[论文解读] Trade-offs Between Individual and Group Fairness in Machine Learning: A Comprehensive Review

Sandra Benítez-Peña, Blas Kolic+2 more|arXiv (Cornell University)|Jan 23, 2026

Ethics and Social Impacts of AI被引用 0

一句话总结

本论文综述同时解决群体公平（GF）和个体公平（IF）的方法，分析它们的权衡，并提供混合公平方法的分类及批判性评估。

ABSTRACT

Algorithmic fairness has become a central concern in computational decision-making systems, where ensuring equitable outcomes is essential for both ethical and legal reasons. Two dominant notions of fairness have emerged in the literature: Group Fairness (GF), which focuses on mitigating disparities across demographic subpopulations, and Individual Fairness (IF), which emphasizes consistent treatment of similar individuals. These notions have traditionally been studied in isolation. In contrast, this survey examines methods that jointly address GF and IF, integrating both perspectives within unified frameworks and explicitly characterizing the trade-offs between them. We provide a systematic and critical review of hybrid fairness approaches, organizing existing methods according to the fairness mechanisms they employ and the algorithmic and mathematical strategies used to reconcile multiple fairness criteria. For each class of methods, we examine their theoretical foundations, optimization mechanisms, and empirical evaluation practices, and discuss their limitations. Additionally, we discuss the challenges and identify open research directions for developing principled, context-aware hybrid fairness methods. By synthesizing insights across the literature, this survey aims to serve as a comprehensive resource for researchers and practitioners seeking to design hybrid algorithms that provide reliable fairness guarantees at both the individual and group levels.

研究动机与目标

• 需要在高风险的 ML 决策中同时解决 GF 与 IF 的公平性方法的必要性
• 提供一个统一框架与分类法，用于跨公平机制的混合 GF–IF 方法
• 批判性评估现有方法的理论基础、优化策略与评估实践
• 识别上下文感知、 principled 的混合公平方法的局限性与开放研究方向

提出的方法

• 对同时解决 GF 与 IF 的文献进行系统性且批判性的回顾
• 按公平机制与协调多目标的优化策略对方法进行分类
• 提出两张互补表格（方法学分类和实证实现）来组织文献
• 讨论 GF 与 IF 之间的理论不兼容性与权衡，包括 Pareto 前沿的考虑
• 给出统一记号并正式阐述 GF 与 IF 的概念，以便进行跨-study 比较

Figure 1 : Trade-off between group and individual fairness. Left : observations from two groups in feature space $X$ , where edges indicate similar individuals (using $k$ -nearest neighbors) who should receive similar decisions (individual fairness). A single global threshold (gray radius) maximizes

实验结果

研究问题

• RQ1 现有哪些明确将群体公平和个体公平在单一学习框架中整合的方法？
• RQ2 GF 与 IF 如何相互作用，在同时优化两者时会产生哪些权衡？
• RQ3 在数据、表征和模型阶段，混合 GF–IF 方法有哪些分类法与设计原则？
• RQ4 在 principled、上下文感知的混合公平方法方面，主要局限性与开放方向有哪些？

主要发现

• GF 与 IF 经常在数学上不兼容，导致在改善一个时可能损害另一个的权衡
• 存在大量混合方法，按数据变换、重加权、表征学习、增强和正则化等组织
• 文献呈现双表整合：方法学分类与对计算与数据集的实证评估
• 许多研究明确建模权衡或联合目标，以平衡群体层面与个体层面的公平
• 回顾强调上下文感知、 principled 的方法在两个层面上保证公平的开放挑战与方向

(a) Excluding arXiv.