"Explain it in the Same Way!" -- Model-Agnostic Group Fairness of Counterfactual Explanations
This tackles fairness issues in AI decision-making systems for users in protected groups, but it is incremental as it builds on existing counterfactual explanation methods.
The paper addresses the problem of counterfactual explanations having varying complexity across protected groups, which disadvantages some groups, and proposes a model-agnostic method to ensure these explanations do not differ significantly in complexity between groups.
Counterfactual explanations are a popular type of explanation for making the outcomes of a decision making system transparent to the user. Counterfactual explanations tell the user what to do in order to change the outcome of the system in a desirable way. However, it was recently discovered that the recommendations of what to do can differ significantly in their complexity between protected groups of individuals. Providing more difficult recommendations of actions to one group leads to a disadvantage of this group compared to other groups. In this work we propose a model-agnostic method for computing counterfactual explanations that do not differ significantly in their complexity between protected groups.