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.

监督的公平感知机器学习在面临数据分布从源领域到目标领域的变化时，是一个新兴的领域，解决了在实现公正和无偏预测方面的挑战。本综述提供各种类型的分布变化的总结，并全面调查了现有方法，重点介绍了文献中常用的六种方法。此外，本综述列出了可用于经验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连接，讨论了重大挑战，并确定了未来研究的潜在方向。