Fairness in Machine Learning: A Review for Statisticians

• 成果类型: Review
• 署名作者: He, Xianwen; Li, Yao
• 署名单位: University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina School of Medicine
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459; 1537-274X
• DOI: 10.1080/01621459.2025.2579579
• 发表日期: 2025-10-02
• 页码: 2834-2851
• 关键词: Classification; Fairness learning; Machine learning ethics; Statistical computing; BIAS

摘要

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. