Need to be implemented

Metrics

FACT: A Diagnostic for Group Fairness Trade-offs (Link)

Visualizations

Debiasing Methods

Basics:

1. Reweighing (Working on it)
2. Adversarial debiasing
3. Reject Option Based Classification
4. Optimized Pre-Processing
5. Disparate Impact Remover
6. Learning Fair Representations
7. Calibrated Equalized Odds Post-processing
8. Equalized Odds Post-processing (Working on it)
9. Meta Fair Classifier
10. Prejudice Remover

Advanced:

1. Fair Data Adaption: A practical data adaption method based on quantile preservation in causal structural equation models (Link)