Methods for Explainability that Incorporate Fairness Measures

This report describes novel explainability methods to provide explanations on fairness of the outcomes of AI algorithms, tying the produced explanations to respective fairness definitions. In particular, we will focus on how we will incorporate additional factors in the optimization objective of explainability algorithms such as counterafactuals or SHAP, in order to account for in changes of fairness definition configurations. This will also describe their initial implementations.