: Int - weight_lt: Int - _id_in: [ObjectId] - namespace_exists: Boolean - description_nin: [String] - namespace_ne: String - field_list_nin: [TaxaField_listQueryInput] - description: String - _id_nin: [ObjectId] - complete_entities: Boolean - _id_lt: ObjectId - weight_in: [Int] - weight_lte: Int - field_list_in: [TaxaField_listQueryInput] - field_list_exists: Boolean - namespace_in: [String] - dummy_fields_in: [TaxaDummy_fieldQueryInput] - weight_gt: Int - weight_nin: [Int] - namespace_nin: [String] - _id_lte: ObjectId - description_gte: String - AND: [TaxaQueryInput!] - description_exists: Boolean - namespace_gte: String - description_gt: String - dummy_fields: [TaxaDummy_fieldQueryInput] - namespace: String - weight_exists: Boolean - namespace_lt: String - _id_ne: ObjectId - description_in: [String] - complete_entities_ne: Boolean - _id: ObjectId - dummy_fields_exists: Boolean -} - -input ClassificationAttributeQueryInput { - short_name_gt: String - short_name_nin: [String] - value_json_exists: Boolean - AND: [ClassificationAttributeQueryInput!] - OR: [ClassificationAttributeQueryInput!] - short_name: String - short_name_ne: String - value_json_lte: String - value_json_in: [String] - value_json_gt: String - value_json_gte: String - value_json_lt: String - value_json_nin: [String] - short_name_lte: String - short_name_lt: String - short_name_gte: String - value_json: String - value_json_ne: String - short_name_exists: Boolean - short_name_in: [String] -} - -input ChecklistRiskUpdateInput { - precedents_unset: Boolean - risk_notes: String - risk_status: String - touched_unset: Boolean - likelihood_unset: Boolean - risk_status_unset: Boolean - tags_unset: Boolean - title: String - id_unset: Boolean - precedents: [ChecklistRiskPrecedentUpdateInput] - generated: Boolean - id: String - tags: [String] - touched: Boolean - likelihood: String - severity: String - risk_notes_unset: Boolean - generated_unset: Boolean - severity_unset: Boolean - title_unset: Boolean +type Mutation { + createDefaultAdminUser(input: CreateDefaultAdminUserInput): DefaultAdminUser + deleteManyCandidates(query: CandidateQueryInput): DeleteManyPayload + deleteManyChecklists(query: ChecklistQueryInput): DeleteManyPayload + deleteManyClassifications(query: ClassificationQueryInput): DeleteManyPayload + deleteManyDuplicates(query: DuplicateQueryInput): DeleteManyPayload + deleteManyEntities(query: EntityQueryInput): DeleteManyPayload + deleteManyHistory_incidents(query: History_incidentQueryInput): DeleteManyPayload + deleteManyHistory_reports(query: History_reportQueryInput): DeleteManyPayload + deleteManyIncidents(query: IncidentQueryInput): DeleteManyPayload + deleteManyNotifications(query: NotificationQueryInput): DeleteManyPayload + deleteManyQuickadds(query: QuickaddQueryInput): DeleteManyPayload + deleteManyReports(query: ReportQueryInput): DeleteManyPayload + deleteManySubmissions(query: SubmissionQueryInput): DeleteManyPayload + deleteManySubscriptions(query: SubscriptionQueryInput): DeleteManyPayload + deleteManyTaxas(query: TaxaQueryInput): DeleteManyPayload + deleteManyUsers(query: UserQueryInput): DeleteManyPayload + deleteOneCandidate(query: CandidateQueryInput!): Candidate + deleteOneChecklist(query: ChecklistQueryInput!): Checklist + deleteOneClassification(query: ClassificationQueryInput!): Classification + deleteOneDuplicate(query: DuplicateQueryInput!): Duplicate + deleteOneEntity(query: EntityQueryInput!): Entity + deleteOneHistory_incident(query: History_incidentQueryInput!): History_incident + deleteOneHistory_report(query: History_reportQueryInput!): History_report + deleteOneIncident(query: IncidentQueryInput!): Incident + deleteOneNotification(query: NotificationQueryInput!): Notification + deleteOneQuickadd(query: QuickaddQueryInput!): Quickadd + deleteOneReport(query: ReportQueryInput!): Report + deleteOneSubmission(query: SubmissionQueryInput!): Submission + deleteOneSubscription(query: SubscriptionQueryInput!): Subscription + deleteOneTaxa(query: TaxaQueryInput!): Taxa + deleteOneUser(query: UserQueryInput!): User + getUser(input: GetUserInput): AppUser + insertManyCandidates(data: [CandidateInsertInput!]!): InsertManyPayload + insertManyChecklists(data: [ChecklistInsertInput!]!): InsertManyPayload + insertManyClassifications(data: [ClassificationInsertInput!]!): InsertManyPayload + insertManyDuplicates(data: [DuplicateInsertInput!]!): InsertManyPayload + insertManyEntities(data: [EntityInsertInput!]!): InsertManyPayload + insertManyHistory_incidents(data: [History_incidentInsertInput!]!): InsertManyPayload + insertManyHistory_reports(data: [History_reportInsertInput!]!): InsertManyPayload + insertManyIncidents(data: [IncidentInsertInput!]!): InsertManyPayload + insertManyNotifications(data: [NotificationInsertInput!]!): InsertManyPayload + insertManyQuickadds(data: [QuickaddInsertInput!]!): InsertManyPayload + insertManyReports(data: [ReportInsertInput!]!): InsertManyPayload + insertManySubmissions(data: [SubmissionInsertInput!]!): InsertManyPayload + insertManySubscriptions(data: [SubscriptionInsertInput!]!): InsertManyPayload + insertManyTaxas(data: [TaxaInsertInput!]!): InsertManyPayload + insertManyUsers(data: [UserInsertInput!]!): InsertManyPayload + insertOneCandidate(data: CandidateInsertInput!): Candidate + insertOneChecklist(data: ChecklistInsertInput!): Checklist + insertOneClassification(data: ClassificationInsertInput!): Classification + insertOneDuplicate(data: DuplicateInsertInput!): Duplicate + insertOneEntity(data: EntityInsertInput!): Entity + insertOneHistory_incident(data: History_incidentInsertInput!): History_incident + insertOneHistory_report(data: History_reportInsertInput!): History_report + insertOneIncident(data: IncidentInsertInput!): Incident + insertOneNotification(data: NotificationInsertInput!): Notification + insertOneQuickadd(data: QuickaddInsertInput!): Quickadd + insertOneReport(data: ReportInsertInput!): Report + insertOneSubmission(data: SubmissionInsertInput!): Submission + insertOneSubscription(data: SubscriptionInsertInput!): Subscription + insertOneTaxa(data: TaxaInsertInput!): Taxa + insertOneUser(data: UserInsertInput!): User + logIncidentHistory(input: History_incidentInsertInput): LogIncidentHistoryPayload + logReportHistory(input: History_reportInsertInput): LogReportHistoryPayload + processNotifications: Int + promoteSubmissionToReport(input: PromoteSubmissionToReportInput): PromoteSubmissionToReportPayload + replaceOneCandidate(query: CandidateQueryInput, data: CandidateInsertInput!): Candidate + replaceOneChecklist(query: ChecklistQueryInput, data: ChecklistInsertInput!): Checklist + replaceOneClassification(query: ClassificationQueryInput, data: ClassificationInsertInput!): Classification + replaceOneDuplicate(data: DuplicateInsertInput!, query: DuplicateQueryInput): Duplicate + replaceOneEntity(query: EntityQueryInput, data: EntityInsertInput!): Entity + replaceOneHistory_incident(data: History_incidentInsertInput!, query: History_incidentQueryInput): History_incident + replaceOneHistory_report(data: History_reportInsertInput!, query: History_reportQueryInput): History_report + replaceOneIncident(data: IncidentInsertInput!, query: IncidentQueryInput): Incident + replaceOneNotification(query: NotificationQueryInput, data: NotificationInsertInput!): Notification + replaceOneQuickadd(query: QuickaddQueryInput, data: QuickaddInsertInput!): Quickadd + replaceOneReport(query: ReportQueryInput, data: ReportInsertInput!): Report + replaceOneSubmission(query: SubmissionQueryInput, data: SubmissionInsertInput!): Submission + replaceOneSubscription(query: SubscriptionQueryInput, data: SubscriptionInsertInput!): Subscription + replaceOneTaxa(query: TaxaQueryInput, data: TaxaInsertInput!): Taxa + replaceOneUser(query: UserQueryInput, data: UserInsertInput!): User + updateManyCandidates(query: CandidateQueryInput, set: CandidateUpdateInput!): UpdateManyPayload + updateManyChecklists(query: ChecklistQueryInput, set: ChecklistUpdateInput!): UpdateManyPayload + updateManyClassifications(query: ClassificationQueryInput, set: ClassificationUpdateInput!): UpdateManyPayload + updateManyDuplicates(query: DuplicateQueryInput, set: DuplicateUpdateInput!): UpdateManyPayload + updateManyEntities(query: EntityQueryInput, set: EntityUpdateInput!): UpdateManyPayload + updateManyHistory_incidents(query: History_incidentQueryInput, set: History_incidentUpdateInput!): UpdateManyPayload + updateManyHistory_reports(query: History_reportQueryInput, set: History_reportUpdateInput!): UpdateManyPayload + updateManyIncidents(query: IncidentQueryInput, set: IncidentUpdateInput!): UpdateManyPayload + updateManyNotifications(query: NotificationQueryInput, set: NotificationUpdateInput!): UpdateManyPayload + updateManyQuickadds(query: QuickaddQueryInput, set: QuickaddUpdateInput!): UpdateManyPayload + updateManyReports(query: ReportQueryInput, set: ReportUpdateInput!): UpdateManyPayload + updateManySubmissions(query: SubmissionQueryInput, set: SubmissionUpdateInput!): UpdateManyPayload + updateManySubscriptions(set: SubscriptionUpdateInput!, query: SubscriptionQueryInput): UpdateManyPayload + updateManyTaxas(query: TaxaQueryInput, set: TaxaUpdateInput!): UpdateManyPayload + updateManyUsers(query: UserQueryInput, set: UserUpdateInput!): UpdateManyPayload + updateOneCandidate(query: CandidateQueryInput, set: CandidateUpdateInput!): Candidate + updateOneChecklist(query: ChecklistQueryInput, set: ChecklistUpdateInput!): Checklist + updateOneClassification(query: ClassificationQueryInput, set: ClassificationUpdateInput!): Classification + updateOneDuplicate(query: DuplicateQueryInput, set: DuplicateUpdateInput!): Duplicate + updateOneEntity(query: EntityQueryInput, set: EntityUpdateInput!): Entity + updateOneHistory_incident(query: History_incidentQueryInput, set: History_incidentUpdateInput!): History_incident + updateOneHistory_report(query: History_reportQueryInput, set: History_reportUpdateInput!): History_report + updateOneIncident(query: IncidentQueryInput, set: IncidentUpdateInput!): Incident + updateOneNotification(query: NotificationQueryInput, set: NotificationUpdateInput!): Notification + updateOneQuickadd(query: QuickaddQueryInput, set: QuickaddUpdateInput!): Quickadd + updateOneReport(query: ReportQueryInput, set: ReportUpdateInput!): Report + updateOneSubmission(query: SubmissionQueryInput, set: SubmissionUpdateInput!): Submission + updateOneSubscription(query: SubscriptionQueryInput, set: SubscriptionUpdateInput!): Subscription + updateOneTaxa(query: TaxaQueryInput, set: TaxaUpdateInput!): Taxa + updateOneUser(query: UserQueryInput, set: UserUpdateInput!): User + upsertOneCandidate(query: CandidateQueryInput, data: CandidateInsertInput!): Candidate + upsertOneChecklist(query: ChecklistQueryInput, data: ChecklistInsertInput!): Checklist + upsertOneClassification(query: ClassificationQueryInput, data: ClassificationInsertInput!): Classification + upsertOneDuplicate(data: DuplicateInsertInput!, query: DuplicateQueryInput): Duplicate + upsertOneEntity(query: EntityQueryInput, data: EntityInsertInput!): Entity + upsertOneHistory_incident(query: History_incidentQueryInput, data: History_incidentInsertInput!): History_incident + upsertOneHistory_report(query: History_reportQueryInput, data: History_reportInsertInput!): History_report + upsertOneIncident(query: IncidentQueryInput, data: IncidentInsertInput!): Incident + upsertOneNotification(query: NotificationQueryInput, data: NotificationInsertInput!): Notification + upsertOneQuickadd(query: QuickaddQueryInput, data: QuickaddInsertInput!): Quickadd + upsertOneReport(query: ReportQueryInput, data: ReportInsertInput!): Report + upsertOneSubmission(query: SubmissionQueryInput, data: SubmissionInsertInput!): Submission + upsertOneSubscription(query: SubscriptionQueryInput, data: SubscriptionInsertInput!): Subscription + upsertOneTaxa(query: TaxaQueryInput, data: TaxaInsertInput!): Taxa + upsertOneUser(query: UserQueryInput, data: UserInsertInput!): User } -type Notification { +input SubmissionInsertInput { + nlp_similar_incidents: [SubmissionNlp_similar_incidentInsertInput] + editor_similar_incidents: [Int] + source_domain: String! + text: String! + incident_editors: SubmissionIncident_editorsRelationInput + submitters: [String]! + cloudinary_id: String + editor_notes: String + plain_text: String + authors: [String]! _id: ObjectId - incident_id: Int - processed: Boolean - sentDate: DateTime - type: String - userId: User -} - -type History_incidentEmbedding { - from_reports: [Int] - vector: [Float] -} - -input TaxaField_listUpdateInput { - long_name_unset: Boolean - short_description_unset: Boolean - short_name_unset: Boolean - display_type_unset: Boolean - long_name: String - long_description: String - item_fields_unset: Boolean - permitted_values_unset: Boolean - hide_search_unset: Boolean - public_unset: Boolean - required_unset: Boolean - instant_facet_unset: Boolean - placeholder: String - weight: Int - mongo_type: String - permitted_values: [String] - short_name: String - complete_from: TaxaField_listComplete_fromUpdateInput - item_fields: TaxaField_listItem_fieldUpdateInput - default_unset: Boolean - display_type: String - mongo_type_unset: Boolean - required: Boolean - weight_unset: Boolean - complete_from_unset: Boolean - field_number: String - public: Boolean - long_description_unset: Boolean - placeholder_unset: Boolean - weight_inc: Int - default: String - hide_search: Boolean - field_number_unset: Boolean - short_description: String - instant_facet: Boolean -} - -input UpdateOneReportTranslationInput { + tags: [String]! language: String! - plain_text: String! - report_number: Int! - text: String! + editor_dissimilar_incidents: [Int] + date_published: String! + harmed_parties: SubmissionHarmed_partiesRelationInput + incident_title: String + url: String! + deployers: SubmissionDeployersRelationInput + image_url: String! + date_downloaded: String! + embedding: SubmissionEmbeddingInsertInput + user: SubmissionUserRelationInput + developers: SubmissionDevelopersRelationInput + epoch_date_modified: Int + incident_ids: [Int] + date_modified: String! + description: String + incident_date: String + quiet: Boolean + status: String + date_submitted: String! title: String! } -input ReportEmbeddingInsertInput { - vector: [Float] - from_text_hash: String -} - input ReportUserRelationInput { create: UserInsertInput link: String } -type Candidate { - _id: ObjectId - authors: [String] - classification_similarity: [CandidateClassification_similarity] - date_downloaded: String - date_published: String - dismissed: Boolean - embedding: CandidateEmbedding - epoch_date_downloaded: Int - epoch_date_published: Int - image_url: String - language: String - match: Boolean! - matching_entities: [String] - matching_harm_keywords: [String] - matching_keywords: [String] - plain_text: String - similarity: Float - source_domain: String - text: String - title: String - url: String! -} - -type Duplicate { - _id: ObjectId - duplicate_incident_number: Int - true_incident_number: Int -} - -type TaxaDummy_field { - field_number: String - short_name: String -} +scalar DateTime -input TaxaField_listItem_fieldUpdateInput { - placeholder_unset: Boolean - long_name_unset: Boolean - short_name_unset: Boolean - instant_facet: Boolean - long_description: String - long_description_unset: Boolean - public: Boolean - mongo_type: String - complete_from: TaxaField_listItem_fieldComplete_fromUpdateInput - required_unset: Boolean - weight_inc: Int - complete_from_unset: Boolean - default_unset: Boolean - mongo_type_unset: Boolean - default: String - field_number: String - public_unset: Boolean - display_type: String - long_name: String - weight_unset: Boolean - short_description_unset: Boolean - short_name: String - placeholder: String - field_number_unset: Boolean - permitted_values: [String] - display_type_unset: Boolean - required: Boolean - weight: Int - short_description: String - permitted_values_unset: Boolean - instant_facet_unset: Boolean +input ReportEmbeddingInsertInput { + from_text_hash: String + vector: [Float] } -input History_incidentInsertInput { - date: String! - description: String - AllegedDeveloperOfAISystem: [String] - editor_dissimilar_incidents: [Int] - flagged_dissimilar_incidents: [Int] - editor_notes: String - editor_similar_incidents: [Int] - AllegedDeployerOfAISystem: [String] - nlp_similar_incidents: [History_incidentNlp_similar_incidentInsertInput] - modifiedBy: String - tsne: History_incidentTsneInsertInput - AllegedHarmedOrNearlyHarmedParties: [String] - _id: ObjectId - incident_id: Int! - reports: [Int]! - epoch_date_modified: Int - embedding: History_incidentEmbeddingInsertInput - editors: [String]! - title: String! +input PromoteSubmissionToReportInput { + incident_ids: [Int] + is_incident_report: Boolean + submission_id: ObjectId } -input SubmissionInsertInput { - date_modified: String! - nlp_similar_incidents: [SubmissionNlp_similar_incidentInsertInput] - user: SubmissionUserRelationInput - language: String! - submitters: [String]! - cloudinary_id: String - date_published: String! - source_domain: String! - text: String! - deployers: SubmissionDeployersRelationInput - description: String - _id: ObjectId - image_url: String! +type PromoteSubmissionToReportPayload { incident_ids: [Int] - harmed_parties: SubmissionHarmed_partiesRelationInput - quiet: Boolean - date_submitted: String! - editor_similar_incidents: [Int] - incident_date: String - title: String! - date_downloaded: String! - embedding: SubmissionEmbeddingInsertInput - authors: [String]! - status: String - url: String! - epoch_date_modified: Int - plain_text: String - incident_editors: SubmissionIncident_editorsRelationInput - editor_notes: String - tags: [String]! - editor_dissimilar_incidents: [Int] - developers: SubmissionDevelopersRelationInput - incident_title: String + report_number: Int } -input SubscriptionUpdateInput { - entityId: SubscriptionEntityIdRelationInput - userId_unset: Boolean - _id: ObjectId - incident_id: SubscriptionIncident_idRelationInput - incident_id_unset: Boolean - entityId_unset: Boolean - type: String - type_unset: Boolean - _id_unset: Boolean - userId: SubscriptionUserIdRelationInput +input History_reportEmbeddingInsertInput { + from_text_hash: String + vector: [Float] } `; diff --git a/site/gatsby-site/server/rules.ts b/site/gatsby-site/server/rules.ts index d104fb2949..dbcd217ef7 100644 --- a/site/gatsby-site/server/rules.ts +++ b/site/gatsby-site/server/rules.ts @@ -31,7 +31,7 @@ export const isSelf = () => rule()( const collection = context.client.db('customData').collection('users'); const simpleType = getSimplifiedType(UserType); - const filter = getMongoDbFilter(simpleType, info.variableValues.filter as GraphQLFilter); + const filter = getMongoDbFilter(simpleType, args.filter as GraphQLFilter); const users = await collection.find(filter).toArray(); const { user } = context; @@ -40,7 +40,6 @@ export const isSelf = () => rule()( const meetsAdmin = user?.roles.includes('admin'); - if (meetsAdmin || meetsOwnership) { return true; @@ -55,7 +54,7 @@ export const isSubscriptionOwner = () => rule()( const collection = context.client.db('customData').collection('subscriptions'); const simpleType = getSimplifiedType(SubscriptionType); - const filter = getMongoDbFilter(simpleType, info.variableValues.filter as GraphQLFilter); + const filter = getMongoDbFilter(simpleType, args.filter as GraphQLFilter); const subscriptions = await collection.find(filter).toArray(); const { user } = context; @@ -64,7 +63,6 @@ export const isSubscriptionOwner = () => rule()( const meetsAdmin = user?.roles.includes('admin'); - if (meetsAdmin || meetsOwnership) { return true; @@ -92,4 +90,28 @@ export const hasHeaderSecret = (headerName: keyof Config) => rule()( } ) +export const notQueriesAdminData = () => rule()( + + async (parent, args, context: Context, info) => { + + const fieldNodes = info.fieldNodes; + + for (const fieldNode of fieldNodes) { + if (fieldNode.selectionSet) { + const selections = fieldNode.selectionSet.selections; + + for (const selection of selections) { + + if (selection.kind === 'Field' && selection.name.value === 'adminData') { + + return new Error('not authorized') + } + } + } + } + + return true; + } +) + export const isAdmin = isRole('admin'); diff --git a/site/gatsby-site/server/tests/atlas.spec.ts b/site/gatsby-site/server/tests/atlas.spec.ts new file mode 100644 index 0000000000..6cacffb087 --- /dev/null +++ b/site/gatsby-site/server/tests/atlas.spec.ts @@ -0,0 +1,63 @@ +import * as common from '../fields/common'; + +describe('Atlas', () => { + + afterEach(() => { + jest.clearAllMocks(); + }); + + test('Should cache the access token, and only request a new one after expiration', async () => { + + // token expiration: Monday, 18 November 2024 20:57:31 UTC + + const mockData1 = { + "access_token": "eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.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.gNEFR8fkZt20Y_vq9t9XUSczV1iQV59bng0vw2oASJo", + "user_id": "user1", + } + + global.fetch = jest.fn().mockResolvedValueOnce({ + ok: true, + status: 200, + json: () => Promise.resolve(mockData1) + }); + + jest.spyOn(global.Date, 'now').mockImplementationOnce(() => Date.parse('2024-11-18T20:27:31.000Z')); + jest.spyOn(global.Date, 'now').mockImplementationOnce(() => Date.parse('2024-11-18T20:28:31.000Z')); + + // token has not been cached yet + const result1 = await common.getAccessToken();; + // token has been cached + const result2 = await common.getAccessToken();; + + expect(global.fetch).toHaveBeenCalledTimes(1); + expect(result1).toBe(mockData1.access_token); + expect(result2).toBe(mockData1.access_token); + + + // token expiration: Monday, 18 November 2024 23:31:58 UTC + + const mockData2 = { + access_token: "eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.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.XJ0uU4ghOOF5RMIRhbQ8zjF7iJn4VxddhqatQW8gyVE", + user_id: "user1", + } + + global.fetch = jest.fn().mockResolvedValueOnce({ + ok: true, + status: 200, + json: () => Promise.resolve(mockData2) + }); + + jest.spyOn(global.Date, 'now').mockImplementationOnce(() => Date.parse('2024-11-18T20:56:31.000Z')); + jest.spyOn(global.Date, 'now').mockImplementationOnce(() => Date.parse('2024-11-18T20:57:31.000Z')); + + + // token has expired, so a new one should be requested + const result3 = await common.getAccessToken();; + // token has been cached + const result4 = await common.getAccessToken();; + + expect(global.fetch).toHaveBeenCalledTimes(1); + expect(result3).toBe(mockData2.access_token); + expect(result4).toBe(mockData2.access_token); + }); +}); diff --git a/site/gatsby-site/server/tests/emails.spec.ts b/site/gatsby-site/server/tests/emails.spec.ts new file mode 100644 index 0000000000..945682c9d6 --- /dev/null +++ b/site/gatsby-site/server/tests/emails.spec.ts @@ -0,0 +1,49 @@ +import { EmailParams } from 'mailersend'; +import * as emails from '../emails'; + +describe('Emails', () => { + + test('Should throw an assertion error if the email "to" field contains multiple recipients', async () => { + + jest.spyOn(emails, 'mailersendBulkSend'); + + const multiRecipientEmail = [new EmailParams() + .setFrom({ email: 'sender@example.com', name: 'Sender' }) + .setTo([ + { email: 'recipient1@example.com', name: 'Recipient 1' }, + { email: 'recipient2@example.com', name: 'Recipient 2' } + ]) + .setSubject('Test Email') + .setText('Test content') + .setHtml('

Test content

') + .setSendAt(Date.now())] + + await expect(emails.mailersendBulkSend(multiRecipientEmail)) + .rejects + .toThrow('Emails must have exactly one recipient'); + }); + + test('Should throw an assertion error if the email "cc" field is used', async () => { + + jest.spyOn(emails, 'mailersendBulkSend'); + + const multiRecipientEmail = [new EmailParams() + .setFrom({ email: 'sender@example.com', name: 'Sender' }) + .setTo([{ email: 'recipient1@examplee.com`', name: 'Recipient 1' }]) + .setCc([ + { email: 'recipient1@example.com', name: 'Recipient 1' }, + { email: 'recipient2@example.com', name: 'Recipient 2' } + ]) + .setSubject('Test Email') + .setText('Test content') + .setHtml('

Test content

') + .setSendAt(Date.now())] + + await expect(emails.mailersendBulkSend(multiRecipientEmail)) + .rejects + .toThrow('Should not use the \"cc\" field'); + }); + + + +}); \ No newline at end of file diff --git a/site/gatsby-site/server/tests/fixtures/classifications.ts b/site/gatsby-site/server/tests/fixtures/classifications.ts index 9cc04afb05..d54a88e55a 100644 --- a/site/gatsby-site/server/tests/fixtures/classifications.ts +++ b/site/gatsby-site/server/tests/fixtures/classifications.ts @@ -72,6 +72,7 @@ const incident1 = { "Alleged deployer of AI system": [], "Alleged developer of AI system": [], "Alleged harmed or nearly harmed parties": [], + implicated_systems: [], description: "Test description 1", title: "Test Incident 1", editors: [ diff --git a/site/gatsby-site/server/tests/fixtures/incidents.ts b/site/gatsby-site/server/tests/fixtures/incidents.ts index 0347b63dc4..24e1579775 100644 --- a/site/gatsby-site/server/tests/fixtures/incidents.ts +++ b/site/gatsby-site/server/tests/fixtures/incidents.ts @@ -154,6 +154,7 @@ const incident1: DBIncident = { "Alleged deployer of AI system": [], "Alleged developer of AI system": [], "Alleged harmed or nearly harmed parties": [], + implicated_systems: [], description: "Test description 1", title: "Test Incident 1", editors: [ @@ -202,6 +203,7 @@ const incident2: DBIncident = { "entity1" ], "Alleged harmed or nearly harmed parties": [], + implicated_systems: [], description: "Test description 2", title: "Test Incident 2", editors: [ @@ -283,6 +285,7 @@ const incident3: DBIncident = { y: -0.6 }, reports: [2, 3], + implicated_systems: ['test system 1'], editor_notes: "", }; @@ -329,6 +332,9 @@ const fixture: Fixture = { userId } } + implicated_systems { + entity_id + } `, seeds: { customData: { diff --git a/site/gatsby-site/server/tests/fixtures/incidentsHistory.ts b/site/gatsby-site/server/tests/fixtures/incidentsHistory.ts new file mode 100644 index 0000000000..d00cfec399 --- /dev/null +++ b/site/gatsby-site/server/tests/fixtures/incidentsHistory.ts @@ -0,0 +1,270 @@ +import { ObjectId } from "bson"; +import { Fixture } from "../utils"; +import { History_Incident } from "../../generated/graphql"; +import { DBIncidentHistory } from "../../interfaces"; + +const subscriber = { + _id: new ObjectId('60a7c5b7b4f5b8a6d8f9c7e6'), + first_name: 'Subscriber', + last_name: 'One', + roles: ['subscriber'], + userId: 'subscriber1', +} + +const editor1 = { + _id: new ObjectId('60a7c5b7b4f5b8a6d8f9c7e4'), + first_name: 'John', + last_name: 'Doe', + roles: ['incident_editor'], + userId: 'editor1', +}; + +const anonymous = { + _id: new ObjectId('60a7c5b7b4f5b8a6d8f9c7e5'), + first_name: 'Anon', + last_name: 'Anon', + roles: [], + userId: 'anon', +}; + +const incident1: DBIncidentHistory = { + _id: new ObjectId("60a7c5b7b4f5b8a6d8f9c7e0"), + incident_id: 1, + date: "2023-01-14T00:00:00.000Z", + "Alleged deployer of AI system": [], + "Alleged developer of AI system": [], + "Alleged harmed or nearly harmed parties": [], + implicated_systems: [], + description: "Test description 1", + title: "Test Incident 1", + editors: [ + "editor1" + ], + nlp_similar_incidents: [ + { + incident_id: 2, + similarity: 0.9 + }, + { + incident_id: 3, + similarity: 0.85 + } + ], + editor_similar_incidents: [], + editor_dissimilar_incidents: [], + flagged_dissimilar_incidents: [], + embedding: { + vector: [ + 0.1, + 0.2, + ], + from_reports: [ + 105, + 104, + ], + }, + tsne: { + x: -0.1, + y: -0.2 + }, + reports: [1, 2], + editor_notes: "Sample editor notes", +}; + +const incident2: DBIncidentHistory = { + _id: new ObjectId("60a7c5b7b4f5b8a6d8f9c7e1"), + incident_id: 2, + date: "2023-02-14T00:00:00.000Z", + "Alleged deployer of AI system": [ + "entity1", + "entity2" + ], + "Alleged developer of AI system": [ + "entity1" + ], + "Alleged harmed or nearly harmed parties": [], + implicated_systems: [], + description: "Test description 2", + title: "Test Incident 2", + editors: [ + "editor1" + ], + nlp_similar_incidents: [ + { + incident_id: 1, + similarity: 0.92 + }, + { + incident_id: 4, + similarity: 0.87 + } + ], + editor_similar_incidents: [], + editor_dissimilar_incidents: [], + flagged_dissimilar_incidents: [], + embedding: { + vector: [ + 0.6, + 0.7, + ], + from_reports: [ + 205, + 204, + ], + }, + tsne: { + x: -0.3, + y: -0.4 + }, + reports: [3], + editor_notes: "", +}; + +const incident3: DBIncidentHistory = { + _id: new ObjectId("60a7c5b7b4f5b8a6d8f9c7e2"), + incident_id: 3, + date: "2023-03-14T00:00:00.000Z", + "Alleged deployer of AI system": [ + "test deployer 5", + "test deployer 6" + ], + "Alleged developer of AI system": [ + "test developer 3" + ], + "Alleged harmed or nearly harmed parties": [ + "test harmed party 3" + ], + implicated_systems: ['test system 1'], + description: "Test description 3", + title: "Test Incident 3", + editors: [], + nlp_similar_incidents: [ + { + incident_id: 4, + similarity: 0.91 + }, + { + incident_id: 1, + similarity: 0.89 + } + ], + editor_similar_incidents: [], + editor_dissimilar_incidents: [], + flagged_dissimilar_incidents: [], + embedding: { + vector: [ + 1.1, + 1.2, + ], + from_reports: [ + 305, + 304, + ], + }, + tsne: { + x: -0.5, + y: -0.6 + }, + reports: [2, 3], + editor_notes: "", +}; + +const fixture: Fixture = { + name: 'history_incident', + query: ` + _id + AllegedDeployerOfAISystem + AllegedDeveloperOfAISystem + AllegedHarmedOrNearlyHarmedParties + date + description + editor_dissimilar_incidents + editor_notes + editors + editor_similar_incidents + embedding { + from_reports + vector + } + epoch_date_modified + flagged_dissimilar_incidents + incident_id + nlp_similar_incidents { + incident_id + similarity + } + title + tsne { + x + y + } + reports + modifiedBy + `, + seeds: { + customData: { + users: [ + subscriber, + editor1, + anonymous, + ], + }, + history: { + incidents: [ + incident1, + incident2, + incident3, + ], + }, + }, + testSingular: { + allowed: [anonymous, subscriber], + denied: [], + filter: { reports: { IN: [1] } }, + result: { + incident_id: 1, + title: 'Test Incident 1', + date: '2023-01-14T00:00:00.000Z', + reports: [1, 2] + }, + }, + testPluralFilter: { + allowed: [anonymous, subscriber], + denied: [], + filter: { + _id: { EQ: incident1._id }, + }, + result: [ + { incident_id: 1 }, + ] + }, + testPluralSort: { + allowed: [subscriber], + denied: [], + sort: { incident_id: "ASC" }, + result: [ + { incident_id: 1 }, + { incident_id: 2 }, + { incident_id: 3 }, + ], + }, + testPluralPagination: { + allowed: [subscriber], + denied: [], + pagination: { limit: 2, skip: 1 }, + sort: { incident_id: "ASC" }, + result: [ + { incident_id: 2 }, + { incident_id: 3 }, + ] + }, + testUpdateOne: null, + testUpdateMany: null, + testInsertOne: null, + testInsertMany: null, + testDeleteOne: null, + testDeleteMany: null, + testUpsertOne: null, +} + +export default fixture; \ No newline at end of file diff --git a/site/gatsby-site/server/tests/fixtures/reportsHistory.ts b/site/gatsby-site/server/tests/fixtures/reportsHistory.ts new file mode 100644 index 0000000000..b868a3f602 --- /dev/null +++ b/site/gatsby-site/server/tests/fixtures/reportsHistory.ts @@ -0,0 +1,238 @@ +import { ObjectId } from "bson"; +import { Fixture } from "../utils"; +import { History_Report, } from "../../generated/graphql"; +import { DBReportHistory } from "../../interfaces"; + +const report1: DBReportHistory = { + _id: new ObjectId('60a7c5b7b4f5b8a6d8f9c7e4'), + authors: ["Author 1", "Author 2"], + cloudinary_id: "sample_cloudinary_id", + date_downloaded: "2021-09-14T00:00:00.000Z", + date_modified: "2021-09-14T00:00:00.000Z", + date_published: "2021-09-14T00:00:00.000Z", + date_submitted: "2021-09-14T00:00:00.000Z", + description: "Sample description", + editor_notes: "Sample editor notes", + embedding: { + from_text_hash: "sample_hash", + vector: [0.1, 0.2, 0.3] + }, + epoch_date_downloaded: 1631577600, + epoch_date_modified: 1631577600, + epoch_date_published: 1631577600, + epoch_date_submitted: 1631577600, + flag: false, + image_url: "http://example.com/image.png", + inputs_outputs: ["input1", "output1"], + is_incident_report: true, + language: "en", + plain_text: "Sample plain text", + report_number: 1, + source_domain: "example.com", + submitters: ["Submitter 1", "Submitter 2"], + tags: ["tag1", "tag2"], + text: "Sample text", + title: "Sample title", + url: "http://example.com", + user: "admin", + quiet: false, + modifiedBy: "admin", +} + +const report2: DBReportHistory = { + _id: new ObjectId('60a7c5b7b4f5b8a6d8f9c7e9'), + authors: ["Author 5", "Author 6"], + cloudinary_id: "sample_cloudinary_id_3", + date_downloaded: "2022-10-14T00:00:00.000Z", + date_modified: "2022-10-14T00:00:00.000Z", + date_published: "2022-10-14T00:00:00.000Z", + date_submitted: "2022-10-14T00:00:00.000Z", + description: "Another sample description", + editor_notes: "Another sample editor notes", + embedding: { + from_text_hash: "sample_hash_3", + vector: [0.7, 0.8, 0.9] + }, + epoch_date_downloaded: 1665705600, + epoch_date_modified: 1665705600, + epoch_date_published: 1665705600, + epoch_date_submitted: 1665705600, + flag: true, + image_url: "http://example3.com/image3.png", + inputs_outputs: ["input3", "output3"], + is_incident_report: false, + language: "es", + plain_text: "Another sample plain text", + report_number: 2, + source_domain: "example3.com", + submitters: ["Submitter 5", "Submitter 6"], + tags: ["tag5", "tag6"], + text: "Another sample text", + title: "Another sample title", + url: "http://example3.com", + user: "admin", + quiet: true, + modifiedBy: "admin", +} + +const report3: DBReportHistory = { + _id: new ObjectId('60a7c5b7b4f5b8a6d8f9c7f4'), + authors: ["Author 3", "Author 4"], + cloudinary_id: "sample_cloudinary_id_2", + date_downloaded: "2021-11-14T00:00:00.000Z", + date_modified: "2021-11-14T00:00:00.000Z", + date_published: "2021-11-14T00:00:00.000Z", + date_submitted: "2021-11-14T00:00:00.000Z", + description: "Third sample description", + editor_notes: "Third sample editor notes", + embedding: { + from_text_hash: "sample_hash_2", + vector: [0.4, 0.5, 0.6] + }, + epoch_date_downloaded: 1636848000, + epoch_date_modified: 1636848000, + epoch_date_published: 1636848000, + epoch_date_submitted: 1636848000, + flag: false, + image_url: "http://example2.com/image2.png", + inputs_outputs: ["input2", "output2"], + is_incident_report: true, + language: "fr", + plain_text: "Third sample plain text", + report_number: 3, + source_domain: "example2.com", + submitters: ["Submitter 3", "Submitter 4"], + tags: ["tag3", "tag4"], + text: "Third sample text", + title: "Third sample title", + url: "http://example2.com", + user: "admin", + quiet: false, + modifiedBy: "admin", +} + +const subscriber = { + _id: new ObjectId('60a7c5b7b4f5b8a6d8f9c7e6'), + first_name: 'Subscriber', + last_name: 'One', + roles: ['subscriber'], + userId: 'subscriber1', +} + +const admin = { + _id: new ObjectId('60a7c5b7b4f5b8a6d8f9c7e5'), + first_name: 'Super', + last_name: 