Fairness and Robustness of Contrasting Explanations
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Fairness and explainability are two important and closely related requirements of decision making systems. While ensuring and evaluating fairness as well as explainability of decision masking systems has been extensively studied independently, only little effort has been investigated into studying fairness of explanations on their own - i.e. the explanations it self should be fair. In this work we formally and empirically study individual fairness and robustness of contrasting explanations - in particular we consider counterfactual explanations as a prominent instance of contrasting explanations. Furthermore, we propose to use plausible counterfactuals instead of closest counterfactuals for improving the individual fairness of counterfactual explanations.
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