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：分布偏移下的监督式公平感知机器学习是一个新兴领域，旨在解决当数据分布从源域向目标域变化时，如何保持预测的公平性与无偏性这一挑战。在现实应用中，机器学习模型通常基于特定数据集训练，但在部署环境中数据分布可能因多种因素随时间推移而偏移。这种偏移会导致不公平的预测，对具有种族、性别等敏感属性的特定群体造成不成比例的影响。本综述总结了分布偏移的多种类型，并基于这些偏移对现有方法进行了全面探究，重点梳理了文献中六种常用方法。此外，本文列出了用于实证研究的公开数据集和评估指标。我们进一步探讨了与相关研究领域的交叉关联，讨论了重大挑战，并指出了未来研究的方向。