The endeavor to preserve the generalization of a fair and invariant classifier across domains, especially in the presence of distribution shifts, becomes a significant and intricate challenge in machine learning. In response to this challenge, numerous effective algorithms have been developed with a focus on addressing the problem of fairness-aware domain generalization. These algorithms are designed to navigate various types of distribution shifts, with a particular emphasis on covariate and dependence shifts. In this context, covariate shift pertains to changes in the marginal distribution of input features, while dependence shift involves alterations in the joint distribution of the label variable and sensitive attributes. In this paper, we introduce a simple but effective approach that aims to learn a fair and invariant classifier by simultaneously addressing both covariate and dependence shifts across domains. We assert the existence of an underlying transformation model can transform data from one domain to another, while preserving the semantics related to non-sensitive attributes and classes. By augmenting various synthetic data domains through the model, we learn a fair and invariant classifier in source domains. This classifier can then be generalized to unknown target domains, maintaining both model prediction and fairness concerns. Extensive empirical studies on four benchmark datasets demonstrate that our approach surpasses state-of-the-art methods.

翻译：保持公平且不变的分类器在跨领域中的泛化能力，尤其是在分布偏移存在的情况下，已成为机器学习中一项重要且复杂的挑战。为应对这一挑战，研究者已开发出多种有效算法，专注于解决公平感知的领域泛化问题。这些算法旨在应对各种类型的分布偏移，尤其侧重协变量偏移和依赖偏移。其中，协变量偏移指输入特征边际分布的变化，而依赖偏移则涉及标签变量与敏感属性联合分布的改变。本文提出一种简单但有效的方法，旨在通过同时处理跨领域的协变量偏移和依赖偏移，学习一个公平且不变的分类器。我们断言存在一个潜在的转换模型，能够将数据从一个领域转换至另一个领域，同时保留与非敏感属性和类别相关的语义。通过该模型增强多种合成数据领域，我们在源领域中学习到一个公平且不变的分类器，该分类器可泛化至未知的目标领域，同时保持模型预测和公平性考量。在四个基准数据集上的广泛实证研究表明，我们的方法优于现有最先进方法。