'Man', + roles: ['admin'], + userId: 'admin', +} + +const anonymous = { + _id: new ObjectId('60a7c5b7b4f5b8a6d8f9c7e9'), + first_name: 'Anon', + last_name: 'Anon', + roles: [], + userId: 'anon', +} + + +const fixture: Fixture = { + name: 'history_report', + query: ` + _id + authors + cloudinary_id + date_downloaded + date_modified + date_published + date_submitted + description + editor_notes + embedding { + from_text_hash + vector + } + epoch_date_downloaded + epoch_date_modified + epoch_date_published + epoch_date_submitted + flag + image_url + inputs_outputs + is_incident_report + language + plain_text + report_number + source_domain + submitters + tags + text + title + url + user + modifiedBy + quiet + `, + seeds: { + customData: { + users: [ + subscriber, + admin, + anonymous, + ], + }, + history: { + reports: [ + report1, + report2, + report3, + ] + } + }, + testSingular: { + allowed: [anonymous], + denied: [], + filter: { _id: { EQ: new ObjectId('60a7c5b7b4f5b8a6d8f9c7e4') } }, + result: { + report_number: 1, + } + }, + testPluralFilter: { + allowed: [anonymous], + denied: [], + filter: { + _id: { EQ: new ObjectId('60a7c5b7b4f5b8a6d8f9c7e4') }, + }, + result: [ + { report_number: 1 }, + ] + }, + testPluralSort: { + allowed: [anonymous], + denied: [], + sort: { report_number: "ASC" }, + result: [ + { report_number: 1 }, + { report_number: 2 }, + { report_number: 3 }, + ], + }, + testPluralPagination: { + allowed: [anonymous], + denied: [], + pagination: { limit: 2, skip: 2 }, + sort: { report_number: "ASC" }, + result: [ + { report_number: 3 }, + ] + }, + testUpdateOne: null, + testUpdateMany: null, + testInsertOne: null, + testInsertMany: null, + testDeleteOne: null, + testDeleteMany: null, + testUpsertOne: null, +} + +export default fixture; \ No newline at end of file diff --git a/site/gatsby-site/server/tests/fixtures/submissions.ts b/site/gatsby-site/server/tests/fixtures/submissions.ts index 12a73e087a..796bdd72f4 100644 --- a/site/gatsby-site/server/tests/fixtures/submissions.ts +++ b/site/gatsby-site/server/tests/fixtures/submissions.ts @@ -37,6 +37,7 @@ const submission1 = { title: "Submission 1", url: "http://example.com", user: "user1", + implicated_systems: ["entity1"] } const submission2 = { @@ -61,6 +62,7 @@ const submission2 = { title: "Submission 2", url: "http://example.com", user: "user1", + implicated_systems: ["entity1"] } const submission3 = { @@ -73,7 +75,7 @@ const submission3 = { date_submitted: "2023-11-14T00:00:00.000Z", deployers: ["entity3"], description: "Another sample description", - developers: ["entity4"], + developers: ["entity1"], harmed_parties: ["entity5"], incident_editors: ["editor3"], image_url: "https://sample_image_url_4.com", @@ -85,6 +87,7 @@ const submission3 = { title: "Submission 3", url: "http://example.com", user: "user1", + implicated_systems: ["entity1"] } @@ -164,6 +167,9 @@ const fixture: Fixture = user { userId } + implicated_systems { + entity_id + } `, seeds: { customData: { @@ -259,6 +265,7 @@ const fixture: Fixture = title: "New Submission", url: "http://example.com", user: { link: "editor1" }, + implicated_systems: { link: ["entity1"] } }, result: { _id: expect.any(String), diff --git a/site/gatsby-site/server/tests/fixtures/users.ts b/site/gatsby-site/server/tests/fixtures/users.ts index 5dd3605c39..df3ad15146 100644 --- a/site/gatsby-site/server/tests/fixtures/users.ts +++ b/site/gatsby-site/server/tests/fixtures/users.ts @@ -45,8 +45,8 @@ const fixture: Fixture = { }, }, testSingular: { - allowed: [subscriber], - denied: [anonymous], + allowed: [anonymous], + denied: [], filter: { userId: { EQ: 'user2' } }, result: { first_name: 'Jane', @@ -56,8 +56,8 @@ const fixture: Fixture = { } }, testPluralFilter: { - allowed: [admin], - denied: [subscriber, anonymous], + allowed: [admin, subscriber, anonymous], + denied: [], filter: { userId: { IN: ['user1', 'user3'] }, }, @@ -77,8 +77,8 @@ const fixture: Fixture = { ], }, testPluralPagination: { - allowed: [admin], - denied: [subscriber, anonymous], + allowed: [admin, subscriber, anonymous], + denied: [], pagination: { limit: 1, skip: 1 }, sort: { first_name: "ASC" }, result: [ @@ -91,8 +91,8 @@ const fixture: Fixture = { ], }, testPluralSort: { - allowed: [admin], - denied: [subscriber, anonymous], + allowed: [admin, subscriber, anonymous], + denied: [], sort: { first_name: "ASC" }, result: [ { @@ -118,7 +118,7 @@ const fixture: Fixture = { testInsertOne: null, testInsertMany: null, testUpdateOne: { - allowed: [admin, subscriber], + allowed: [admin, subscriber,], denied: [anonymous], filter: { userId: { EQ: 'user2' } }, update: { set: { first_name: 'Updated John' } }, diff --git a/site/gatsby-site/server/tests/history.spec.ts b/site/gatsby-site/server/tests/history.spec.ts new file mode 100644 index 0000000000..54ac3fc55c --- /dev/null +++ b/site/gatsby-site/server/tests/history.spec.ts @@ -0,0 +1,611 @@ +import { expect, jest, it } from '@jest/globals'; +import { ApolloServer } from "@apollo/server"; +import { makeRequest, seedFixture, startTestServer } from "./utils"; +import * as context from '../context'; +import { DBIncident, DBReport } from '../interfaces'; +import { IncidentFilterType, IncidentUpdateType, ReportInsertType } from '../generated/graphql'; + +describe(`History`, () => { + let server: ApolloServer, url: string; + + beforeAll(async () => { + ({ server, url } = await startTestServer()); + }); + + afterAll(async () => { + await server?.stop(); + }); + + + it(`Create history item when linking reports to incidents`, async () => { + + const incidents: DBIncident[] = [ + { + incident_id: 1, + title: 'Incident 1', + description: 'Incident 1 description', + "Alleged deployer of AI system": [], + "Alleged developer of AI system": [], + "Alleged harmed or nearly harmed parties": [], + date: new Date().toISOString(), + editors: [], + reports: [1], + implicated_systems: [], + } + ] + + const reports: DBReport[] = [ + { + report_number: 1, + title: 'Report 1', + description: 'Report 1 description', + authors: [], + cloudinary_id: 'cloudinary_id', + date_downloaded: new Date().toISOString(), + date_modified: new Date().toISOString(), + date_published: new Date().toISOString(), + date_submitted: new Date().toISOString(), + epoch_date_downloaded: 1, + epoch_date_modified: 1, + epoch_date_published: 1, + epoch_date_submitted: 1, + image_url: 'image_url', + language: 'en', + plain_text: 'plain_text', + source_domain: 'source_domain', + submitters: [], + tags: [], + text: 'text', + url: 'url', + user: 'user_id', + }, + { + report_number: 2, + title: 'Report 2', + description: 'Report 2 description', + authors: [], + cloudinary_id: 'cloudinary_id', + date_downloaded: new Date().toISOString(), + date_modified: new Date().toISOString(), + date_published: new Date().toISOString(), + date_submitted: new Date().toISOString(), + epoch_date_downloaded: 1, + epoch_date_modified: 1, + epoch_date_published: 1, + epoch_date_submitted: 1, + image_url: 'image_url', + language: 'en', + plain_text: 'plain_text', + source_domain: 'source_domain', + submitters: [], + tags: [], + text: 'text', + url: 'url', + user: 'user_id', + } + ] + + + await seedFixture({ + customData: { + users: [ + { + userId: "123", + roles: ['incident_editor'], + } + ], + }, + aiidprod: { + incidents, + reports, + }, + history: { + incidents: [], + } + }); + + const mutationData = { + query: ` + mutation($input: LinkReportsToIncidentsInput!) { + linkReportsToIncidents(input: $input) { + incident_id + reports { + report_number + } + } + } + `, + variables: { + input: { + incident_ids: [1], + report_numbers: [2] + } + } + }; + + + jest.spyOn(context, 'verifyToken').mockResolvedValue({ sub: "123" }) + + const response = await makeRequest(url, mutationData); + + expect(response.body.data).toMatchObject({ + linkReportsToIncidents: [ + { + incident_id: 1, + reports: [ + { + report_number: 1, + }, + { + report_number: 2, + }, + ] + } + ] + }) + + const history = await makeRequest(url, { + query: ` + query { + history_incidents { + _id + incident_id + title + description + reports + AllegedDeployerOfAISystem + AllegedHarmedOrNearlyHarmedParties + AllegedDeveloperOfAISystem + date + description + editor_dissimilar_incidents + editor_notes + editor_similar_incidents + editors + embedding { + from_reports + vector + } + flagged_dissimilar_incidents + tsne { + x + y + } + modifiedBy + } + } + `}); + + expect(history.body.data).toMatchObject({ + history_incidents: + [ + { + _id: expect.any(String), + reports: [1, 2], + title: incidents[0].title, + description: incidents[0].description, + date: incidents[0].date, + AllegedDeployerOfAISystem: incidents[0]["Alleged deployer of AI system"], + AllegedDeveloperOfAISystem: incidents[0]["Alleged developer of AI system"], + AllegedHarmedOrNearlyHarmedParties: incidents[0]["Alleged harmed or nearly harmed parties"], + modifiedBy: '123', + }, + ] + }); + }); + + it(`Create history item when flagging for dissimilarity`, async () => { + + const incidents: DBIncident[] = [ + { + incident_id: 1, + title: 'Incident 1', + description: 'Incident 1 description', + "Alleged deployer of AI system": [], + "Alleged developer of AI system": [], + "Alleged harmed or nearly harmed parties": [], + date: new Date().toISOString(), + editors: [], + reports: [1], + flagged_dissimilar_incidents: [], + implicated_systems: [], + } + ] + + + await seedFixture({ + customData: { + users: [ + { + userId: "123", + roles: [], + } + ], + }, + aiidprod: { + incidents, + }, + history: { + incidents: [], + }, + }); + + const mutationData = { + query: ` + mutation ($incidentId: Int!, $dissimilarIds: [Int!]) { + flagIncidentSimilarity( + incidentId: $incidentId + dissimilarIds: $dissimilarIds + ) { + incident_id + flagged_dissimilar_incidents + } + } + `, + variables: { + incidentId: 1, + dissimilarIds: [2] + } + }; + + + jest.spyOn(context, 'verifyToken').mockResolvedValue({ sub: "123" }) + + const response = await makeRequest(url, mutationData); + + expect(response.body.data).toMatchObject({ + flagIncidentSimilarity: { + incident_id: 1, + flagged_dissimilar_incidents: [2], + } + }) + + + const history = await makeRequest(url, { + query: ` + query { + history_incidents { + incident_id + flagged_dissimilar_incidents + modifiedBy + } + } + `}); + + expect(history.body.data).toMatchObject({ + history_incidents: + [ + { + incident_id: 1, + flagged_dissimilar_incidents: [2], + modifiedBy: '123', + }, + ] + }); + + }); + + it(`Create history item when editing Incident`, async () => { + + const incidents: DBIncident[] = [ + { + incident_id: 1, + title: 'Incident 1', + description: 'Incident 1 description', + "Alleged deployer of AI system": [], + "Alleged developer of AI system": [], + "Alleged harmed or nearly harmed parties": [], + date: new Date().toISOString(), + editors: [], + reports: [1], + implicated_systems: [], + } + ] + + await seedFixture({ + customData: { + users: [ + { + userId: "123", + roles: ['incident_editor'], + } + ], + }, + aiidprod: { + incidents, + }, + history: { + incidents: [], + }, + }); + + const mutationData: { variables: { filter: IncidentFilterType, update: IncidentUpdateType }, query: string } = { + query: ` + mutation($filter: IncidentFilterType!, $update: IncidentUpdateType!) { + updateOneIncident(filter: $filter, update: $update) { + incident_id + title + } + } + `, + variables: { + filter: { incident_id: { EQ: 1 } }, + update: { + set: { + title: 'Incident 1 updated', + description: 'Incident 1 description updated', + } + } + } + }; + + jest.spyOn(context, 'verifyToken').mockResolvedValue({ sub: "123" }); + + await makeRequest(url, mutationData); + + + const history = await makeRequest(url, { + query: ` + query { + history_incidents { + incident_id + title + description + modifiedBy + } + } + `}); + + expect(history.body.data).toMatchObject({ + history_incidents: + [ + { + incident_id: 1, + title: 'Incident 1 updated', + description: 'Incident 1 description updated', + modifiedBy: '123', + }, + ] + }); + }); + + it(`Create history item when inserting Incident`, async () => { + + await seedFixture({ + customData: { + users: [ + { + userId: "123", + roles: ['incident_editor'], + } + ], + }, + aiidprod: { + incidents: [], + }, + history: { + incidents: [], + }, + }); + + const mutationData = { + query: ` + mutation($data: IncidentInsertType!) { + insertOneIncident(data: $data) { + incident_id + title + } + } + `, + variables: { + data: { + incident_id: 1, + title: "Test", + date: "2024-01-01", + editor_notes: "note", + flagged_dissimilar_incidents: [] + } + } + }; + + jest.spyOn(context, 'verifyToken').mockResolvedValue({ sub: "123" }); + + + await makeRequest(url, mutationData); + + const history = await makeRequest(url, { + query: ` + query { + history_incidents { + incident_id + title + modifiedBy + } + } + `}); + + expect(history.body.data).toMatchObject({ + history_incidents: + [ + { + incident_id: 1, + title: "Test", + modifiedBy: '123', + }, + ] + }); + }); + + it(`Create history item when editing a Report`, async () => { + + const reports: DBReport[] = [ + { + report_number: 1, + title: 'Report 1', + description: 'Report 1 description', + authors: [], + cloudinary_id: 'cloudinary_id', + date_downloaded: new Date().toISOString(), + date_modified: new Date().toISOString(), + date_published: new Date().toISOString(), + date_submitted: new Date().toISOString(), + epoch_date_downloaded: 1, + epoch_date_modified: 1, + epoch_date_published: 1, + epoch_date_submitted: 1, + image_url: 'image_url', + language: 'en', + plain_text: 'plain_text', + source_domain: 'source_domain', + submitters: [], + tags: [], + text: 'text', + url: 'url', + user: '123', + } + ]; + + await seedFixture({ + customData: { + users: [ + { + userId: "123", + roles: ['incident_editor'], + } + ], + }, + aiidprod: { + reports, + }, + history: { + reports: [], + }, + }); + + const mutationData = { + query: ` + mutation($filter: ReportFilterType!, $update: ReportUpdateType!) { + updateOneReport(filter: $filter, update: $update) { + report_number + title + } + } + `, + variables: { + filter: { report_number: { EQ: 1 } }, + update: { + set: { + title: 'Report 1 updated', + } + } + } + }; + + jest.spyOn(context, 'verifyToken').mockResolvedValue({ sub: "123" }); + + await makeRequest(url, mutationData); + + const history = await makeRequest(url, { + query: ` + query { + history_reports { + report_number + title + modifiedBy + } + } + `}); + + expect(history.body.data).toMatchObject({ + history_reports: + [ + { + report_number: 1, + title: 'Report 1 updated', + modifiedBy: '123', + }, + ] + }); + }); + + it(`Create history item when inserting a Report`, async () => { + + await seedFixture({ + customData: { + users: [ + { + userId: "123", + roles: ['incident_editor'], + } + ], + }, + aiidprod: { + reports: [], + }, + history: { + reports: [], + }, + }); + + const mutationData: { query: string, variables: { data: ReportInsertType } } = { + query: ` + mutation($data: ReportInsertType!) { + insertOneReport(data: $data) { + report_number + title + } + } + `, + variables: { + data: { + report_number: 1, + title: "Test", + date_downloaded: new Date().toISOString(), + date_modified: new Date().toISOString(), + date_published: new Date().toISOString(), + date_submitted: new Date().toISOString(), + epoch_date_downloaded: 1, + epoch_date_modified: 1, + epoch_date_published: 1, + epoch_date_submitted: 1, + image_url: 'image_url', + authors: [], + text: 'text', + cloudinary_id: 'cloudinary_id', + language: 'en', + plain_text: 'plain_text', + source_domain: 'source_domain', + submitters: [], + tags: [], + url: 'url', + }, + }, + }; + + jest.spyOn(context, 'verifyToken').mockResolvedValue({ sub: "123" }); + + await makeRequest(url, mutationData); + + const history = await makeRequest(url, { + query: ` + query { + history_reports { + report_number + title + modifiedBy + } + } + `}); + + expect(history.body.data).toMatchObject({ + history_reports: + [ + { + report_number: 1, + title: "Test", + modifiedBy: '123', + }, + ] + }); + }); +}); diff --git a/site/gatsby-site/server/tests/notifications.spec.ts b/site/gatsby-site/server/tests/notifications.spec.ts index 2cc25f15dc..caf9944a42 100644 --- a/site/gatsby-site/server/tests/notifications.spec.ts +++ b/site/gatsby-site/server/tests/notifications.spec.ts @@ -3,12 +3,14 @@ import { ApolloServer } from "@apollo/server"; import { makeRequest, seedFixture, startTestServer } from "./utils"; import * as context from '../context'; import * as common from '../fields/common'; +import * as emails from '../emails'; import { DBEntity, DBIncident, DBNotification, DBReport, DBSubmission, DBSubscription, DBUser } from '../interfaces'; import config from '../config'; import { IncidentFilterType, IncidentInsertType, IncidentUpdateType, PromoteSubmissionToReportInput } from '../generated/graphql'; import { ObjectId } from 'bson'; import templates from '../emails/templates'; -import { replacePlaceholdersWithAllowedKeys } from '../fields/common'; +import { replacePlaceholdersWithAllowedKeys } from '../emails'; +import { processNotifications } from '../../src/scripts/process-notifications'; describe(`Notifications`, () => { let server: ApolloServer, url: string; @@ -23,15 +25,6 @@ describe(`Notifications`, () => { it(`processNotifications mutation - shouldn't send anything when notifications collection is empty`, async () => { - const mutationData = { - query: ` - mutation { - processNotifications - } - `, - }; - - await seedFixture({ customData: { users: [ @@ -49,12 +42,10 @@ describe(`Notifications`, () => { }); - jest.spyOn(context, 'verifyToken').mockResolvedValue({ sub: "123" }) - const sendEmailMock = jest.spyOn(common, 'sendEmail').mockResolvedValue(); + const sendEmailMock = jest.spyOn(emails, 'sendEmail').mockResolvedValue(); - const response = await makeRequest(url, mutationData, { ['PROCESS_NOTIFICATIONS_SECRET']: config.PROCESS_NOTIFICATIONS_SECRET }); + await processNotifications(); - expect(response.body.data).toMatchObject({ processNotifications: 0 }) expect(sendEmailMock).toHaveBeenCalledTimes(0); }); @@ -110,6 +101,7 @@ describe(`Notifications`, () => { date: new Date().toISOString(), editors: [], reports: [1], + implicated_systems: [], } ] @@ -153,19 +145,14 @@ describe(`Notifications`, () => { } }); - const mutationData = { - query: ` - mutation { - processNotifications - } - `, - }; - jest.spyOn(context, 'verifyToken').mockResolvedValue({ sub: "123" }) - jest.spyOn(common, 'getUsersAdminData').mockResolvedValue([{ userId: '123', email: 'test@test.com' }]); - const sendEmailMock = jest.spyOn(common, 'sendEmail').mockResolvedValue(); + jest.spyOn(common, 'getUserAdminData').mockResolvedValue({ userId: '123', email: 'test@test.com' }); + + const sendEmailMock = jest.spyOn(emails, 'sendEmail').mockResolvedValue(); + + + const result = await processNotifications(); - const response = await makeRequest(url, mutationData, { ['PROCESS_NOTIFICATIONS_SECRET']: config.PROCESS_NOTIFICATIONS_SECRET });; expect(sendEmailMock).toHaveBeenCalledTimes(1); expect(sendEmailMock).nthCalledWith(1, expect.objectContaining({ @@ -185,10 +172,12 @@ describe(`Notifications`, () => { developers: "", deployers: "", entitiesHarmed: "", + implicatedSystems: "", }, templateId: "NewIncident", })); - expect(response.body.data).toMatchObject({ processNotifications: 1 }); + + expect(result).toBe(1); }); it(`processNotifications mutation - notifications of new incident entities`, async () => { @@ -240,6 +229,7 @@ describe(`Notifications`, () => { date: new Date().toISOString(), editors: [], reports: [1], + implicated_systems: [], } ] @@ -283,19 +273,13 @@ describe(`Notifications`, () => { } }); - const mutationData = { - query: ` - mutation { - processNotifications - } - `, - }; jest.spyOn(context, 'verifyToken').mockResolvedValue({ sub: "123" }) - jest.spyOn(common, 'getUsersAdminData').mockResolvedValue([{ userId: '123', email: 'test@test.com' }]); - const sendEmailMock = jest.spyOn(common, 'sendEmail').mockResolvedValue(); + jest.spyOn(common, 'getUserAdminData').mockResolvedValue({ userId: '123', email: 'test@test.com' }); - const response = await makeRequest(url, mutationData, { ['PROCESS_NOTIFICATIONS_SECRET']: config.PROCESS_NOTIFICATIONS_SECRET });; + const sendEmailMock = jest.spyOn(emails, 'sendEmail').mockResolvedValue(); + + const result = await processNotifications(); expect(sendEmailMock).toHaveBeenCalledTimes(1); expect(sendEmailMock).nthCalledWith(1, expect.objectContaining({ @@ -317,10 +301,11 @@ describe(`Notifications`, () => { developers: "", deployers: "", entitiesHarmed: "", + implicatedSystems: "", }, templateId: "NewEntityIncident", })); - expect(response.body.data).toMatchObject({ processNotifications: 1 }); + expect(result).toBe(1); }); it(`processNotifications mutation - notifications of new incident reports`, async () => { @@ -372,6 +357,7 @@ describe(`Notifications`, () => { date: new Date().toISOString(), editors: [], reports: [1], + implicated_systems: [], } ] @@ -415,19 +401,11 @@ describe(`Notifications`, () => { } }); - const mutationData = { - query: ` - mutation { - processNotifications - } - `, - }; - jest.spyOn(context, 'verifyToken').mockResolvedValue({ sub: "123" }) - jest.spyOn(common, 'getUsersAdminData').mockResolvedValue([{ userId: '123', email: 'test@test.com' }]); - const sendEmailMock = jest.spyOn(common, 'sendEmail').mockResolvedValue(); + jest.spyOn(common, 'getUserAdminData').mockResolvedValue({ userId: '123', email: 'test@test.com' }); + const sendEmailMock = jest.spyOn(emails, 'sendEmail').mockResolvedValue(); - const response = await makeRequest(url, mutationData, { ['PROCESS_NOTIFICATIONS_SECRET']: config.PROCESS_NOTIFICATIONS_SECRET });; + const result = await processNotifications(); expect(sendEmailMock).toHaveBeenCalledTimes(1); expect(sendEmailMock).nthCalledWith(1, expect.objectContaining({ @@ -448,7 +426,7 @@ describe(`Notifications`, () => { }, templateId: "NewReportAddedToAnIncident", })); - expect(response.body.data).toMatchObject({ processNotifications: 1 }); + expect(result).toBe(1); }); it(`processNotifications mutation - notifications of incident updates`, async () => { @@ -498,6 +476,7 @@ describe(`Notifications`, () => { date: new Date().toISOString(), editors: [], reports: [1], + implicated_systems: [], } ] @@ -541,19 +520,12 @@ describe(`Notifications`, () => { } }); - const mutationData = { - query: ` - mutation { - processNotifications - } - `, - }; jest.spyOn(context, 'verifyToken').mockResolvedValue({ sub: "123" }) - jest.spyOn(common, 'getUsersAdminData').mockResolvedValue([{ userId: '123', email: 'test@test.com' }]); - const sendEmailMock = jest.spyOn(common, 'sendEmail').mockResolvedValue(); + jest.spyOn(common, 'getUserAdminData').mockResolvedValue({ userId: '123', email: 'test@test.com' }); + const sendEmailMock = jest.spyOn(emails, 'sendEmail').mockResolvedValue(); - const response = await makeRequest(url, mutationData, { ['PROCESS_NOTIFICATIONS_SECRET']: config.PROCESS_NOTIFICATIONS_SECRET });; + const result = await processNotifications(); expect(sendEmailMock).toHaveBeenCalledTimes(1); expect(sendEmailMock).nthCalledWith(1, expect.objectContaining({ @@ -574,7 +546,7 @@ describe(`Notifications`, () => { }, templateId: "IncidentUpdate", })); - expect(response.body.data).toMatchObject({ processNotifications: 1 }); + expect(result).toBe(1); }); it(`processNotifications mutation - notifications of submission promotion`, async () => { @@ -625,6 +597,7 @@ describe(`Notifications`, () => { date: new Date().toISOString(), editors: [], reports: [1], + implicated_systems: [], } ] @@ -668,19 +641,12 @@ describe(`Notifications`, () => { } }); - const mutationData = { - query: ` - mutation { - processNotifications - } - `, - }; jest.spyOn(context, 'verifyToken').mockResolvedValue({ sub: "123" }) - jest.spyOn(common, 'getUsersAdminData').mockResolvedValue([{ userId: '123', email: 'test@test.com' }]); - const sendEmailMock = jest.spyOn(common, 'sendEmail').mockResolvedValue(); + jest.spyOn(common, 'getUserAdminData').mockResolvedValue({ userId: '123', email: 'test@test.com' }); + const sendEmailMock = jest.spyOn(emails, 'sendEmail').mockResolvedValue(); - const response = await makeRequest(url, mutationData, { ['PROCESS_NOTIFICATIONS_SECRET']: config.PROCESS_NOTIFICATIONS_SECRET });; + const result = await processNotifications(); expect(sendEmailMock).toHaveBeenCalledTimes(1); expect(sendEmailMock).nthCalledWith(1, expect.objectContaining({ @@ -700,7 +666,7 @@ describe(`Notifications`, () => { }, templateId: "SubmissionApproved", })); - expect(response.body.data).toMatchObject({ processNotifications: 1 }); + expect(result).toBe(1); }); it(`Should create Incident and Entity Notifications on Incident creation`, async () => { @@ -827,6 +793,7 @@ describe(`Notifications`, () => { text: 'text', url: 'url', user: 'user_id', + implicated_systems: [], }, ] @@ -970,6 +937,7 @@ describe(`Notifications`, () => { }, reports: [1], editor_notes: "Sample editor notes", + implicated_systems: [], }, ] @@ -1165,6 +1133,7 @@ describe(`Notifications`, () => { }, reports: [], editor_notes: "Sample editor notes", + implicated_systems: [], }, ] @@ -1281,6 +1250,7 @@ describe(`Notifications`, () => { date: new Date().toISOString(), editors: [], reports: [1], + implicated_systems: ['entity-1'], } ] @@ -1332,14 +1302,15 @@ describe(`Notifications`, () => { `, }; - jest.spyOn(common, 'sendEmail').mockRestore(); - jest.spyOn(common, 'getUsersAdminData').mockResolvedValueOnce([{ userId: 'user1', email: 'test@test.com' }, { userId: 'user2', email: 'test2@test.com' }]); - - const mockMailersendBulkSend = jest.spyOn(common, 'mailersendBulkSend').mockResolvedValue(); + jest.spyOn(emails, 'sendEmail').mockRestore(); + jest.spyOn(common, 'getUserAdminData').mockResolvedValueOnce({ userId: 'user1', email: 'test@test.com' }) + jest.spyOn(common, 'getUserAdminData').mockResolvedValueOnce({ userId: 'user2', email: 'test2@test.com' }); + + const mockMailersendBulkSend = jest.spyOn(emails, 'mailersendBulkSend').mockResolvedValue(); - const response = await makeRequest(url, mutationData, { ['PROCESS_NOTIFICATIONS_SECRET']: config.PROCESS_NOTIFICATIONS_SECRET }); + const result = await processNotifications(); - expect(response.body.data).toMatchObject({ processNotifications: 1 }); + expect(result).toBe(1); expect(mockMailersendBulkSend.mock.calls[0][0][0]).toMatchObject({ from: { @@ -1362,7 +1333,8 @@ describe(`Notifications`, () => { deployers: "Entity 1", developers: "Entity 1", entitiesHarmed: "Entity 1", - }, ['developers', 'deployers', 'entitiesHarmed']), + implicatedSystems: "Entity 1", + }, ['developers', 'deployers', 'entitiesHarmed', 'implicatedSystems']), send_at: undefined, attachments: undefined, template_id: undefined, @@ -1382,6 +1354,7 @@ describe(`Notifications`, () => { email: "test@test.com", userId: "user1", siteUrl: "http://localhost:8000", + implicatedSystems: "Entity 1", }, }, ], @@ -1409,7 +1382,8 @@ describe(`Notifications`, () => { deployers: "Entity 1", developers: "Entity 1", entitiesHarmed: "Entity 1", - }, ['developers', 'deployers', 'entitiesHarmed']), + implicatedSystems: "Entity 1", + }, ['developers', 'deployers', 'entitiesHarmed', 'implicatedSystems']), send_at: undefined, attachments: undefined, template_id: undefined, @@ -1429,10 +1403,141 @@ describe(`Notifications`, () => { email: "test2@test.com", userId: "user2", siteUrl: "http://localhost:8000", + implicatedSystems: "Entity 1", }, }, ], precedence_bulk: undefined, }) }); + + it('Should throw and revert notifications status on error', async () => { + + const notifications: DBNotification[] = [ + { + processed: false, + type: 'new-incidents', + incident_id: 1, + }, + ] + + const subscriptions: DBSubscription[] = [ + { + type: 'new-incidents', + userId: '123', + }, + { + type: 'incident', + userId: '123', + incident_id: 1, + }, + { + type: 'submission-promoted', + userId: '123', + incident_id: 1, + } + ] + + const users: DBUser[] = [ + { + userId: "123", + roles: ['admin'], + } + ] + + const entities: DBEntity[] = [ + { + entity_id: 'entity-1', + name: 'Entity 1', + } + ] + + const incidents: DBIncident[] = [ + { + incident_id: 1, + title: 'Incident 1', + description: 'Incident 1 description', + "Alleged deployer of AI system": [], + "Alleged developer of AI system": [], + "Alleged harmed or nearly harmed parties": [], + date: new Date().toISOString(), + editors: [], + reports: [1], + implicated_systems: [], + } + ] + + const reports: DBReport[] = [ + { + report_number: 1, + title: 'Report 1', + description: 'Report 1 description', + authors: [], + cloudinary_id: 'cloudinary_id', + date_downloaded: new Date().toISOString(), + date_modified: new Date().toISOString(), + date_published: new Date().toISOString(), + date_submitted: new Date().toISOString(), + epoch_date_downloaded: 1, + epoch_date_modified: 1, + epoch_date_published: 1, + epoch_date_submitted: 1, + image_url: 'image_url', + language: 'en', + plain_text: 'plain_text', + source_domain: 'source_domain', + submitters: [], + tags: [], + text: 'text', + url: 'url', + user: 'user_id', + } + ] + + await seedFixture({ + customData: { + users, + notifications, + subscriptions, + }, + aiidprod: { + incidents, + entities, + reports, + } + }); + + + jest.spyOn(context, 'verifyToken').mockResolvedValue({ sub: "123" }) + jest.spyOn(common, 'getUserAdminData').mockResolvedValue({ userId: '123', email: 'test@test.com' }); + + const sendEmailMock = jest.spyOn(emails, 'sendEmail').mockImplementation(() => { + throw new Error('Failed to send email'); + }); + + + await expect(processNotifications()).rejects.toThrow('Failed to send email'); + expect(sendEmailMock).toHaveBeenCalledTimes(1); + + const result = await makeRequest(url, { + query: ` + query { + notifications { + type + incident_id + processed + entity_id + } + } + `}); + + expect(result.body.data.notifications).toMatchObject([ + { + type: 'new-incidents', + incident_id: 1, + processed: false, + entity_id: null, + }, + ]); + }); }); diff --git a/site/gatsby-site/server/tests/process-notifications.spec.ts b/site/gatsby-site/server/tests/process-notifications.spec.ts new file mode 100644 index 0000000000..349c7e1216 --- /dev/null +++ b/site/gatsby-site/server/tests/process-notifications.spec.ts @@ -0,0 +1,56 @@ +import * as notifications from '../../src/scripts/process-notifications'; +import * as reporter from '../reporter'; + +describe('Process Notifications script', () => { + + beforeEach(() => { + jest.clearAllMocks(); + }); + + afterEach(() => { + jest.restoreAllMocks(); + }); + + it('should handle errors properly, log them, and exit with code 1', async () => { + + const mockConsoleError = jest.spyOn(console, 'error').mockImplementation(); + const mockProcessExit = jest.spyOn(process, 'exit').mockImplementation(); + const mockReporterError = jest.spyOn(reporter, 'error').mockImplementation(); + const mockProcessNotifications = jest.spyOn(notifications, 'processNotifications') + + const testError = new Error('Test error'); + mockProcessNotifications.mockRejectedValueOnce(testError); + + await notifications.run(); + + expect(mockConsoleError).toHaveBeenCalledWith(testError); + expect(mockReporterError).toHaveBeenCalledWith(testError); + expect(mockProcessExit).toHaveBeenCalledWith(1); + + expect(mockConsoleError).toHaveBeenCalledTimes(1); + expect(mockReporterError).toHaveBeenCalledTimes(1); + expect(mockProcessExit).toHaveBeenCalledTimes(1); + }); + + it('should log success and exit with code 0 when processing succeeds', async () => { + + const mockConsoleLog = jest.spyOn(console, 'log').mockImplementation(); + const mockConsoleError = jest.spyOn(console, 'error').mockImplementation(); + const mockProcessExit = jest.spyOn(process, 'exit').mockImplementation(); + const mockReporterError = jest.spyOn(reporter, 'error').mockImplementation(); + const mockProcessNotifications = jest.spyOn(notifications, 'processNotifications') + + mockProcessNotifications.mockResolvedValueOnce(1); + + await notifications.run(); + + expect(mockConsoleLog).toHaveBeenCalledWith('Process Pending Notifications: Completed.'); + expect(mockProcessExit).toHaveBeenCalledWith(0); + + expect(mockConsoleError).not.toHaveBeenCalled(); + expect(mockReporterError).not.toHaveBeenCalled(); + + expect(mockConsoleLog).toHaveBeenCalledTimes(1); + expect(mockProcessExit).toHaveBeenCalledTimes(1); + }); +}); \ No newline at end of file diff --git a/site/gatsby-site/server/tests/query-fields.spec.ts b/site/gatsby-site/server/tests/query-fields.spec.ts index 74d62771fe..33b0d3cfd8 100644 --- a/site/gatsby-site/server/tests/query-fields.spec.ts +++ b/site/gatsby-site/server/tests/query-fields.spec.ts @@ -12,6 +12,8 @@ import submissionsFixture from './fixtures/submissions'; import classificationsFixture from './fixtures/classifications'; import subscriptionsFixture from './fixtures/subscriptions'; import duplicatesFixture from './fixtures/duplicates'; +import reportsHistoryFixture from './fixtures/reportsHistory'; +import incidentsHistoryFixture from './fixtures/incidentsHistory'; import * as context from '../context'; @@ -25,6 +27,8 @@ const fixtures = [ classificationsFixture, subscriptionsFixture, duplicatesFixture, + reportsHistoryFixture, + incidentsHistoryFixture, ] fixtures.forEach((collection) => { diff --git a/site/gatsby-site/server/tests/users.spec.ts b/site/gatsby-site/server/tests/users.spec.ts new file mode 100644 index 0000000000..a9f8388d96 --- /dev/null +++ b/site/gatsby-site/server/tests/users.spec.ts @@ -0,0 +1,177 @@ +import { expect, jest, it } from '@jest/globals'; +import { ApolloServer } from "@apollo/server"; +import { makeRequest, seedFixture, startTestServer } from "./utils"; +import * as context from '../context'; + +describe(`Users`, () => { + let server: ApolloServer, url: string; + + beforeAll(async () => { + ({ server, url } = await startTestServer()); + }); + + afterAll(async () => { + await server?.stop(); + }); + + it(`Should allow anonymous querying of users' public data`, async () => { + + const mutationData = { + query: ` + query { + users { + userId + roles + } + } + `, + }; + + await seedFixture({ + customData: { + users: [ + { + userId: "user1", + roles: ['subscriber'], + }, + { + userId: "user2", + roles: ['admin'], + } + ], + }, + }); + + + jest.spyOn(context, 'verifyToken').mockResolvedValue({ sub: "user1" }) + + const response = await makeRequest(url, mutationData); + + expect(response.body.data).toMatchObject({ + users: [ + { + userId: "user1", + roles: [ + "subscriber", + ], + }, + { + userId: "user2", + roles: [ + "admin", + ], + }, + ] + }); + }); + + it(`Should deny anonymous querying of users' private data`, async () => { + + const mutationData = { + query: ` + query { + users { + userId + roles + adminData { + email + } + } + } + `, + }; + + await seedFixture({ + customData: { + users: [ + { + userId: "user1", + roles: ['subscriber'], + }, + { + userId: "user2", + roles: ['admin'], + } + ], + }, + }); + + + jest.spyOn(context, 'verifyToken').mockResolvedValue({ sub: "user1" }) + + const response = await makeRequest(url, mutationData); + + expect(response.body).toMatchObject({ + data: { + users: