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.
Fairness and explainability are two important and closely related requirements of decision making systems. While fairness and explainability of decision making systems have been extensively studied independently, only little effort has been put into studying fairness of explanations on their own . Current explanations can be unfair to an individual: an example is given by counterfactual explanations which propose different actions to change the output class to two similar individuals. In this work we formally and empirically study individual fairness and its mathematical formalization as robustness for counterfactual explanations as a prominent instance of contrasting explanations. In addition , we propose to use plausible counterfactuals instead of closest counterfactuals for improving the individual fairness of counterfactual explanations.
R
ensuring and evaluating fairness as well as
fairness and
R
masking systems has
making systems have
R
investigated
put
D
- i. e. the explanations it self should be fair
A
Current explanations can be unfair to an individual: an example is given by counterfactual explanations which propose different actions to change the output class to two similar individuals.
R
robustness of contrasting explanations - in particular we consider
its mathematical formalization as robustness for
R
Furthermore
In addition