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Suggested Further Readings

  1. Abdul, A., Vermeulen, J., Wang, D., Lim, B. Y. B. Y., & Kankanhalli, M. (2018). Trends and trajectories for explainable, accountable and intelligible systems: An HCI research agenda. Conference on Human Factors in Computing Systems - Proceedings, 2018-April, 1–18. https://doi.org/10/gfzzgc
  2. Ananny, M., & Crawford, K. (2018). Seeing without knowing: Limitations of the transparency ideal and its application to algorithmic accountability. New Media and Society, 20(3), 973–989. https://doi.org/10/gddxrg
  3. Brown, A., Chouldechova, A., Putnam-Hornstein, E., Tobin, A., & Vaithianathan, R. (2019). Toward Algorithmic Accountability in Public Services. 1–12. https://doi.org/10/gjgz67
  4. Cardullo, P., & Kitchin, R. (2017). Being a ‘citizen’ in the smart city: Up and down the scaffold of smart citizen participation. 1–24. https://doi.org/10/gmv79q
  5. Crawford, K. (2016). Can an Algorithm be Agonistic? Ten Scenes from Life in Calculated Publics. Science, Technology, & Human Values, 41(1), 77–92. https://doi.org/10/gddv8j
  6. Dourish, P. (2016). Algorithms and their others: Algorithmic culture in context. Big Data and Society, 3(2), 1–11. https://doi.org/10/gcdx9q
  7. Hildebrandt, M. (2017). Privacy As Protection of the Incomputable Self: Agonistic Machine Learning. SSRN Electronic Journal, 1–33. https://doi.org/10/gmv79g
  8. Hirsch, T., Merced, K., Narayanan, S., Imel, Z. E. Z. E., & Atkins, D. C. D. C. (2017). Designing contestability: Interaction design, machine learning, and mental health. DIS 2017 - Proceedings of the 2017 ACM Conference on Designing Interactive Systems, 95–99. https://doi.org/10/gddxqb
  9. Jewell, M. (2018). Contesting the decision: Living in (and living with) the smart city. International Review of Law, Computers and Technology. https://doi.org/10/gk48xb
  10. Jobin, A., Ienca, M., & Vayena, E. (2019). The global landscape of AI ethics guidelines. Nature Machine Intelligence, 1(9), 389–399. https://doi.org/10/gf73q2
  11. Mittelstadt, B. D. B. D., Allo, P., Taddeo, M., Wachter, S., & Floridi, L. (2016). The ethics of algorithms: Mapping the debate. Big Data and Society, 3(2), 205395171667967. https://doi.org/10/gcdx92
  12. Morley, J., Floridi, L., Kinsey, L., & Elhalal, A. (2019). From What to How. An Overview of AI Ethics Tools, Methods and Research to Translate Principles into Practices. Science and Engineering Ethics, 0123456789. https://doi.org/10/ghc8ng
  13. Sarra, C. (2020). Put Dialectics into the Machine: Protection against Automatic-decision-making through a Deeper Understanding of Contestability by Design. Global Jurist, 20(3), 20200003. https://doi.org/10/gj7sk6
  14. Shneiderman, B. (2020). Bridging the Gap Between Ethics and Practice: Guidelines for Reliable, Safe, and Trustworthy Human-centered AI Systems. ACM Transactions on Interactive Intelligent Systems, 10(4), 1–31. https://doi.org/10/gh4rnz
  15. Sio, F. S. de, & Hoven, J. van den. (2018). Meaningful human control over autonomous systems: A philosophical account. Frontiers Robotics AI, 5(FEB), 1–14. https://doi.org/10/gf597h
  16. Sloane, M., Moss, E., Awomolo, O., & Forlano, L. (2020). Participation is not a Design Fix for Machine Learning. ArXiv:2007.02423 [Cs]. http://arxiv.org/abs/2007.02423
  17. Tsamados, A., Aggarwal, N., Cowls, J., Morley, J., Roberts, H., Taddeo, M., & Floridi, L. (2021). The ethics of algorithms: Key problems and solutions. AI & SOCIETY. https://doi.org/10/gkx6tg
  18. Vaccaro, K., Karahalios, K., Mulligan, D. K., Kluttz, D., & Hirsch, T. (2019). Contestability in Algorithmic Systems. Conference Companion Publication of the 2019 on Computer Supported Cooperative Work and Social Computing, 523–527. https://doi.org/10/gjr4r5
  19. Vaccaro, K., Sandvig, C., & Karahalios, K. (2020). “At the End of the Day Facebook Does What It Wants”: How Users Experience Contesting Algorithmic Content Moderation. Proceedings of the ACM on Human-Computer Interaction, 4(CSCW2), 1–22. https://doi.org/10/gj7sk7