Supervised fairness-aware machine learning under distribution shifts is an emerging field that addresses the challenge of maintaining equitable and unbiased predictions when faced with changes in data distributions from source to target domains. In real-world applications, machine learning models are often trained on a specific dataset but deployed in environments where the data distribution may shift over time due to various factors. This shift can lead to unfair predictions, disproportionately affecting certain groups characterized by sensitive attributes, such as race and gender. In this survey, we provide a summary of various types of distribution shifts and comprehensively investigate existing methods based on these shifts, highlighting six commonly used approaches in the literature. Additionally, this survey lists publicly available datasets and evaluation metrics for empirical studies. We further explore the interconnection with related research fields, discuss the significant challenges, and identify potential directions for future studies.

翻译：监督式公平感知机器学习在分布偏移情境下是一个新兴领域，其旨在解决当数据分布从源域到目标域发生变化时，如何保持预测的公平性与无偏性。在实际应用中，机器学习模型通常基于特定数据集训练，但部署环境中数据分布可能因多种因素随时间变化。这种偏移可能导致不公平的预测，对受敏感属性（如种族和性别）界定的特定群体产生不成比例的影响。在本综述中，我们总结了分布偏移的多种类型，并基于这些偏移全面考察了现有方法，重点介绍了文献中六种常用方法。此外，本综述列出了可用于实证研究的公开数据集及评估指标。我们进一步探讨了与相关研究领域的关联，讨论了主要挑战，并指出了未来研究的潜在方向。