提出首个图结构框架，生成可执行的群体公平性反事实解释。
FACEGroup: Feasible and Actionable Counterfactual Explanations for Group Fairness
- 构建图模型捕捉现实可行性约束与群体间相似性
- 在基准数据集上生成可行反事实，量化公平差异
- 适合关注群体公平审计的研究者与从业者
反事实解释通过揭示输入需如何改变以达成期望结果来评估不公平性。本文提出首个基于图结构的框架，用于生成群体公平性的反事实解释，这是可信机器学习的关键方面。我们的框架FACEGroup（可实现且可操作的群体公平性反事实解释）建模真实世界的可行性约束，识别具有相似反事实的子群体，并捕捉反事实生成中的关键权衡，区别于现有方法。为评估公平性，我们引入新型指标，分别在群体和子群体层面分析，明确考虑这些权衡。在基准数据集上的实验表明，FACEGroup能有效生成可行的群体反事实，同时考虑权衡；我们的指标能捕捉并量化公平性差异。
原文摘要 · Abstract (English)
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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