标题： 公平领域泛化：一种信息论视角 显示英文标题

标题： Fair Domain Generalization: An Information-Theoretic View

摘要： 领域泛化（DG）和算法公平性是机器学习中的两个关键挑战。 然而，大多数DG方法仅关注在未见目标领域中最小化期望风险，而没有考虑算法公平性。 相反，公平性方法通常不考虑领域变化，因此在训练期间实现的公平性可能无法推广到未见的测试领域。 在这项工作中，我们通过研究公平领域泛化（FairDG）问题来弥合这些差距，该问题旨在最小化未见目标领域中的期望风险和公平性违规。 我们推导了多类分类任务中多组敏感属性的期望风险和公平性违规的新信息论上界。 这些上界从信息理论的角度为算法设计提供了关键见解。 在这些见解的指导下，我们引入了PAFDG（领域泛化中的帕累托最优公平性），这是一个实用的框架，用于解决FairDG问题，并通过帕累托优化建模效用与公平性的权衡。 在现实世界视觉和语言数据集上的实验表明，PAFDG在效用-公平性权衡方面优于现有方法。 显示英文摘要

摘要： Domain generalization (DG) and algorithmic fairness are two critical challenges in machine learning. However, most DG methods focus only on minimizing expected risk in the unseen target domain without considering algorithmic fairness. Conversely, fairness methods typically do not account for domain shifts, so the fairness achieved during training may not generalize to unseen test domains. In this work, we bridge these gaps by studying the problem of Fair Domain Generalization (FairDG), which aims to minimize both expected risk and fairness violations in unseen target domains. We derive novel mutual information-based upper bounds for expected risk and fairness violations in multi-class classification tasks with multi-group sensitive attributes. These bounds provide key insights for algorithm design from an information-theoretic perspective. Guided by these insights, we introduce PAFDG (Pareto-Optimal Fairness for Domain Generalization), a practical framework that solves the FairDG problem and models the utility-fairness trade-off through Pareto optimization. Experiments on real-world vision and language datasets show that PAFDG achieves superior utility-fairness trade-offs compared to existing methods.