The increasing reliance on ML models in high-stakes tasks has raised a major concern on fairness violations. Although there has been a surge of work that improves algorithmic fairness, most of them are under the assumption of an identical training and test distribution. In many real-world applications, however, such an assumption is often violated as previously trained fair models are often deployed in a different environment, and the fairness of such models has been observed to collapse. In this paper, we study how to transfer model fairness under distribution shifts, a widespread issue in practice. We conduct a fine-grained analysis of how the fair model is affected under different types of distribution shifts and find that domain shifts are more challenging than subpopulation shifts. Inspired by the success of self-training in transferring accuracy under domain shifts, we derive a sufficient condition for transferring group fairness. Guided by it, we propose a practical algorithm with a fair consistency regularization as the key component. A synthetic dataset benchmark, which covers all types of distribution shifts, is deployed for experimental verification of the theoretical findings. Experiments on synthetic and real datasets including image and tabular data demonstrate that our approach effectively transfers fairness and accuracy under various distribution shifts.
翻译:虽然工作激增,提高了算法的公平性,但大多数工作都假设了相同的培训和测试分布。然而,在许多现实应用中,这种假设常常受到侵犯,因为以前经过培训的公平模型往往在不同环境中部署,而且这种模型的公平性已观察到崩溃。在本文件中,我们研究如何在分配转移中转移公平模型,这是一个广泛的实践问题。我们仔细分析了公平模型在不同类型的分配转移中如何受到影响,发现域变比亚人口变化更具挑战性。由于在域变转移中成功地进行自我培训以转移准确性,我们为转移群体公平性创造了充分的条件。我们以此为指导,提出一个包含公平一致性规范的实用算法,作为关键组成部分。一个涵盖所有类型的分配转移的合成数据集基准,用于实验性核实理论结论。关于合成和真实数据集的实验,包括图像和表格数据,表明我们在各种分配转移中有效地转移了公平性和准确性。