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：分布偏移下的监督公平性机器学习是一个新兴领域，旨在应对数据从源域到目标域分布变化时保持预测公平性与无偏性的挑战。在实际应用中，机器学习模型通常基于特定数据集训练，却部署在数据分布会随时间推移因多种因素而发生变化的环境中。这种分布偏移可能导致不公平预测，对以种族、性别等敏感属性为特征的部分群体产生不成比例的影响。本综述总结了多种分布偏移类型，并基于这些偏移对现有方法进行了全面研究，重点介绍了文献中六种常用方法。此外，本综述还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相互联系，讨论了当前面临的重要挑战，并指出了未来研究的潜在方向。