[论文解读] A Sequentially Fair Mechanism for Multiple Sensitive Attributes

François Hu, Philipp Ratz+1 more|arXiv (Cornell University)|Sep 12, 2023

Health Systems, Economic Evaluations, Quality of Life被引用 4

一句话总结

本文提出一种基于多边际Wasserstein均值的多敏感属性（MSA）顺序公平机制，以实现在后处理预测中实现精确与近似公平性。该方法确保在不同敏感特征下结果的顺序不变性，同时支持针对性的公平性优先级设定，并提供闭式解，实证性能在合成与真实世界数据集上表现优异。

ABSTRACT

In the standard use case of Algorithmic Fairness, the goal is to eliminate the relationship between a sensitive variable and a corresponding score. Throughout recent years, the scientific community has developed a host of definitions and tools to solve this task, which work well in many practical applications. However, the applicability and effectivity of these tools and definitions becomes less straightfoward in the case of multiple sensitive attributes. To tackle this issue, we propose a sequential framework, which allows to progressively achieve fairness across a set of sensitive features. We accomplish this by leveraging multi-marginal Wasserstein barycenters, which extends the standard notion of Strong Demographic Parity to the case with multiple sensitive characteristics. This method also provides a closed-form solution for the optimal, sequentially fair predictor, permitting a clear interpretation of inter-sensitive feature correlations. Our approach seamlessly extends to approximate fairness, enveloping a framework accommodating the trade-off between risk and unfairness. This extension permits a targeted prioritization of fairness improvements for a specific attribute within a set of sensitive attributes, allowing for a case specific adaptation. A data-driven estimation procedure for the derived solution is developed, and comprehensive numerical experiments are conducted on both synthetic and real datasets. Our empirical findings decisively underscore the practical efficacy of our post-processing approach in fostering fair decision-making.

研究动机与目标

• 为解决现有单敏感属性公平方法在处理多敏感属性交叉偏见方面的局限性。
• 开发一种后处理框架，确保在不损害预测性能的前提下，实现对多个敏感特征的公平性。
• 通过为特定属性引入近似公平性约束，实现灵活的、优先级化的公平性改进。
• 提供一种闭式、可解释的解决方案，量化公平性修正中各敏感特征间的相关性。
• 通过实证验证该方法在减少不公平性的同时，保持在多样化数据集上的高预测准确性的有效性。

提出的方法

• 该方法构建多边际2-Wasserstein均值，将强人口统计均等性推广至多敏感属性。
• 通过利用Wasserstein均值的结合律，将最优公平预测器重新表述为顺序形式，确保无论修正顺序如何，结果保持一致。
• 通过引入可调节的公平性约束，支持近似公平性，实现风险与不公平性之间的权衡。
• 开发了基于数据的估计程序，从经验分布中计算均值，支持实际部署。
• 通过调节各公平性约束参数，实现对特定属性公平性改进的优先级设定。
• 该方法可后处理应用于任何现成的预测器，保持其原始性能的同时纠正偏差。

Figure 1: Synthetic data with $\boldsymbol{\tau}=(0,0.05,0.1)$ . A sequential unfairness evaluation, $\mathcal{U}_{3}$ , of ( left pane ) exact fairness, ( middle ) approximate $A_{1:3}$ -fairness with $\boldsymbol{\varepsilon}$ -RI where $\boldsymbol{\varepsilon}=\varepsilon_{1,2,3}=(0.2,0.5,0.75)$

实验结果

研究问题

• RQ1 如何在不依赖修正顺序的前提下，一致地实现对多个敏感属性的公平性？
• RQ2 如何利用最优传输理论，最优地将人口统计均等性推广至多敏感属性？
• RQ3 在多属性设置下，如何灵活地为特定属性优先设置公平性约束？
• RQ4 近似公平性对预测性能与子群体公平性的影响如何？
• RQ5 敏感特征间的相关性如何影响公平性修正过程与结果？

主要发现

• 所提方法实现了与修正顺序无关的精确公平性，无论敏感属性以何种顺序进行修正，预测结果均保持一致。
• 在Adult数据集上，该方法将不公平性从0.18降低至0.03，同时保持F1为0.90，优于Fairlearn（不公平性0.08）与FairBalance（不公平性0.16）。
• 在包含10个敏感属性的tunadromd数据集中，该方法将不公平性从3.77降低至1.05，证明了其在高维敏感空间中的可扩展性。
• 该方法支持针对性的公平性改进：通过将某一属性（如性别）的ε调整为另一属性（如种族）的六分之一，可在保持子群平均预测值的同时，对主要属性进行修正。
• 数值实验表明，该方法在保持高预测性能（Adult数据集上F1≈0.95）的同时，显著降低了所有测试数据集中的不公平性。
• 顺序框架允许清晰解释子群层面的公平性影响，揭示修正顺序可能导致特定子群净收益或净损失，具体取决于修正顺序。

Figure 2: (Risk, Unfairness) phase diagrams that shows the sequential fairness approach for ( left ) two and ( right ) three sensitive features. In this study, Unfairness represents the overall unfairness $\hat{\mathcal{U}}=\hat{\mathcal{U}}_{1:3}$ . Bottom-left corner gives the best trade-off.