Achieving the generalization of an invariant classifier from source domains to shifted target domains while simultaneously considering model fairness is a substantial and complex challenge in machine learning. Existing domain generalization research typically attributes domain shifts to concept shift, which relates to alterations in class labels, and covariate shift, which pertains to variations in data styles. In this paper, by introducing another form of distribution shift, known as dependence shift, which involves variations in fair dependence patterns across domains, we propose a novel domain generalization approach that addresses domain shifts by considering both covariate and dependence shifts. We assert the existence of an underlying transformation model can transform data from one domain to another. By generating data in synthetic domains through the model, a fairness-aware invariant classifier is learned that enforces both model accuracy and fairness in unseen domains. Extensive empirical studies on four benchmark datasets demonstrate that our approach surpasses state-of-the-art methods.

翻译：在机器学习中，实现不变分类器从源域到偏移目标域的泛化，同时兼顾模型公平性，是一项重大且复杂的挑战。现有域泛化研究通常将域偏移归因于概念偏移（与类别标签变化相关）和协变量偏移（与数据风格变化相关）。本文通过引入另一种分布偏移形式——依赖偏移，即域间公平依赖模式的变化，提出了一种新颖的域泛化方法，通过同时考虑协变量偏移和依赖偏移来应对域偏移。我们断言存在一个潜在的变换模型，能够将数据从一个域转换到另一个域。通过该模型在合成域中生成数据，学习一个公平性感知的不变分类器，该分类器在未见域中同时保障模型准确性和公平性。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。