提出可认证的公平性审计方法，应对数据分布变化时的不公平风险。
From Fragile to Certified: Wasserstein Audits of Group Fairness Under Distribution Shift
- 基于Wasserstein距离构建鲁棒公平性框架，统一多种公平性定义
- 在标准数据集上验证，该方法在分布漂移下仍保持公平性评估稳定
- 适合需要可证明公平性的高风险场景，如金融信贷与司法决策
群体公平性度量（如等几率）在重采样下波动剧烈，尤其在分布漂移时极为脆弱，影响可靠审计。本文提出一种基于Wasserstein的分布鲁棒框架，对以经验分布为中心的合理测试分布球内的最差情况群体公平性进行认证。该框架通过通用条件概率泛函统一常见群体公平性概念，并定义ε-Wasserstein分布公平性（ε-WDF）作为审计目标。借助强对偶性，推导出可计算的重构形式和高效估计器（DRUNE）。证明了可行性与一致性，并建立了有限样本下的认证保证，同时在光滑性和边界条件下给出量化界。在多个标准基准和分类器上，ε-WDF 在分布漂移下均实现稳定公平性评估，为超越观测数据的群体公平性审计与认证提供了原则性基础。
原文摘要 · Abstract (English)
Group-fairness metrics (e.g., equalized odds) can vary sharply across resamples and are especially brittle under distribution shift, undermining reliable audits. We propose a Wasserstein distributionally robust framework that certifies worst-case group fairness over a ball of plausible test distributions centered at the empirical law. Our formulation unifies common group fairness notions via a generic conditional-probability functional and defines $\varepsilon$-Wasserstein Distributional Fairness ($\varepsilon$-WDF) as the audit target. Leveraging strong duality, we derive tractable reformulations and an efficient estimator (DRUNE) for $\varepsilon$-WDF. We prove feasibility and consistency and establish finite-sample certification guarantees for auditing fairness, along with quantitative bounds under smoothness and margin conditions. Across standard benchmarks and classifiers, $\varepsilon$-WDF delivers stable fairness assessments under distribution shift, providing a principled basis for auditing and certifying group fairness beyond observational data.
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。