null, + }, + errors: [ + { + message: "not authorized", + path: [ + "users", + ], + extensions: { + code: "INTERNAL_SERVER_ERROR", + }, + }, + ] + }); + }); + + it(`Should allow user with admin role querying of users' private data`, async () => { + + const mutationData = { + query: ` + query { + users { + userId + roles + adminData { + email + } + } + } + `, + }; + + await seedFixture({ + customData: { + users: [ + { + userId: "user1", + roles: ['subscriber'], + }, + { + userId: "user2", + roles: ['admin'], + } + ], + }, + }); + + + jest.spyOn(context, 'verifyToken').mockResolvedValue({ sub: "user2" }) + + const response = await makeRequest(url, mutationData); + + expect(response.body).toMatchObject({ + data: { + users: [ + { + userId: "user1", + roles: [ + "subscriber", + ], + }, + { + userId: "user2", + roles: [ + "admin", + ], + }, + ], + } + }); + }); +}); diff --git a/site/gatsby-site/server/types/incidentHistory.ts b/site/gatsby-site/server/types/incidentHistory.ts new file mode 100644 index 0000000000..77ca3c2ff2 --- /dev/null +++ b/site/gatsby-site/server/types/incidentHistory.ts @@ -0,0 +1,44 @@ +import { GraphQLInt, GraphQLList, GraphQLNonNull, GraphQLObjectType, GraphQLString } from "graphql"; +import { ObjectIdScalar } from "../scalars"; +import { IncidentEmbeddingType, NlpSimilarIncidentType, TsneType } from "./types"; + +export const IncidentHistoryType = new GraphQLObjectType({ + name: 'History_incident', + fields: { + _id: { type: ObjectIdScalar }, + AllegedDeployerOfAISystem: { type: new GraphQLList(GraphQLString), resolve: (source) => source['Alleged deployer of AI system'] }, + AllegedDeveloperOfAISystem: { type: new GraphQLList(GraphQLString), resolve: (source) => source['Alleged developer of AI system'] }, + AllegedHarmedOrNearlyHarmedParties: { type: new GraphQLList(GraphQLString), resolve: (source) => source['Alleged harmed or nearly harmed parties'] }, + implicated_systems: { type: new GraphQLList(GraphQLString) }, + date: { type: new GraphQLNonNull(GraphQLString) }, + description: { type: GraphQLString }, + modifiedBy: { type: GraphQLString }, + editor_dissimilar_incidents: { type: new GraphQLList(GraphQLInt) }, + editor_notes: { type: GraphQLString }, + editor_similar_incidents: { type: new GraphQLList(GraphQLInt) }, + editors: { type: new GraphQLNonNull(new GraphQLList(GraphQLString)) }, + embedding: { type: IncidentEmbeddingType }, + epoch_date_modified: { type: GraphQLInt }, + flagged_dissimilar_incidents: { type: new GraphQLList(GraphQLInt) }, + incident_id: { type: new GraphQLNonNull(GraphQLInt) }, + nlp_similar_incidents: { type: new GraphQLList(NlpSimilarIncidentType) }, + reports: { type: new GraphQLNonNull(new GraphQLList(GraphQLInt)) }, + title: { type: new GraphQLNonNull(GraphQLString) }, + tsne: { type: TsneType } + } +}); + +//@ts-ignore +IncidentHistoryType.getFields().reports.dependencies = ['reports']; + +//@ts-ignore +IncidentHistoryType.getFields().AllegedDeployerOfAISystem.dependencies = ['Alleged deployer of AI system']; + +//@ts-ignore +IncidentHistoryType.getFields().AllegedDeveloperOfAISystem.dependencies = ['Alleged developer of AI system']; + +//@ts-ignore +IncidentHistoryType.getFields().AllegedHarmedOrNearlyHarmedParties.dependencies = ['Alleged harmed or nearly harmed parties']; + +//@ts-ignore +IncidentHistoryType.getFields().implicated_systems.dependencies = ['implicated_systems']; diff --git a/site/gatsby-site/server/types/incidents.ts b/site/gatsby-site/server/types/incidents.ts index 3aa7f089bb..f91bb0a708 100644 --- a/site/gatsby-site/server/types/incidents.ts +++ b/site/gatsby-site/server/types/incidents.ts @@ -1,27 +1,11 @@ -import { GraphQLFloat, GraphQLInt, GraphQLList, GraphQLNonNull, GraphQLObjectType, GraphQLString } from "graphql"; +import { GraphQLInt, GraphQLList, GraphQLNonNull, GraphQLObjectType, GraphQLString } from "graphql"; import { ObjectIdScalar } from "../scalars"; import { EntityType } from "./entity"; -import { getListRelationshipConfig, getListRelationshipExtension, getListRelationshipResolver } from "../utils"; +import { getListRelationshipConfig, getListRelationshipExtension, getListRelationshipResolver, getRelationshipConfig } from "../utils"; import { UserType } from "./user"; -import { NlpSimilarIncidentType } from "./types"; +import { IncidentEmbeddingType, NlpSimilarIncidentType, TsneType } from "./types"; import { ReportType } from "./report"; -const EmbeddingType = new GraphQLObjectType({ - name: 'IncidentEmbedding', - fields: { - from_reports: { type: new GraphQLList(GraphQLInt) }, - vector: { type: new GraphQLList(GraphQLFloat) } - } -}); - -const TsneType = new GraphQLObjectType({ - name: 'IncidentTsne', - fields: { - x: { type: GraphQLFloat }, - y: { type: GraphQLFloat } - } -}); - export const IncidentType = new GraphQLObjectType({ name: 'Incident', fields: { @@ -56,14 +40,15 @@ export const IncidentType = new GraphQLObjectType({ dbMapping: 'Alleged harmed or nearly harmed parties', }, }, + implicated_systems: getListRelationshipConfig(EntityType, GraphQLString, 'implicated_systems', 'entity_id', 'entities', 'aiidprod'), editor_dissimilar_incidents: { type: new GraphQLList(GraphQLInt) }, editor_similar_incidents: { type: new GraphQLList(GraphQLInt) }, editors: getListRelationshipConfig(UserType, GraphQLString, 'editors', 'userId', 'users', 'customData'), - embedding: { type: EmbeddingType }, + embedding: { type: IncidentEmbeddingType }, flagged_dissimilar_incidents: { type: new GraphQLNonNull(new GraphQLList(GraphQLInt)) }, nlp_similar_incidents: { type: new GraphQLList(NlpSimilarIncidentType) }, reports: getListRelationshipConfig(ReportType, GraphQLInt, 'reports', 'report_number', 'reports', 'aiidprod'), - tsne: { type: TsneType } + tsne: { type: TsneType }, }, }); @@ -80,3 +65,5 @@ IncidentType.getFields().AllegedDeveloperOfAISystem.dependencies = ['Alleged dev IncidentType.getFields().AllegedHarmedOrNearlyHarmedParties.dependencies = ['Alleged harmed or nearly harmed parties']; //@ts-ignore IncidentType.getFields().editors.dependencies = ['editors']; +//@ts-ignore +IncidentType.getFields().implicated_systems.dependencies = ['implicated_systems']; diff --git a/site/gatsby-site/server/types/report.ts b/site/gatsby-site/server/types/report.ts index a1d15d6555..05811bfc0b 100644 --- a/site/gatsby-site/server/types/report.ts +++ b/site/gatsby-site/server/types/report.ts @@ -1,17 +1,10 @@ -import { GraphQLBoolean, GraphQLFloat, GraphQLInt, GraphQLList, GraphQLNonNull, GraphQLObjectType, GraphQLString } from "graphql"; +import { GraphQLBoolean, GraphQLInt, GraphQLList, GraphQLNonNull, GraphQLObjectType, GraphQLString } from "graphql"; import { DateTimeResolver } from "graphql-scalars"; import { ObjectIdScalar } from "../scalars"; import { getRelationshipConfig } from "../utils"; import { Context } from "../interfaces"; import { UserType } from "./user"; - -const EmbeddingType = new GraphQLObjectType({ - name: 'ReportEmbedding', - fields: { - from_text_hash: { type: GraphQLString }, - vector: { type: new GraphQLList(GraphQLFloat) } - } -}); +import { EmbeddingType } from "./types"; const ReportTranslationsType = new GraphQLObjectType({ name: 'ReportTranslations', diff --git a/site/gatsby-site/server/types/reportHistory.ts b/site/gatsby-site/server/types/reportHistory.ts new file mode 100644 index 0000000000..14f8ccbd1d --- /dev/null +++ b/site/gatsby-site/server/types/reportHistory.ts @@ -0,0 +1,40 @@ +import { GraphQLBoolean, GraphQLInt, GraphQLList, GraphQLNonNull, GraphQLObjectType, GraphQLString } from "graphql"; +import { DateTimeResolver } from "graphql-scalars"; +import { ObjectIdScalar } from "../scalars"; +import { EmbeddingType } from "./types"; + +export const ReportHistoryType = new GraphQLObjectType({ + name: 'History_report', + fields: { + _id: { type: ObjectIdScalar }, + authors: { type: new GraphQLList(GraphQLString) }, + cloudinary_id: { type: GraphQLString }, + date_downloaded: { type: new GraphQLNonNull(DateTimeResolver) }, + date_modified: { type: new GraphQLNonNull(DateTimeResolver) }, + date_published: { type: new GraphQLNonNull(DateTimeResolver) }, + date_submitted: { type: new GraphQLNonNull(DateTimeResolver) }, + description: { type: GraphQLString }, + modifiedBy: { type: GraphQLString }, + editor_notes: { type: GraphQLString }, + embedding: { type: EmbeddingType }, + epoch_date_downloaded: { type: GraphQLInt }, + epoch_date_modified: { type: GraphQLInt }, + epoch_date_published: { type: GraphQLInt }, + epoch_date_submitted: { type: GraphQLInt }, + flag: { type: GraphQLBoolean }, + image_url: { type: GraphQLString }, + inputs_outputs: { type: new GraphQLList(GraphQLString) }, + is_incident_report: { type: GraphQLBoolean }, + language: { type: GraphQLString }, + plain_text: { type: GraphQLString }, + report_number: { type: GraphQLInt }, + source_domain: { type: GraphQLString }, + submitters: { type: new GraphQLList(GraphQLString) }, + tags: { type: new GraphQLList(GraphQLString) }, + text: { type: GraphQLString }, + title: { type: GraphQLString }, + url: { type: GraphQLString }, + user: { type: GraphQLString }, + quiet: { type: GraphQLBoolean }, + } +}); diff --git a/site/gatsby-site/server/types/submission.ts b/site/gatsby-site/server/types/submission.ts index 1e25224a2f..5407b804e1 100644 --- a/site/gatsby-site/server/types/submission.ts +++ b/site/gatsby-site/server/types/submission.ts @@ -51,7 +51,8 @@ export const SubmissionType = new GraphQLObjectType({ url: { type: new GraphQLNonNull(GraphQLString) }, user: getRelationshipConfig(UserType, GraphQLString, 'user', 'userId', 'users', 'customData'), status: { type: GraphQLString }, - quiet: { type: GraphQLBoolean } + quiet: { type: GraphQLBoolean }, + implicated_systems: getListRelationshipConfig(EntityType, GraphQLString, 'implicated_systems', 'entity_id', 'entities', 'aiidprod'), }) }); @@ -65,3 +66,5 @@ SubmissionType.getFields().harmed_parties.dependencies = ['harmed_parties'] SubmissionType.getFields().incident_editors.dependencies = ['incident_editors'] //@ts-ignore SubmissionType.getFields().user.dependencies = ['user'] +//@ts-ignore +SubmissionType.getFields().implicated_systems.dependencies = ['implicated_systems'] diff --git a/site/gatsby-site/server/types/types.ts b/site/gatsby-site/server/types/types.ts index 6f4e8c1f1a..c31cf6980c 100644 --- a/site/gatsby-site/server/types/types.ts +++ b/site/gatsby-site/server/types/types.ts @@ -1,4 +1,4 @@ -import { GraphQLObjectType, GraphQLInt, GraphQLFloat } from 'graphql'; +import { GraphQLObjectType, GraphQLInt, GraphQLFloat, GraphQLString, GraphQLList } from 'graphql'; export const NlpSimilarIncidentType = new GraphQLObjectType({ name: 'IncidentNlp_similar_incident', // this name is to prevent merging warnings when stitching schemas @@ -7,3 +7,27 @@ export const NlpSimilarIncidentType = new GraphQLObjectType({ similarity: { type: GraphQLFloat } }) }); + +export const EmbeddingType = new GraphQLObjectType({ + name: 'ReportEmbedding', + fields: { + from_text_hash: { type: GraphQLString }, + vector: { type: new GraphQLList(GraphQLFloat) } + } +}); + +export const IncidentEmbeddingType = new GraphQLObjectType({ + name: 'IncidentEmbedding', + fields: { + from_reports: { type: new GraphQLList(GraphQLInt) }, + vector: { type: new GraphQLList(GraphQLFloat) } + } +}); + +export const TsneType = new GraphQLObjectType({ + name: 'IncidentTsne', + fields: { + x: { type: GraphQLFloat }, + y: { type: GraphQLFloat } + } +}); diff --git a/site/gatsby-site/server/types/user.ts b/site/gatsby-site/server/types/user.ts index b1b1ad12a6..3ef9fc0455 100644 --- a/site/gatsby-site/server/types/user.ts +++ b/site/gatsby-site/server/types/user.ts @@ -29,7 +29,7 @@ export const UserType = new GraphQLObjectType({ if (user!.id === source.userId || user!.roles.includes('admin')) { - response = await getUserAdminData(source.userId) + response = await getUserAdminData(source.userId) ?? {}; } return response; diff --git a/site/gatsby-site/server/utils.ts b/site/gatsby-site/server/utils.ts index 1484b2acf8..5094df8a5c 100644 --- a/site/gatsby-site/server/utils.ts +++ b/site/gatsby-site/server/utils.ts @@ -452,10 +452,12 @@ const defaultQueryFields: QueryFields[] = ['plural', 'singular']; * @param {QueryFields[]} [params.generateFields=defaultQueryFields] - An array specifying whether to generate singular, plural, or both query fields. * @returns {GraphQLFieldConfigMap} - A map of GraphQL field configurations for the generated queries. */ -export function generateQueryFields({ collectionName, databaseName = 'aiidprod', Type, generateFields = defaultQueryFields }: { collectionName: string, databaseName?: string, Type: GraphQLObjectType, generateFields?: QueryFields[] }): GraphQLFieldConfigMap { +export function generateQueryFields( + { collectionName, databaseName = 'aiidprod', Type, generateFields = defaultQueryFields, fieldName = undefined }: + { collectionName: string, databaseName?: string, Type: GraphQLObjectType, generateFields?: QueryFields[], fieldName?: string }): GraphQLFieldConfigMap { - const singularName = singularize(collectionName); - const pluralName = pluralize(collectionName); + const singularName = singularize(fieldName ?? collectionName); + const pluralName = pluralize(fieldName ?? collectionName); const fields: GraphQLFieldConfigMap = {}; diff --git a/site/gatsby-site/src/components/blog/PrismicBlogPost.js b/site/gatsby-site/src/components/blog/PrismicBlogPost.js index 6f50575f45..ee82d39c44 100644 --- a/site/gatsby-site/src/components/blog/PrismicBlogPost.js +++ b/site/gatsby-site/src/components/blog/PrismicBlogPost.js @@ -61,7 +61,7 @@ const PrismicBlogPost = ({ post, location }) => { {post.data.aitranslated && ( <> - + View Original @@ -74,7 +74,7 @@ const PrismicBlogPost = ({ post, location }) => { -
+
diff --git a/site/gatsby-site/src/components/cite/CitationFormat.js b/site/gatsby-site/src/components/cite/CitationFormat.js index c0641a9761..68ef80a6a3 100644 --- a/site/gatsby-site/src/components/cite/CitationFormat.js +++ b/site/gatsby-site/src/components/cite/CitationFormat.js @@ -49,6 +49,7 @@ function CitationFormat({ incidentReports, incident }) { nodes={incidentReports} incidentDate={incident.date} incident_id={incident.incident_id} + incidentTitle={incident.title} editors={incident.editors} /> ); diff --git a/site/gatsby-site/src/components/cite/IncidentStatsCard.js b/site/gatsby-site/src/components/cite/IncidentStatsCard.js index b0538f2e94..6866e1169d 100644 --- a/site/gatsby-site/src/components/cite/IncidentStatsCard.js +++ b/site/gatsby-site/src/components/cite/IncidentStatsCard.js @@ -59,7 +59,10 @@ const IncidentStatsCard = ({ > {stat.label}
-
+
{stats[stat.key]}
diff --git a/site/gatsby-site/src/components/cite/SimilarIncidents.js b/site/gatsby-site/src/components/cite/SimilarIncidents.js index c7b0496341..25aba17082 100644 --- a/site/gatsby-site/src/components/cite/SimilarIncidents.js +++ b/site/gatsby-site/src/components/cite/SimilarIncidents.js @@ -1,5 +1,5 @@ import React, { useCallback, useState } from 'react'; -import { formatISO, format, parse, getUnixTime } from 'date-fns'; +import { formatISO, format, parse } from 'date-fns'; import { FontAwesomeIcon } from '@fortawesome/react-fontawesome'; import { faFlag, faQuestionCircle, faEdit } from '@fortawesome/free-solid-svg-icons'; import { Image } from '../../utils/cloudinary'; @@ -9,7 +9,6 @@ import { FIND_FULL_INCIDENT, FLAG_INCIDENT_SIMILARITY } from '../../graphql/inci import md5 from 'md5'; import { useUserContext } from 'contexts/userContext'; import useToastContext, { SEVERITY } from '../../hooks/useToast'; -import { useLogIncidentHistory } from '../../hooks/useLogIncidentHistory'; import Button from '../../elements/Button'; import { useLocalization, LocalizedLink } from 'plugins/gatsby-theme-i18n'; import { Trans, useTranslation } from 'react-i18next'; @@ -36,11 +35,7 @@ const SimilarIncidentCard = ({ incident, flaggable = true, flagged, parentIncide const addToast = useToastContext(); - const { logIncidentHistory } = useLogIncidentHistory(); - const flagIncident = useCallback(async () => { - const now = new Date(); - const flagged_dissimilar_incidents = isFlagged ? parentIncident.flagged_dissimilar_incidents?.filter((e) => e != incident.incident_id) : parentIncident.flagged_dissimilar_incidents @@ -61,16 +56,6 @@ const SimilarIncidentCard = ({ incident, flaggable = true, flagged, parentIncide }, }); - await logIncidentHistory( - { - ...incidentData.incident, - flagged_dissimilar_incidents, - epoch_date_modified: getUnixTime(now), - editors: { link: editors }, - }, - user - ); - addToast({ message: isFlagged ? t(`Flag reverted.