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Metrics and methods for a systematic comparison of fairness-aware machine learning algorithms
We found that fairness-unaware algorithms typically fail to produce adequately fair models and that the simplest algorithms are not necessarily the fairest ones.
Fairness by Explicability and Adversarial SHAP Learning
To satisfy this definition, we develop a framework for mitigating model bias using regularizations constructed from the SHAP values of an adversarial surrogate model.
Counterfactual fairness: removing direct effects through regularization
We develop regularizations to tackle classical fairness measures and present a causal regularization that satisfies our new fairness definition by removing the impact of unprivileged group variables on the model outcomes as measured by the CDE.
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