Fair Recourse for All: Ensuring Individual and Group Fairness in Counterfactual Explanations

Summary

This paper addresses fairness in counterfactual explanations (CFs), which show how input changes would alter an ML model's decision and so offer actionable recourse. The authors define individual, group and hybrid fairness, and propose a model-agnostic, reinforcement learning based method that generates CFs meeting fairness constraints at both individual and group levels. Evaluated on four benchmark datasets, the approach preserves proximity and plausibility of the generated CFs.