`) diff --git a/site/gatsby-site/src/components/cite/citationTypes/BibTex.js b/site/gatsby-site/src/components/cite/citationTypes/BibTex.js index b5472ed7ee..790d3222a7 100644 --- a/site/gatsby-site/src/components/cite/citationTypes/BibTex.js +++ b/site/gatsby-site/src/components/cite/citationTypes/BibTex.js @@ -7,7 +7,7 @@ import { faCopy } from '@fortawesome/free-solid-svg-icons'; import { FontAwesomeIcon } from '@fortawesome/react-fontawesome'; import { Trans, useTranslation } from 'react-i18next'; -const BibTex = ({ nodes, incidentDate, incident_id, editors }) => { +const BibTex = ({ nodes, incidentDate, incident_id, incidentTitle, editors }) => { const addToast = useToastContext(); const { t } = useTranslation(); @@ -34,14 +34,16 @@ const BibTex = ({ nodes, incidentDate, incident_id, editors }) => { editor = {${last_name}, ${first_name}}, journal = {AI Incident Database}, publisher = {Responsible AI Collaborative}, - title = {Incident Number ${incident_id}}, + title = {Incident Number ${incident_id}: ${incidentTitle}}, url = {https://incidentdatabase.ai/cite/${incident_id}}, year = {${incidentDate.substring(0, 4)}}, - urldate = {${format(new Date(), 'MMMM d, y')}}`.replace(/^ +/, '\t') + + urldate = {${format(new Date(), 'MMMM d, y')}}, + note = {Retrieved ${format(new Date(), 'MMMM yyyy')} from + \\url{https://incidentdatabase.ai/cite/${incident_id}}}`.replace(/^ +/, '\t') + '\n' + '}'; - const jsx = {bibTex}; + const jsx = {bibTex}; return ( <> diff --git a/site/gatsby-site/src/components/cite/citationTypes/Citation.js b/site/gatsby-site/src/components/cite/citationTypes/Citation.js index 5390a1849e..f81c9da5bd 100644 --- a/site/gatsby-site/src/components/cite/citationTypes/Citation.js +++ b/site/gatsby-site/src/components/cite/citationTypes/Citation.js @@ -7,7 +7,7 @@ import React, { useEffect, useState } from 'react'; import { Trans, useTranslation } from 'react-i18next'; import { getFormattedName } from '../../../utils/typography'; -const Citation = ({ nodes, incidentDate, incident_id, editors }) => { +const Citation = ({ nodes, incidentDate, incident_id, incidentTitle, editors }) => { const docs = [...nodes]; // Sort the docs according to their submit date @@ -48,7 +48,7 @@ const Citation = ({ nodes, incidentDate, incident_id, editors }) => { const editorFirstNameInitial = first_name[0] + '.'; const text = t( - '{{submitterCite}}. ({{incidentDate}}) Incident Number {{incidentId}}. in {{editorLastName}}, {{editorFirstNameInitial}} (ed.) Artificial Intelligence Incident Database. Responsible AI Collaborative. {{retrievalString}}', + '{{submitterCite}}. ({{incidentDate}}) Incident Number {{incidentId}}: {{incidentTitle}}. in {{editorLastName}}, {{editorFirstNameInitial}} (ed.) Artificial Intelligence Incident Database. Responsible AI Collaborative. {{retrievalString}}', { submitterCite, incidentDate, @@ -56,6 +56,7 @@ const Citation = ({ nodes, incidentDate, incident_id, editors }) => { editorLastName, editorFirstNameInitial, retrievalString, + incidentTitle, } ); diff --git a/site/gatsby-site/src/components/discover/Actions.js b/site/gatsby-site/src/components/discover/Actions.js index 9376bd220a..63590e2f06 100644 --- a/site/gatsby-site/src/components/discover/Actions.js +++ b/site/gatsby-site/src/components/discover/Actions.js @@ -15,14 +15,9 @@ import { useMutation, useQuery } from '@apollo/client'; import { Trans, useTranslation } from 'react-i18next'; import CustomButton from '../../elements/Button'; import { Modal } from 'flowbite-react'; -import { useUserContext } from 'contexts/userContext'; -import { useLogReportHistory } from '../../hooks/useLogReportHistory'; -import { getUnixTime } from 'date-fns'; import useLocalizePath from 'components/i18n/useLocalizePath'; function FlagModalContent({ reportNumber }) { - const { user } = useUserContext(); - const { data } = useQuery(FIND_REPORT, { variables: { filter: { report_number: { EQ: reportNumber } }, @@ -31,25 +26,13 @@ function FlagModalContent({ reportNumber }) { const [flagReportMutation, { loading }] = useMutation(FLAG_REPORT); - const { logReportHistory } = useLogReportHistory(); - const flagReport = async () => { - const now = new Date(); - await flagReportMutation({ variables: { report_number: reportNumber, input: true, }, }); - - const updated = { - flag: true, - date_modified: now, - epoch_date_modified: getUnixTime(now), - }; - - await logReportHistory(data.report, updated, user); }; const report = data?.report; diff --git a/site/gatsby-site/src/components/discover/hitTypes/Compact.js b/site/gatsby-site/src/components/discover/hitTypes/Compact.js index 6085d41c34..859855e83b 100644 --- a/site/gatsby-site/src/components/discover/hitTypes/Compact.js +++ b/site/gatsby-site/src/components/discover/hitTypes/Compact.js @@ -68,7 +68,12 @@ export default function Compact({ item, toggleFilterByIncidentId, viewType }) { /> - + {item.is_translated && ( + + )}
diff --git a/site/gatsby-site/src/components/discover/hitTypes/Details.js b/site/gatsby-site/src/components/discover/hitTypes/Details.js index 5e3dc76cf4..c4c4c7d937 100644 --- a/site/gatsby-site/src/components/discover/hitTypes/Details.js +++ b/site/gatsby-site/src/components/discover/hitTypes/Details.js @@ -53,7 +53,9 @@ export default function Details({ item, toggleFilterByIncidentId, viewType }) { - + {item.is_translated && ( + + )} - -
- +
+
+
- + {item.is_translated && ( +
+ +
+ )} +
); return ; diff --git a/site/gatsby-site/src/components/doc/PrismicDocPost.js b/site/gatsby-site/src/components/doc/PrismicDocPost.js index 3387968f99..e9ed8501b0 100644 --- a/site/gatsby-site/src/components/doc/PrismicDocPost.js +++ b/site/gatsby-site/src/components/doc/PrismicDocPost.js @@ -10,6 +10,10 @@ import Sponsors from 'components/landing/Sponsors'; import TranslationBadge from 'components/i18n/TranslationBadge'; import { extractHeaders } from 'utils/extractHeaders'; import { Heading1, Heading2 } from 'components/CustomHeaders'; +import { RichText } from 'prismic-reactjs'; +import ReactMarkdown from 'react-markdown'; +import remarkGfm from 'remark-gfm'; +import rehypeSlug from 'rehype-slug'; const PrismicDocPost = ({ doc, location }) => { let headers = []; @@ -59,8 +63,21 @@ const PrismicDocPost = ({ doc, location }) => {
{doc.data.content.map((content, index) => ( <> + {content.markdown?.richText.length > 0 && ( +
+ {(() => { + const rawMarkdown = RichText.asText(content.markdown.richText); + + return ( + + {rawMarkdown} + + ); + })()} +
+ )} {content.text && ( -
+
)} diff --git a/site/gatsby-site/src/components/entities/AllegedEntities.js b/site/gatsby-site/src/components/entities/AllegedEntities.js index 3f461030c5..b3d193c742 100644 --- a/site/gatsby-site/src/components/entities/AllegedEntities.js +++ b/site/gatsby-site/src/components/entities/AllegedEntities.js @@ -46,22 +46,41 @@ function PartiesList({ entities }) { )); } +function ImplicatedSystemsList({ entities }) { + return ( +
+ {entities.length > 0 && ( + <> + Implicated AI system{entities.length > 1 ? 's' : ''}:{' '} + {' '} + + )} +
+ ); +} + export default function AllegedEntities({ entities }) { const entitiesHarming = entities.filter((e) => e.harmedEntities.length > 0); const entitiesHarmed = entities.filter((e) => e.incidentsHarmedBy.length > 0); + const entitiesImplicatedSystems = entities.filter((e) => e.incidentsImplicatedSystems.length > 0); + if ( entitiesHarming.every( (e) => e.incidentsAsDeveloper.length == 0 && e.incidentsAsDeployer.length === 0 ) ) { return ( - - Alleged:{' '} - {' '} - developed and deployed an AI system, which harmed . - + <> + + Alleged:{' '} + {' '} + developed and deployed an AI system, which harmed{' '} + . + + + ); } @@ -74,9 +93,13 @@ export default function AllegedEntities({ entities }) { ); return ( - - Alleged: developed an AI system deployed by{' '} - , which harmed . - + <> + + Alleged: developed an AI system deployed by{' '} + , which harmed{' '} + . + + + ); } diff --git a/site/gatsby-site/src/components/entities/CommonEntities.js b/site/gatsby-site/src/components/entities/CommonEntities.js index 32fabd4028..a7a3bb8372 100644 --- a/site/gatsby-site/src/components/entities/CommonEntities.js +++ b/site/gatsby-site/src/components/entities/CommonEntities.js @@ -28,6 +28,7 @@ export default function CommonEntities() { Alleged_deployer_of_AI_system Alleged_developer_of_AI_system Alleged_harmed_or_nearly_harmed_parties + implicated_systems } } entities: allMongodbAiidprodEntities { diff --git a/site/gatsby-site/src/components/entities/EntitiesTable.js b/site/gatsby-site/src/components/entities/EntitiesTable.js index 88a3e2980c..c8474e8417 100644 --- a/site/gatsby-site/src/components/entities/EntitiesTable.js +++ b/site/gatsby-site/src/components/entities/EntitiesTable.js @@ -251,6 +251,13 @@ export default function EntitiesTable({ data, className = '', ...props }) { filter: incidentFilter, sortType: sortByCount, }, + { + title: t('As Implicated system'), + accessor: 'incidentsImplicatedSystems', + Cell: IncidentsCell, + filter: incidentFilter, + sortType: sortByCount, + }, { title: t('Related Entities'), accessor: 'relatedEntities', diff --git a/site/gatsby-site/src/components/entities/EntityCard.js b/site/gatsby-site/src/components/entities/EntityCard.js index 2f407f2ed3..523cd95d28 100644 --- a/site/gatsby-site/src/components/entities/EntityCard.js +++ b/site/gatsby-site/src/components/entities/EntityCard.js @@ -22,6 +22,10 @@ export default function EntityCard({ entity, ...props }) { header: 'Incidents involved as Deployer', key: 'incidentsAsDeployer', }, + { + header: 'Incidents implicated systems', + key: 'incidentsImplicatedSystems', + }, ]; return ( diff --git a/site/gatsby-site/src/components/forms/SubmissionWizard/StepThree.js b/site/gatsby-site/src/components/forms/SubmissionWizard/StepThree.js index 66ce40eddc..13090e0f11 100644 --- a/site/gatsby-site/src/components/forms/SubmissionWizard/StepThree.js +++ b/site/gatsby-site/src/components/forms/SubmissionWizard/StepThree.js @@ -18,6 +18,7 @@ import { faStickyNote, faPenNib, faTenge, + faGear, } from '@fortawesome/free-solid-svg-icons'; import { debounce } from 'debounce'; import UsersInputGroup from '../UsersInputGroup'; @@ -74,6 +75,17 @@ const StepThree = (props) => { message: "Harmed Parties can't be longer than 200 characters", }) .nullable(), + implicated_systems: yup + .string() + .matches(/^.{3,}$/, { + excludeEmptyString: true, + message: 'Implicated Systems must have at least 3 characters', + }) + .matches(/^.{3,200}$/, { + excludeEmptyString: true, + message: "Implicated Systems can't be longer than 200 characters", + }) + .nullable(), }); const handleSubmit = (values) => { @@ -160,6 +172,8 @@ const FormDetails = ({ const [submitCount, setSubmitCount] = useState(0); + const [entityNamesList, setEntityNamesList] = useState(entityNames); + const { isRole } = useUserContext(); const { @@ -191,6 +205,26 @@ const FormDetails = ({ } }, [submissionFailed, submissionComplete, submissionReset]); + const handleEntityChange = (values) => { + // Update entityNamesList with adding values that are not in entityNamesList + const newEntityNamesList = values + .filter((value) => { + if (!value.label) { + return !entityNamesList.includes(value); + } + return !entityNamesList.includes(value?.label); + }) + .map((entity) => { + if (entity.label) { + return entity.label; + } else { + return entity; + } + }); + + setEntityNamesList([...entityNamesList, ...newEntityNamesList]); + }; + const saveInLocalStorage = useRef( debounce((values) => { localStorage.setItem('formValues', JSON.stringify(values)); @@ -265,8 +299,8 @@ const FormDetails = ({ placeholder={t('Who employed or was responsible for the technology?')} className="mt-3" schema={schema} - options={entityNames} - handleChange={handleChange} + options={entityNamesList} + handleChange={handleEntityChange} handleBlur={handleBlur} touched={touched} values={values} @@ -282,8 +316,8 @@ const FormDetails = ({ placeholder={t('Who created or built the technology involved in the incident?')} className="mt-3" schema={schema} - options={entityNames} - handleChange={handleChange} + options={entityNamesList} + handleChange={handleEntityChange} handleBlur={handleBlur} touched={touched} values={values} @@ -299,6 +333,23 @@ const FormDetails = ({ placeholder={t('Who experienced negative impacts?')} className="mt-3" schema={schema} + options={entityNamesList} + handleChange={handleEntityChange} + handleBlur={handleBlur} + touched={touched} + values={values} + errors={errors} + /> + + + + { createEntityMutation ); + submission.implicated_systems = await processEntities( + allEntities, + values.implicated_systems, + createEntityMutation + ); + await insertSubmission({ variables: { data: submission } }); addToast({ diff --git a/site/gatsby-site/src/components/forms/TagsControl.js b/site/gatsby-site/src/components/forms/TagsControl.js index 18893712ae..45746b4a83 100644 --- a/site/gatsby-site/src/components/forms/TagsControl.js +++ b/site/gatsby-site/src/components/forms/TagsControl.js @@ -3,7 +3,14 @@ import React from 'react'; import { useTranslation } from 'react-i18next'; import Tags from './Tags'; -const TagsControl = ({ name, placeholder, className, disabled = false, options = undefined }) => { +const TagsControl = ({ + name, + placeholder, + className, + disabled = false, + options = undefined, + handleChange = undefined, +}) => { const { 0: { value }, 2: { setTouched, setValue }, @@ -20,6 +27,9 @@ const TagsControl = ({ name, placeholder, className, disabled = false, options = onChange={(value) => { setTouched(true); setValue(value); + if (handleChange && handleChange.length > 0) { + handleChange(value); + } }} {...{ name, @@ -29,6 +39,6 @@ const TagsControl = ({ name, placeholder, className, disabled = false, options = }} /> ); -} +}; export default TagsControl; diff --git a/site/gatsby-site/src/components/forms/TagsInputGroup.js b/site/gatsby-site/src/components/forms/TagsInputGroup.js index 4b11381a6b..d6b47b6875 100644 --- a/site/gatsby-site/src/components/forms/TagsInputGroup.js +++ b/site/gatsby-site/src/components/forms/TagsInputGroup.js @@ -41,7 +41,9 @@ const TagsInputGroup = ({ } data-cy={props['data-cy']} > - +
{isInvalid ? errors[name] : null} diff --git a/site/gatsby-site/src/components/incidents/IncidentEditModal.js b/site/gatsby-site/src/components/incidents/IncidentEditModal.js index 552d96e316..7f73145c65 100644 --- a/site/gatsby-site/src/components/incidents/IncidentEditModal.js +++ b/site/gatsby-site/src/components/incidents/IncidentEditModal.js @@ -11,7 +11,6 @@ import { useTranslation, Trans } from 'react-i18next'; import { processEntities } from '../../utils/entities'; import { getUnixTime } from 'date-fns'; import { useUserContext } from 'contexts/userContext'; -import { useLogIncidentHistory } from '../../hooks/useLogIncidentHistory'; export default function IncidentEditModal({ show, onClose, incidentId }) { const { user } = useUserContext(); @@ -32,8 +31,6 @@ export default function IncidentEditModal({ show, onClose, incidentId }) { const addToast = useToastContext(); - const { logIncidentHistory } = useLogIncidentHistory(); - useEffect(() => { if (incidentData?.incident) { setIncident({ ...incidentData.incident }); @@ -93,6 +90,12 @@ export default function IncidentEditModal({ show, onClose, incidentId }) { createEntityMutation ); + updated.implicated_systems = await processEntities( + entities, + values.implicated_systems, + createEntityMutation + ); + updated.epoch_date_modified = getUnixTime(new Date()); // Add the current user to the list of editors @@ -113,16 +116,6 @@ export default function IncidentEditModal({ show, onClose, incidentId }) { }, }); - await logIncidentHistory( - { - ...incident, - ...updated, - reports: incident.reports, - embedding: incident.embedding, - }, - user - ); - addToast(updateSuccessToast({ incidentId })); onClose(); @@ -135,6 +128,10 @@ export default function IncidentEditModal({ show, onClose, incidentId }) { return null; } + const entityNames = entitiesData?.entities + ? entitiesData.entities.map((node) => node.name).sort() + : []; + return ( Edit Incident {incident?.incident_id} @@ -168,13 +165,17 @@ export default function IncidentEditModal({ show, onClose, incidentId }) { incident.AllegedHarmedOrNearlyHarmedParties === null ? [] : incident.AllegedHarmedOrNearlyHarmedParties.map((item) => item.name), + implicated_systems: + incident.implicated_systems === null + ? [] + : incident.implicated_systems.map((item) => item.name), editors: incident.editors.map((editor) => editor.userId), }} > {({ isValid, isSubmitting, submitForm }) => ( <> - +
- + {item.isTranslated && ( + + )} {item.tags && item.tags.includes(RESPONSE_TAG) && (
diff --git a/site/gatsby-site/src/components/sidebar/index.js b/site/gatsby-site/src/components/sidebar/index.js index c15ba84840..09c9f97c8f 100644 --- a/site/gatsby-site/src/components/sidebar/index.js +++ b/site/gatsby-site/src/components/sidebar/index.js @@ -43,6 +43,23 @@ const Sidebar = ({ defaultCollapsed = false, location = null, setNavCollapsed }) url } path + items { + item { + document { + ... on PrismicSidebarItem { + id + data { + title + label + url { + url + } + path + } + } + } + } + } } } } @@ -58,31 +75,21 @@ const Sidebar = ({ defaultCollapsed = false, location = null, setNavCollapsed }) } `); - let sidebarItems = []; - - if (sidebar.data.items.length > 0) { - sidebarItems = sidebar.data.items.map((item) => { - item = item.item.document.data; - const itemItems = item.items.map((item) => { - item = item.item.document.data; - return { - url: item.item_url.url || item.item_path || '/', - title: item.item_title, - label: item.item_label, - items: [], - }; - }); - - return { - url: item.url.url || item.path || '/', - title: item.title, - label: item.label, - items: itemItems, - }; - }); - } else { - sidebarItems = navConfig; - } + const processItem = (item) => { + const { data } = item.item.document; + + const children = data.items?.map(processItem) || []; + + return { + url: data.url?.url || data.path || '/', + title: data.title, + label: data.label, + items: children, + }; + }; + + const sidebarItems = + sidebar.data.items.length > 0 ? sidebar.data.items.map(processItem) : navConfig; const localizePath = useLocalizePath(); @@ -98,6 +105,36 @@ const Sidebar = ({ defaultCollapsed = false, location = null, setNavCollapsed }) const [redirectTo, setRedirectTo] = useState('/'); + // Safe access to localStorage using browser check + const [expandedNodes, setExpandedNodes] = useState(() => { + if (typeof window !== 'undefined') { + const savedState = localStorage.getItem('sidebarExpandedNodes'); + + return savedState ? JSON.parse(savedState) : {}; + } + return {}; + }); + + // Toggle the expansion of a node and save to localStorage + const toggleExpand = (item) => { + setExpandedNodes((prevState) => { + const newState = { + ...prevState, + [item.url || item.path || item.title]: !prevState[item.url || item.path || item.title], + }; + + if (typeof window !== 'undefined') { + localStorage.setItem('sidebarExpandedNodes', JSON.stringify(newState)); // Save to localStorage + } + return newState; + }); + }; + + const toggleAllItems = () => { + setExpandedNodes([]); + localStorage.setItem('sidebarExpandedNodes', JSON.stringify({})); + }; + useEffect(() => { if (!manual) { collapseMenu(defaultCollapsed); @@ -123,6 +160,8 @@ const Sidebar = ({ defaultCollapsed = false, location = null, setNavCollapsed }) } } + handleResize(); + window.addEventListener('resize', handleResize); return () => { @@ -188,7 +227,9 @@ const Sidebar = ({ defaultCollapsed = false, location = null, setNavCollapsed })
