Fairness in Machine Learning: A Review for Statisticians
这篇综述面向统计学家，梳理了机器学习中防止算法因性别、种族等人口特征产生不公平结果的机制，按预处理、处理中、后处理三个阶段分类，并用实验对比了代表性方法。
With the widespread application of machine learning algorithms in daily life, it is crucial to mitigate the risk of these algorithms producing socially undesirable outcomes that may disproportionately disadvantage certain groups or individuals based on demographic characteristics such as gender, race, or disabilities. In recent years, machine learning fairness has gained increasing attention from both researchers and the public. This article provides a comprehensive overview of fairness-enhancing mechanisms designed to mitigate such risks, along with the fairness criteria they aim to achieve. We organize these fairness-enhancing mechanisms into three categories—pre-processing, in-processing, and post-processing—corresponding to different stages of the machine learning lifecycle and varying levels of access to the underlying algorithm. The discussion focuses on fairness in binary classification models using numerical tabular data, which serve as a foundation for addressing fairness in more complex algorithms. Additionally, we present experimental results that offer a comparative evaluation of representative fairness-enhancing approaches.