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.

本文提出了一种新的框架 ——α 交叉公平（Intersectional Fairness）框架，来解决交叉敏感群体的分类问题，提出了一种新的公平度量，并在实验中验证了该框架的有效性以及一些算法的有效性。