In this work, we consider 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 definition called the $\alpha$-Intersectional Fairness, which combines the absolute and the relative performance across sensitive groups and can be seen as a generalization of the notion of differential fairness. We highlight several desirable properties of the proposed definition and analyze its relation to other fairness measures. Finally, we benchmark multiple popular in-processing fair machine learning approaches using our new fairness definition and show that they do not achieve any improvement over a simple baseline. Our results reveal that the increase in fairness measured by previous definitions hides a "leveling down" effect, i.e., degrading the best performance over groups rather than improving the worst one.

翻译：在这项工作中，我们考虑了分类场景下的交叉群体公平问题，其目标是在存在多个交叉敏感群体的情况下学习无歧视模型。首先，我们阐述了常用于衡量交叉公平的现有公平性指标的各种缺陷。然后，我们提出了一种新定义，称为$\alpha$-交叉公平，该定义结合了跨敏感群体的绝对和相对性能，并可视为差分公平概念的推广。我们强调了所提定义的若干理想属性，并分析了其与其他公平性指标的关系。最后，我们使用新公平定义对多种流行的过程内公平机器学习方法进行了基准测试，结果表明它们并未比简单基线有显著改进。我们的研究揭示，先前定义所衡量的公平性提升掩盖了一种“降级”效应，即降低了跨群体的最佳性能，而非改进了最差性能。