diff --git a/site/gatsby-site/src/components/sidebar/tree.js b/site/gatsby-site/src/components/sidebar/tree.js index 3a1cc1fcac..04c3c6fb60 100644 --- a/site/gatsby-site/src/components/sidebar/tree.js +++ b/site/gatsby-site/src/components/sidebar/tree.js @@ -1,96 +1,78 @@ -import React, { useEffect, useState } from 'react'; -import config from '../../../config'; -import TreeNode from './treeNode'; +import React from 'react'; +import PropTypes from 'prop-types'; import { useLocation } from '@reach/router'; - -const subtreeNav = (treeRoot, currentLocation = undefined, localizePath) => { - let subs = []; - - treeRoot.forEach((item) => { - const defaultNavSetting = { - url: item.url, - label: item.label, - title: item.title, - items: subtreeNav(item.items, currentLocation, localizePath), - collapsed: false, - childVisit: false, - current: false, - }; - - let currentVisit, childVisit; - - if (typeof currentLocation == 'undefined' && typeof document != 'undefined') { - currentLocation = document.location.pathname; - } - - currentVisit = - currentLocation && - [ - localizePath({ path: item.url }), - localizePath({ path: config.gatsby.pathPrefix + item.url === '/' ? '' : item.url }), - ].includes(localizePath({ path: currentLocation })); - - childVisit = false; - defaultNavSetting.items.forEach((item) => { - if ( - !childVisit && - currentLocation && - (localizePath({ path: currentLocation }) === localizePath({ path: item.url }) || - localizePath({ path: currentLocation }) === - localizePath({ path: config.gatsby.pathPrefix + item.url })) - ) { - childVisit = true; - } - }); - - defaultNavSetting.collapsed = !currentVisit && !childVisit; - defaultNavSetting.childVisit = childVisit; - defaultNavSetting.current = currentVisit; - - subs.push(defaultNavSetting); - }); - return subs; -}; +import TreeNode from './treeNode'; const Tree = ({ - setNavCollapsed = null, + items, + isCollapsed, + setNavCollapsed, localizePath, - isCollapsed = false, additionalNodes = [], - items = [], + expandedNodes, + toggleExpand, + isMobile, }) => { const location = useLocation(); - const [navSettings, setNavSetting] = useState( - subtreeNav([...items, ...additionalNodes], location.pathname, localizePath) - ); + const [currentLocation, setCurrentLocation] = React.useState(''); - useEffect(() => { - const nodes = subtreeNav([...items, ...additionalNodes], location.pathname, localizePath); + React.useEffect(() => { + setCurrentLocation(location.pathname); + }, [location]); + + const subtreeNav = (subItems) => { + return subItems.map((item) => { + const localizedPath = localizePath({ path: item.url || item.path }); + + const currentVisit = + currentLocation && + [localizedPath, `${localizedPath}/`].includes(localizePath({ path: currentLocation })); - setNavSetting(nodes); - }, [items, additionalNodes]); + // Recursively process child items + const children = item.items?.length > 0 ? subtreeNav(item.items) : []; - const toggle = (url) => { - setNavSetting(subtreeNav(navSettings, url, localizePath)); - setNavCollapsed && setNavCollapsed(true); + // Check if any child has a "currentVisit" status to determine "childVisit" + const childVisit = children.some((child) => child.current || child.childVisit); + + return { + ...item, + items: children, + current: currentVisit, + collapsed: !currentVisit && !childVisit, + childVisit, + }; + }); }; + const processedItems = subtreeNav([...items, ...additionalNodes]); + return ( <> - {navSettings.map((cur, index) => { - return ( - - ); - })} + {processedItems.map((item) => ( + + ))} ); }; +Tree.propTypes = { + items: PropTypes.array.isRequired, + isCollapsed: PropTypes.bool, + setNavCollapsed: PropTypes.func, + localizePath: PropTypes.func, + additionalNodes: PropTypes.array, + expandedNodes: PropTypes.object.isRequired, + toggleExpand: PropTypes.func.isRequired, +}; + export default Tree; diff --git a/site/gatsby-site/src/components/sidebar/treeNode.js b/site/gatsby-site/src/components/sidebar/treeNode.js index 051e1c01dc..f64a1caae2 100644 --- a/site/gatsby-site/src/components/sidebar/treeNode.js +++ b/site/gatsby-site/src/components/sidebar/treeNode.js @@ -1,129 +1,145 @@ import React from 'react'; -import { Tooltip } from 'flowbite-react'; -import OpenedSvg from '../images/opened'; -import ClosedSvg from '../images/closed'; -import config from '../../../config'; -import Link from '../ui/Link'; +import PropTypes from 'prop-types'; import { Trans, useTranslation } from 'react-i18next'; +import { ChevronDown, ChevronRight } from 'react-feather'; +import Link from '../ui/Link'; // Assuming you have this component for internal links +import { FontAwesomeIcon } from '@fortawesome/react-fontawesome'; import { + faBuilding, + faChartPie, + faDice, faDoorOpen, - faSearch, - faPlusCircle, - faMedal, faList, + faMedal, faNewspaper, - faChartPie, + faPlusCircle, + faSearch, + faShapes, + faSquareCheck, faTable, - faBuilding, faUser, - faSquareCheck, - faShapes, - faDice, } from '@fortawesome/free-solid-svg-icons'; -import { FontAwesomeIcon } from '@fortawesome/react-fontawesome'; +import { Tooltip } from 'flowbite-react'; + +const TreeNode = ({ + item, + isCollapsed, + onClick, + setNavCollapsed, + level = 0, + className = '', + isExpanded, + toggleExpand, + expandedNodes, + isMobile, +}) => { + const hasChildren = item.items && item.items.length > 0; + + const icon = getIcon(item.label, item.current); -const TreeNode = ({ className = '', setCollapsed, navSetting, item, isCollapsed = false }) => { const calculatedClassName = `${className || ''} item ${ item.current ? 'active bg-light-orange text-white dark:bg-gray-700' : 'text-white md:text-inherit hover:bg-light-orange dark:text-white hover:text-white dark:hover:bg-gray-700' }`; - const hasChildren = item.items.length > 0; - - const click = () => { - setCollapsed(item['url']); + // Ensure keyboard accessibility + const handleKeyDown = (event) => { + if (event.key === 'Enter' || event.key === ' ') { + event.preventDefault(); + toggleExpand(item); + } }; - const icon = getIcon(item.label, item.current); - - return ( - <> -
  • - - - {!item.collapsed && hasChildren ? ( -
      - {item.items.map((item, index) => ( - - ))} -
    - ) : null} -
    • - - ); -}; - -const NodeLink = ({ - item, - isCollapsed = false, - click, - icon, - hasChildren, - calculatedClassName, - title, -}) => { const { t } = useTranslation(); return ( - - {icon && - (isCollapsed ? ( - - {icon} - - ) : ( - <>{icon} - ))} - +
    - - {item.title} - - {!config.sidebar.frontLine && item.title && hasChildren ? ( - - ) : null} - - + {/* Link to the item if it has a URL or Path */} + {item.url || item.path ? ( + + {icon && + (isCollapsed ? ( + + {icon} + + ) : ( + <>{icon} + ))} + {!isCollapsed && ( + + {item.title} + + )} + + ) : ( + + {item.title} + + )} + {/* Toggle Icon (if it has children) */} + {hasChildren && !isCollapsed && ( + toggleExpand(item)} // Only clicking the arrow expands/collapses + role="button" // Adding interactive role + tabIndex={0} // Making it focusable + onKeyDown={handleKeyDown} // Keyboard accessibility + style={{ cursor: 'pointer' }} + > + {isExpanded ? : } + + )} +
    + + {/* Render children recursively */} + {hasChildren && isExpanded && ( +
      + {item.items.map((childItem) => ( + + ))} +
    + )} + ); }; +// Helper function to get the icon function getIcon(label, current = false) { const fontAwesomeStyles = ` w-6 h-6 transition-[width] duration-500 - group-hover:text-white dark:group-hover:text-white pointer fa - ${current ? 'text-white' : 'text-gray-400 md:text-gray-600'} `; @@ -154,7 +170,7 @@ function getIcon(label, current = false) { ), user: , checklists: ( - + ), random: , spatial: ( @@ -203,4 +219,16 @@ function getIcon(label, current = false) { ); } +TreeNode.propTypes = { + item: PropTypes.object.isRequired, + isCollapsed: PropTypes.bool, + onClick: PropTypes.func, + setNavCollapsed: PropTypes.func, + level: PropTypes.number, + className: PropTypes.string, + isExpanded: PropTypes.bool, + toggleExpand: PropTypes.func, + expandedNodes: PropTypes.object, +}; + export default TreeNode; diff --git a/site/gatsby-site/src/components/submissions/SubmissionEdit.js b/site/gatsby-site/src/components/submissions/SubmissionEdit.js index 7009c60556..af3301f371 100644 --- a/site/gatsby-site/src/components/submissions/SubmissionEdit.js +++ b/site/gatsby-site/src/components/submissions/SubmissionEdit.js @@ -67,6 +67,12 @@ const SubmissionEdit = ({ id }) => { createEntityMutation ); + update.implicated_systems = await processEntities( + entities, + values.implicated_systems, + createEntityMutation + ); + if (update.nlp_similar_incidents) { update.nlp_similar_incidents = update.nlp_similar_incidents.map((nlp) => { return { ...nlp, __typename: undefined }; @@ -177,6 +183,13 @@ const SubmissionEdit = ({ id }) => { : submission.harmed_parties .filter((item) => item.name) .map((item) => item.name), + implicated_systems: + submission.implicated_systems === null || + submission.implicated_systems === undefined + ? [] + : submission.implicated_systems + .filter((item) => item.name) + .map((item) => item.name), }} > { cloudinary_id, }; - for (const key of ['authors', 'submitters', 'developers', 'deployers', 'harmed_parties']) { + for (const key of [ + 'authors', + 'submitters', + 'developers', + 'deployers', + 'harmed_parties', + 'implicated_systems', + ]) { if (newValues[key] && !Array.isArray(newValues[key])) { newValues[key] = [newValues[key]]; } @@ -423,6 +431,16 @@ const SubmissionForm = ({ onChange = null }) => { options={entityNames} {...TextInputGroupProps} /> + +
    )} diff --git a/site/gatsby-site/src/components/submissions/schemas.js b/site/gatsby-site/src/components/submissions/schemas.js index 000d961ad5..f31e11b8bb 100644 --- a/site/gatsby-site/src/components/submissions/schemas.js +++ b/site/gatsby-site/src/components/submissions/schemas.js @@ -150,6 +150,7 @@ export const incidentSchema = schema.shape({ description: description.required('*Description is required'), incident_date: incident_date.required('*Incident Date required'), incident_ids: yup.mixed(), + implicated_systems: yup.array().required('*Implicated Systems is required'), }); export const reportSchema = schema.shape({ diff --git a/site/gatsby-site/src/components/users/UserDetails.js b/site/gatsby-site/src/components/users/UserDetails.js index f74c033325..00bd9dcb21 100644 --- a/site/gatsby-site/src/components/users/UserDetails.js +++ b/site/gatsby-site/src/components/users/UserDetails.js @@ -42,7 +42,10 @@ export default function UserDetails({ userId }) { data-cy="details-table" > - + {data.user.adminData.email} - + {data.user.first_name} - + {data.user.last_name} - + { - const updatedIncident = transformIncidentData(incident, user); - - await logIncidentHistoryMutation({ variables: { input: updatedIncident } }); - }; - - return { logIncidentHistory }; -} diff --git a/site/gatsby-site/src/hooks/useLogReportHistory.js b/site/gatsby-site/src/hooks/useLogReportHistory.js deleted file mode 100644 index bd7d0af87d..0000000000 --- a/site/gatsby-site/src/hooks/useLogReportHistory.js +++ /dev/null @@ -1,28 +0,0 @@ -import { useMutation } from '@apollo/client/react/hooks'; -import { transformReportData } from '../utils/reports'; -import { LOG_REPORT_HISTORY } from '../graphql/reports'; -import { stripMarkdown } from '../utils/typography'; - -export function useLogReportHistory() { - const [logReportHistoryMutation] = useMutation(LOG_REPORT_HISTORY); - - const logReportHistory = async (report, updates, user) => { - const transformedReport = transformReportData(report, user); - - const updatedReport = { - ...transformedReport, - ...updates, - }; - - if (updates.text) { - updatedReport.plain_text = await stripMarkdown(updates.text); - } - - // Set the user as the last modifier - updatedReport.modifiedBy = user && user.providerType != 'anon-user' ? user.id : ''; - - await logReportHistoryMutation({ variables: { input: updatedReport } }); - }; - - return { logReportHistory }; -} diff --git a/site/gatsby-site/src/pages/apps/incidents.js b/site/gatsby-site/src/pages/apps/incidents.js index 1c4938a8f9..d56ee5c62b 100644 --- a/site/gatsby-site/src/pages/apps/incidents.js +++ b/site/gatsby-site/src/pages/apps/incidents.js @@ -69,6 +69,7 @@ const IncidentsPage = ({ data, ...props }) => { ), AllegedHarmedOrNearlyHarmedParties: incident.Alleged_harmed_or_nearly_harmed_parties.map((id) => entitiesHash[id]), + implicated_systems: incident.implicated_systems.map((id) => entitiesHash[id]), })); setIncidentsData(processedIncidents); @@ -278,6 +279,7 @@ export const query = graphql` Alleged_deployer_of_AI_system Alleged_developer_of_AI_system Alleged_harmed_or_nearly_harmed_parties + implicated_systems } } entities: allMongodbAiidprodEntities { diff --git a/site/gatsby-site/src/pages/apps/newsdigest.js b/site/gatsby-site/src/pages/apps/newsdigest.js index 9fa0c6f40d..68cd1922b2 100644 --- a/site/gatsby-site/src/pages/apps/newsdigest.js +++ b/site/gatsby-site/src/pages/apps/newsdigest.js @@ -18,6 +18,7 @@ import { useUserContext } from 'contexts/userContext'; import CardSkeleton from 'elements/Skeletons/Card'; import { useLocalization } from 'plugins/gatsby-theme-i18n'; import useLocalizePath from 'components/i18n/useLocalizePath'; +import useToast, { SEVERITY } from '../../hooks/useToast'; export default function NewsSearchPage() { const { t } = useTranslation(['submit']); @@ -28,8 +29,8 @@ export default function NewsSearchPage() { const { data: newsArticlesData, loading } = useQuery( gql` - query NewsArticles($query: CandidateQueryInput!) { - candidates(query: $query, limit: 9999) { + query NewsArticles($filter: CandidateFilterType!) { + candidates(filter: $filter) { title url similarity @@ -38,22 +39,25 @@ export default function NewsSearchPage() { matching_entities date_published dismissed + text } } `, { variables: { - query: { - match: true, - date_published_in: Array(14) - .fill() - .map((e, i) => - new Date( - new Date().getTime() - 86400000 * i // i days ago - ) - .toISOString() - .slice(0, 10) - ), + filter: { + match: { EQ: true }, + date_published: { + IN: Array(14) + .fill() + .map((e, i) => + new Date( + new Date().getTime() - 86400000 * i // i days ago + ) + .toISOString() + .slice(0, 10) + ), + }, }, }, } @@ -99,8 +103,8 @@ export default function NewsSearchPage() { ); const [updateCandidate] = useMutation(gql` - mutation UpdateCandidate($query: CandidateQueryInput!, $set: CandidateUpdateInput!) { - updateOneCandidate(query: $query, set: $set) { + mutation UpdateCandidate($filter: CandidateFilterType!, $update: CandidateUpdateType!) { + updateOneCandidate(filter: $filter, update: $update) { url } } @@ -273,6 +277,8 @@ function CandidateCard({ dismissed = false, existingSubmissions, }) { + const addToast = useToast(); + const { isRole } = useUserContext(); const localizePath = useLocalizePath(); @@ -345,19 +351,24 @@ function CandidateCard({ {isRole('incident_editor') && (dismissed ? ( { + onClick={async () => { setDismissedArticles((dismissedArticles) => { const updatedValue = { ...dismissedArticles }; updatedValue[newsArticle.url] = false; return updatedValue; }); - updateCandidate({ + await updateCandidate({ variables: { - query: { url: newsArticle.url }, - set: { dismissed: false }, + filter: { url: { EQ: newsArticle.url } }, + update: { set: { dismissed: false } }, }, }); + + addToast({ + message: `Restored article: ${newsArticle.url}`, + severity: SEVERITY.success, + }); }} > ) : ( { + onClick={async () => { setDismissedArticles((dismissedArticles) => { const updatedValue = { ...dismissedArticles }; updatedValue[newsArticle.url] = true; return updatedValue; }); - updateCandidate({ + await updateCandidate({ variables: { - query: { url: newsArticle.url }, - set: { dismissed: true }, + filter: { url: { EQ: newsArticle.url } }, + update: { set: { dismissed: true } }, }, }); + addToast({ + message: `Dismissed article: ${newsArticle.url}`, + severity: SEVERITY.success, + }); }} > c.code !