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arXiv · 2410.22591

FACEGroup: Feasible and Actionable Counterfactual Explanations for Group Fairness

Christos Fragkathoulas·Vasiliki Papanikou·Evaggelia Pitoura·Evimaria Terzi
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Abstract

Counterfactual explanations assess unfairness by revealing how inputs must change to achieve a desired outcome. This paper introduces the first graph-based framework for generating group counterfactual explanations to audit group fairness, a key aspect of trustworthy machine learning. Our framework, FACEGroup (Feasible and Actionable Counterfactual Explanations for Group Fairness), models real-world feasibility constraints, identifies subgroups with similar counterfactuals, and captures key trade-offs in counterfactual generation, distinguishing it from existing methods. To evaluate fairness, we introduce novel metrics for both group and subgroup level analysis that explicitly account for these trade-offs. Experiments on benchmark datasets show that FACEGroup effectively generates feasible group counterfactuals while accounting for trade-offs, and that our metrics capture and quantify fairness disparities.

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Christos Fragkathoulas, Vasiliki Papanikou, Evaggelia Pitoura, Evimaria Terzi. 2024-10-29. FACEGroup: Feasible and Actionable Counterfactual Explanations for Group Fairness. https://arxiv.org/abs/2410.22591

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