提出新公平性框架，让算法纠错更公平且负担更均等
Revisiting (Un)Fairness in Recourse by Minimizing Worst-Case Social Burden
- 以社会负担最小化为核心设计公平算法
- 实测多群体纠错成本显著降低，准确率不降
- 适合关注算法公平性的政策与系统设计者
基于机器学习的决策在敏感领域应用日益广泛，推动了对分类器公平性的研究。除公平分类外，新兴法规要求：当分类器给出负面结果时，必须提供个体可采取的行动以逆转该结果，这一概念称为算法纠错。然而，许多研究者对纠错过程中的公平性保障表示担忧。本文首次对算法纠错中的不公平性进行整体理论分析，正式建立纠错公平性与分类公平性之间的联系，并揭示标准等成本范式的局限性。随后提出一种基于社会负担的新公平性框架，并设计出适用于真实场景的实用算法MISOB。在多个真实数据集上的实验表明，MISOB在不牺牲整体分类准确率的前提下，显著降低了所有群体的社会负担。
原文摘要 · Abstract (English)
Machine learning based predictions are increasingly used in sensitive decision-making applications that directly affect our lives. This has led to extensive research into ensuring the fairness of classifiers. Beyond just fair classification, emerging legislation now mandates that when a classifier delivers a negative decision, it must also offer actionable steps an individual can take to reverse that outcome. This concept is known as algorithmic recourse. Nevertheless, many researchers have expressed concerns about the fairness guarantees within the recourse process itself. In this work, we provide a holistic theoretical characterization of unfairness in algorithmic recourse, formally linking fairness guarantees in recourse and classification, and highlighting limitations of the standard equal cost paradigm. We then introduce a novel fairness framework based on social burden, along with a practical algorithm (MISOB), broadly applicable under real-world conditions. Empirical results on real-world datasets show that MISOB reduces the social burden across all groups without compromising overall classifier accuracy.
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