Equalized odds, as a popular notion of algorithmic fairness, aims to ensure
that sensitive variables, such as race and gender, do not unfairly influence
the algorithm prediction when conditioning on the true outcome. Despite rapid
advancements, most of the current research focuses on the violation of
equalized odds caused by one sensitive attribute, leaving the challenge of
simultaneously accounting for multiple attributes under-addressed. We address
this gap by introducing a fairness learning approach that integrates
adversarial learning with a novel inverse conditional permutation. This
approach effectively and flexibly handles multiple sensitive attributes,
potentially of mixed data types. The efficacy and flexibility of our method are
demonstrated through both simulation studies and empirical analysis of
real-world datasets.

通过结合对抗学习和一种新颖的逆条件排列，我们介绍了一种公平学习方法，有效且灵活地处理多个敏感属性，可能包含不同数据类型，从而填补了当前研究中专注于单个敏感属性违反平衡几率的空白。通过模拟研究和对真实数据集的实证分析，我们展示了该方法的功效和灵活性。