== values.language)) { const updatedTranslation = pick(values[`translations_${code}`], ['title', 'text']); diff --git a/site/gatsby-site/src/pages/cite/history.js b/site/gatsby-site/src/pages/cite/history.js index b9f5fc2e7b..3596631558 100644 --- a/site/gatsby-site/src/pages/cite/history.js +++ b/site/gatsby-site/src/pages/cite/history.js @@ -2,7 +2,7 @@ import React, { useEffect, useState } from 'react'; import { Image } from 'utils/cloudinary'; import { fill } from '@cloudinary/base/actions/resize'; import { NumberParam, useQueryParam, withDefault } from 'use-query-params'; -import { FIND_REPORT, FIND_REPORT_HISTORY, UPDATE_REPORT } from '../../graphql/reports'; +import { FIND_REPORT_HISTORY, UPDATE_REPORT } from '../../graphql/reports'; import { FIND_USERS } from '../../graphql/users'; import { useMutation, useQuery } from '@apollo/client/react/hooks'; import { useTranslation, Trans } from 'react-i18next'; @@ -17,13 +17,12 @@ import ReportVersionViewModal from 'components/reports/ReportVersionViewModal'; import { Button, Spinner } from 'flowbite-react'; import CustomButton from 'elements/Button'; import { useUserContext } from 'contexts/userContext'; -import { useLogReportHistory } from '../../hooks/useLogReportHistory'; import useToastContext, { SEVERITY } from '../../hooks/useToast'; function IncidentHistoryPage() { const { t } = useTranslation(); - const { isRole, user } = useUserContext(); + const { isRole } = useUserContext(); const addToast = useToastContext(); @@ -39,17 +38,8 @@ function IncidentHistoryPage() { const [reportVersionDetails, setReportVersionDetails] = useState(null); - const [report, setReport] = useState(null); - const { data: usersData, loading: loadingUsers } = useQuery(FIND_USERS); - const { data: reportData, loading: loadingReport } = useQuery(FIND_REPORT, { - fetchPolicy: 'network-only', - variables: { - filter: { report_number: { EQ: reportNumber } }, - }, - }); - const { data: reportHistoryData, loading: loadingReportHistory, @@ -57,24 +47,14 @@ function IncidentHistoryPage() { } = useQuery(FIND_REPORT_HISTORY, { fetchPolicy: 'network-only', variables: { - query: { - report_number: reportNumber, + filter: { + report_number: { EQ: reportNumber }, }, }, }); const [updateReport] = useMutation(UPDATE_REPORT); - const { logReportHistory } = useLogReportHistory(); - - useEffect(() => { - if (reportData?.report) { - setReport({ ...reportData.report }); - } else { - setReport(undefined); - } - }, [reportData]); - useEffect(() => { if (reportHistoryData?.history_reports?.length > 0) { const lastVersion = reportHistoryData.history_reports[0]; @@ -113,7 +93,7 @@ function IncidentHistoryPage() { } }, [reportHistoryData, usersData]); - const loading = loadingReportHistory || loadingUsers || loadingReport; + const loading = loadingReportHistory || loadingUsers; const restoreVersion = async (version) => { if (confirm(t('Are you sure you want to restore this version?'))) { @@ -128,6 +108,7 @@ function IncidentHistoryPage() { __typename: undefined, _id: undefined, changes: undefined, + date_modified: new Date(), epoch_date_modified: getUnixTime(new Date()), editor_notes: version.editor_notes ? version.editor_notes : '', embedding: version.embedding @@ -142,8 +123,6 @@ function IncidentHistoryPage() { }, }); - await logReportHistory(report, updatedReport, user); - await refetchHistory(); addToast({ diff --git a/site/gatsby-site/src/pages/incidents/edit.js b/site/gatsby-site/src/pages/incidents/edit.js index 48d2885af2..8c673e84b5 100644 --- a/site/gatsby-site/src/pages/incidents/edit.js +++ b/site/gatsby-site/src/pages/incidents/edit.js @@ -14,7 +14,6 @@ import { processEntities } from '../../utils/entities'; import DefaultSkeleton from 'elements/Skeletons/Default'; import { getUnixTime } from 'date-fns'; import { useUserContext } from 'contexts/userContext'; -import { useLogIncidentHistory } from '../../hooks/useLogIncidentHistory'; function EditCitePage(props) { const { user } = useUserContext(); @@ -41,8 +40,6 @@ function EditCitePage(props) { const addToast = useToastContext(); - const { logIncidentHistory } = useLogIncidentHistory(); - const updateSuccessToast = ({ incidentId }) => ({ message: ( @@ -105,6 +102,12 @@ function EditCitePage(props) { createEntityMutation ); + updated.implicated_systems = await processEntities( + entities, + values.implicated_systems, + createEntityMutation + ); + updated.epoch_date_modified = getUnixTime(new Date()); // Add the current user to the list of editors @@ -125,16 +128,6 @@ function EditCitePage(props) { }, }); - await logIncidentHistory( - { - ...incident, - ...updated, - reports: incident.reports, - embedding: incident.embedding, - }, - user - ); - await updateSimilarIncidentsReciprocal( updated.editor_similar_incidents, updated.editor_dissimilar_incidents @@ -168,6 +161,10 @@ function EditCitePage(props) { ]); }; + const entityNames = entitiesData?.entities + ? entitiesData.entities.map((node) => node.name).sort() + : []; + return (
    {!loading && ( @@ -205,11 +202,23 @@ function EditCitePage(props) { ? [] : incident.AllegedHarmedOrNearlyHarmedParties.map((item) => item.name), editors: incident.editors.map((user) => user.userId), + implicated_systems: + incident.implicated_systems === null + ? [] + : incident.implicated_systems.map((item) => item.name), }} > - {({ isValid, isSubmitting, submitForm }) => ( + {({ isValid, isSubmitting, submitForm, errors }) => ( <> - + + {!isValid && ( +
    + Could not validate form:{' '} + {Object.values(errors).map((error, index) => ( +
    {error}
    + ))} +
    + )}
    )} -
    +
    {children}
    diff --git a/site/gatsby-site/src/templates/entities.js b/site/gatsby-site/src/templates/entities.js index 7e3907ca8f..dd30c26776 100644 --- a/site/gatsby-site/src/templates/entities.js +++ b/site/gatsby-site/src/templates/entities.js @@ -10,6 +10,7 @@ const incidentFields = [ 'incidentsAsDeployer', 'incidentsAsDeveloper', 'incidentsHarmedBy', + 'incidentsImplicatedSystems', ]; const entitiesFields = ['relatedEntities']; @@ -91,6 +92,7 @@ export const query = graphql` Alleged_deployer_of_AI_system Alleged_developer_of_AI_system Alleged_harmed_or_nearly_harmed_parties + implicated_systems } } } diff --git a/site/gatsby-site/src/templates/entity.js b/site/gatsby-site/src/templates/entity.js index 44aa121579..3482539dde 100644 --- a/site/gatsby-site/src/templates/entity.js +++ b/site/gatsby-site/src/templates/entity.js @@ -28,6 +28,7 @@ const incidentFields = [ 'incidentsAsDeployer', 'incidentsAsDeveloper', 'incidentsHarmedBy', + 'incidentsImplicatedSystems', ]; const EntityPage = ({ pageContext, data, ...props }) => { @@ -46,6 +47,7 @@ const EntityPage = ({ pageContext, data, ...props }) => { incidentsAsDeveloper, incidentsAsBoth, incidentsHarmedBy, + incidentsImplicatedSystems, entities: entitiesData, responses, } = data; @@ -55,6 +57,7 @@ const EntityPage = ({ pageContext, data, ...props }) => { incidentsHarmedBy: incidentsHarmedBy.nodes.sort(sortByReports), incidentsAsDeveloper: incidentsAsDeveloper.nodes.sort(sortByReports), incidentsAsDeployer: incidentsAsDeployer.nodes.sort(sortByReports), + incidentsImplicatedSystems: incidentsImplicatedSystems.nodes.sort(sortByReports), }; const sections = [ @@ -74,6 +77,10 @@ const EntityPage = ({ pageContext, data, ...props }) => { header: 'Incidents involved as Deployer', key: 'incidentsAsDeployer', }, + { + header: 'Incidents implicated systems', + key: 'incidentsImplicatedSystems', + }, ]; const incidents = sections.reduce((array, s) => array.concat(entityIncidents[s.key]), []); @@ -387,6 +394,7 @@ export const query = graphql` $incidentsAsDeveloper: [Int] $incidentsAsBoth: [Int] $incidentsHarmedBy: [Int] + $incidentsImplicatedSystems: [Int] ) { incidentsAsDeployer: allMongodbAiidprodIncidents( filter: { incident_id: { in: $incidentsAsDeployer } } @@ -402,6 +410,7 @@ export const query = graphql` Alleged_deployer_of_AI_system Alleged_developer_of_AI_system Alleged_harmed_or_nearly_harmed_parties + implicated_systems } } @@ -419,6 +428,7 @@ export const query = graphql` Alleged_deployer_of_AI_system Alleged_developer_of_AI_system Alleged_harmed_or_nearly_harmed_parties + implicated_systems } } @@ -436,6 +446,7 @@ export const query = graphql` Alleged_deployer_of_AI_system Alleged_developer_of_AI_system Alleged_harmed_or_nearly_harmed_parties + implicated_systems } } @@ -453,6 +464,25 @@ export const query = graphql` Alleged_deployer_of_AI_system Alleged_developer_of_AI_system Alleged_harmed_or_nearly_harmed_parties + implicated_systems + } + } + + incidentsImplicatedSystems: allMongodbAiidprodIncidents( + filter: { incident_id: { in: $incidentsImplicatedSystems } } + ) { + nodes { + title + description + incident_id + reports { + report_number + } + date + Alleged_deployer_of_AI_system + Alleged_developer_of_AI_system + Alleged_harmed_or_nearly_harmed_parties + implicated_systems } } diff --git a/site/gatsby-site/src/templates/post.js b/site/gatsby-site/src/templates/post.js index 53edc94357..e052ad44b9 100644 --- a/site/gatsby-site/src/templates/post.js +++ b/site/gatsby-site/src/templates/post.js @@ -59,7 +59,7 @@ export default function Post(props) { {mdx.frontmatter.aiTranslated && ( <> - + View Original @@ -72,7 +72,7 @@ export default function Post(props) {
    -
    +
    {children}
    diff --git a/site/gatsby-site/src/templates/prismicBlogPost.js b/site/gatsby-site/src/templates/prismicBlogPost.js index 7cf98468eb..b7f7dac947 100644 --- a/site/gatsby-site/src/templates/prismicBlogPost.js +++ b/site/gatsby-site/src/templates/prismicBlogPost.js @@ -1,12 +1,28 @@ import React from 'react'; import { graphql } from 'gatsby'; import PrismicBlogPost from 'components/blog/PrismicBlogPost'; +import { Link } from 'gatsby'; import HeadContent from 'components/HeadContent'; +import { Trans } from 'react-i18next'; export default function BlogPost(props) { const post = props?.data?.post; - return <>{post && }; + const originalPost = props?.data?.originalPost; + + if (post) { + return ; + } else if (originalPost) { + return ; + } else if (props?.pageContext?.originalPath) { + return ( + + View Original + + ); + } else { + return <>; + } } export const Head = (props) => { @@ -58,5 +74,29 @@ export const pageQuery = graphql` author } } + originalPost: prismicBlog(data: { language: { eq: "en" }, slug: { eq: $slug } }) { + uid + data { + metatitle + metadescription + slug + aitranslated + language + title { + text + } + content { + richText + text + html + } + image { + url + gatsbyImageData + } + date + author + } + } } `; diff --git a/site/gatsby-site/src/utils/AlgoliaUpdater.js b/site/gatsby-site/src/utils/AlgoliaUpdater.js index d9d54386ca..d044ba082c 100644 --- a/site/gatsby-site/src/utils/AlgoliaUpdater.js +++ b/site/gatsby-site/src/utils/AlgoliaUpdater.js @@ -135,6 +135,7 @@ const reportToEntry = ({ incident = null, report, classifications = [{ list: [], featured: featuredValue, flag: report.flag, is_incident_report: report.is_incident_report, + is_translated: report.is_translated, namespaces: classifications.map((c) => { return Object.keys(c.tree)[0]; }), @@ -300,8 +301,13 @@ class AlgoliaUpdater { .toArray(); const fullReports = reports.map((r) => { - let report = { ...r }; + // by default, use the report as is + let report = { + ...r, + is_translated: false, + }; + // If the report has a translation, use it if (translations.some((t) => t.report_number === r.report_number)) { const { title, plain_text } = translations.find((t) => t.report_number === r.report_number) || {}; @@ -310,6 +316,7 @@ class AlgoliaUpdater { ...r, title, plain_text, + is_translated: true, }; } return report; diff --git a/site/gatsby-site/src/utils/cite.js b/site/gatsby-site/src/utils/cite.js index 41ac62d9ec..eee6092350 100644 --- a/site/gatsby-site/src/utils/cite.js +++ b/site/gatsby-site/src/utils/cite.js @@ -104,7 +104,14 @@ export const getTranslatedReports = ({ allMongodbAiidprodReports, translations, (t) => t.report_number === r.report_number ); - return translation ? { ...r, text: translation.text, title: translation.title } : { ...r }; + return translation + ? { + ...r, + text: translation.text, + title: translation.title, + isTranslated: true, // Mark badge to display or not + } + : { ...r }; }); }; @@ -137,6 +144,7 @@ export const transformIncidentData = (incident, user) => { AllegedDeployerOfAISystem, AllegedDeveloperOfAISystem, AllegedHarmedOrNearlyHarmedParties, + implicated_systems, reports, embedding, nlp_similar_incidents, @@ -162,6 +170,12 @@ export const transformIncidentData = (incident, user) => { : AllegedHarmedOrNearlyHarmedParties.map((e) => e.entity_id); } + if (implicated_systems) { + result.implicated_systems = implicated_systems.link + ? implicated_systems.link + : implicated_systems.map((e) => e.entity_id); + } + result.reports = reports ? reports.link ? reports.link @@ -210,6 +224,7 @@ const INCIDENT_TO_COMPARE = { AllegedDeployerOfAISystem: 'Alleged Deployer of AI System', AllegedDeveloperOfAISystem: 'Alleged Developer of AI System', AllegedHarmedOrNearlyHarmedParties: 'Alleged Harmed or Nearly Harmed Parties', + implicated_systems: 'Implicated Systems', editors: 'Editors', editor_notes: 'Editor Notes', reports: 'Reports', @@ -255,6 +270,7 @@ export const getIncidentChanges = (oldVersion, newVersion, users, entities) => { 'AllegedDeployerOfAISystem', 'AllegedDeveloperOfAISystem', 'AllegedHarmedOrNearlyHarmedParties', + 'implicated_systems', ].includes(field) ) { removedLabels = removedClean.map( diff --git a/site/gatsby-site/src/utils/entities.js b/site/gatsby-site/src/utils/entities.js index 6bf1568e32..eac8a74eca 100644 --- a/site/gatsby-site/src/utils/entities.js +++ b/site/gatsby-site/src/utils/entities.js @@ -36,12 +36,18 @@ module.exports.computeEntities = ({ incidents, entities, responses }) => { property: 'Alleged_harmed_or_nearly_harmed_parties', key: 'incidentsHarmedBy', }, + { + property: 'implicated_systems', + key: 'incidentsImplicatedSystems', + }, ]; const harmingProperties = ['Alleged_deployer_of_AI_system', 'Alleged_developer_of_AI_system']; const harmedProperties = ['Alleged_harmed_or_nearly_harmed_parties']; + const otherProperties = ['implicated_systems']; + for (const incident of incidents) { const { incident_id, reports } = incident; @@ -64,6 +70,7 @@ module.exports.computeEntities = ({ incidents, entities, responses }) => { incidentsHarmedBy: [], harmedEntities: [], responses: [], + incidentsImplicatedSystems: [], }; } @@ -89,6 +96,12 @@ module.exports.computeEntities = ({ incidents, entities, responses }) => { } } + if (otherProperties.some((f) => f === field.property)) { + if (!entitiesHash[id][field.key].some((i) => i.incident_id == incident_id)) { + entitiesHash[id][field.key].push(incident_id); + } + } + for (const incidentResponse of incidentResponses) { if ( !entitiesHash[id].responses.some( @@ -123,6 +136,7 @@ module.exports.computeEntities = ({ incidents, entities, responses }) => { ...entitiesHash[id].incidentsAsBoth, ...entitiesHash[id].incidentsAsDeployer, ...entitiesHash[id].incidentsAsDeveloper, + ...entitiesHash[id].incidentsImplicatedSystems, ].includes(incident.incident_id) ) .reduce((harmed, incident) => { diff --git a/site/gatsby-site/src/utils/extractHeaders.js b/site/gatsby-site/src/utils/extractHeaders.js index 3d8fc4217a..ef3f5314a2 100644 --- a/site/gatsby-site/src/utils/extractHeaders.js +++ b/site/gatsby-site/src/utils/extractHeaders.js @@ -9,7 +9,12 @@ export const extractHeaders = (content) => { if (content.length > 0) { content.forEach((block) => { richText = block.text.richText; - headers.push(...extractHeadersFromRichText(richText)); + if (richText && richText.length > 0) { + headers.push(...extractHeadersFromRichText(richText)); + } + if (block?.markdown && block.markdown.richText.length > 0) { + headers.push(...extractHeadersFromMarkdown(block.markdown.richText[0]?.text)); + } }); } else { richText = content.richText; @@ -34,3 +39,35 @@ const extractHeadersFromRichText = (richText) => { return headers; }; + +// Extract headers from markdown and return an array of headers with id and title +export const extractHeadersFromMarkdown = (markdown) => { + if (!markdown) { + return []; + } + + const headers = []; + + const lines = markdown.split('\n'); + + lines.forEach((line) => { + const match = line.match(/^(#{1,2})\s+(.*)/); // Match `#` or `##` headers + + if (match) { + const level = match[1].length; // 1 for `#`, 2 for `##` + + const title = match[2]; + + headers.push({ + id: slugify(title, { + lower: true, + remove: /["'`?!]/g, // Removes quotes and similar characters + }), + title, + level, + }); + } + }); + + return headers; +}; diff --git a/site/gatsby-site/tailwind.config.js b/site/gatsby-site/tailwind.config.js index 009d6f2859..480a1d476b 100644 --- a/site/gatsby-site/tailwind.config.js +++ b/site/gatsby-site/tailwind.config.js @@ -32,6 +32,10 @@ let safelist = [ for (let i = 0; i < 100; i++) { safelist.push(`pl-[${2 + (i || 0) * 1}rem`); } +// TailwindCSS does not support dynamic classes like `mt-${i}` so we need to whitelist them manually +for (let i = 0; i < 100; i++) { + safelist.push(`ml-${i}`, `mr-${i}`, `mt-${i}`, `mb-${i}`, `mx-${i}`, `my-${i}`, `m-${i}`); +} const backfaceVisibility = plugin(function ({ addUtilities }) { addUtilities({ diff --git a/site/gatsby-site/typeDefs.js b/site/gatsby-site/typeDefs.js index fd6d1bc251..dad695c81e 100644 --- a/site/gatsby-site/typeDefs.js +++ b/site/gatsby-site/typeDefs.js @@ -48,6 +48,7 @@ const typeDefs = ` Alleged_developer_of_AI_system: [String] Alleged_harmed_or_nearly_harmed_parties: [String] tsne: mongodbAiidprodIncidentsTsne + implicated_systems: [String] } type mongodbAiidprodSubmissions implements Node { diff --git a/site/realm/data_sources/mongodb-atlas/history/incidents/schema.json b/site/realm/data_sources/mongodb-atlas/history/incidents/schema.json index 3dbd98f707..65d1a15bbb 100644 --- a/site/realm/data_sources/mongodb-atlas/history/incidents/schema.json +++ b/site/realm/data_sources/mongodb-atlas/history/incidents/schema.json @@ -18,6 +18,12 @@ "bsonType": "string" } }, + "implicated_systems": { + "bsonType": "array", + "items": { + "bsonType": "string" + } + }, "_id": { "bsonType": "objectId" },