In this work, we tackle the problem of intersectional group fairness in the classification setting, where the objective is to learn discrimination-free models in the presence of several intersecting sensitive groups. First, we illustrate various shortcomings of existing fairness measures commonly used to capture intersectional fairness. Then, we propose a new framework called the $\alpha$ Intersectional Fairness framework, which combines the absolute and the relative performances between sensitive groups. Finally, we provide various analyses of our proposed framework, including the min-max and efficiency analysis. Our experiments using the proposed framework show that several in-processing fairness approaches show no improvement over a simple unconstrained approach. Moreover, we show that these approaches minimize existing fairness measures by degrading the performance of the best of the group instead of improving the worst.

翻译：在本文中，我们解决了分类场景下的交叉群体公平性问题，其目标是在存在多个交叉敏感群组的情况下学习无歧视模型。首先，我们阐述了常用于捕捉交叉公平性的现有公平性度量存在的各种缺陷。接着，我们提出了一个名为$\alpha$交叉公平性框架的新框架，该框架结合了敏感群组之间的绝对绩效与相对绩效。最后，我们对提出的框架进行了包括极小极大分析和效率分析在内的多项分析。使用所提框架的实验表明，几种处理中的公平性方法相较于简单的无约束方法并未带来改进。此外，我们发现这些方法通过降低最优群组的绩效而非提升最差群组的绩效来最小化现有的公平性度量。