通过匹配输入空间设计新公平性约束，提升模型对不同群体的公平性。
Fairness Through Matching
- 基于输入空间的隐式映射关系，提出新的公平性度量方法。
- 实验表明该方法可有效训练出具有理想公平性的模型。
- 适用于需要精准控制群体间公平性的机器学习场景。
群体公平性要求由特定敏感属性区分的不同群体在整体上获得相等的结果。现有群体公平性度量通常通过不同群体预测结果间的统计差距来衡量。本文揭示了现有度量的一个隐含性质，进而提出一种基于该性质的新群体公平性约束。研究发现，任何群体公平模型都对应一个隐式的输入空间传输映射。基于此，我们提出新的公平性度量——匹配人口均等性（Matched Demographic Parity, MDP），通过给定传输映射匹配来自不同群体的个体，量化其预测差异的平均值。证明了任意传输映射均可用于构建公平模型，并提出新算法 Fairness Through Matching (FTM)，利用用户指定的传输映射施加MDP约束以学习公平模型。特别地，基于最优传输理论提出了两种理想的传输映射形式并讨论其优势。实验表明，通过合理选择传输映射，FTM能成功训练出具备良好公平特性的模型。
原文摘要 · Abstract (English)
Group fairness requires that different protected groups, characterized by a given sensitive attribute, receive equal outcomes overall. Typically, the level of group fairness is measured by the statistical gap between predictions from different protected groups. In this study, we reveal an implicit property of existing group fairness measures, which provides an insight into how the group-fair models behave. Then, we develop a new group-fair constraint based on this implicit property to learn group-fair models. To do so, we first introduce a notable theoretical observation: every group-fair model has an implicitly corresponding transport map between the input spaces of each protected group. Based on this observation, we introduce a new group fairness measure termed Matched Demographic Parity (MDP), which quantifies the averaged gap between predictions of two individuals (from different protected groups) matched by a given transport map. Then, we prove that any transport map can be used in MDP to learn group-fair models, and develop a novel algorithm called Fairness Through Matching (FTM), which learns a group-fair model using MDP constraint with an user-specified transport map. We specifically propose two favorable types of transport maps for MDP, based on the optimal transport theory, and discuss their advantages. Experiments reveal that FTM successfully trains group-fair models with certain desirable properties by choosing the transport map accordingly.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。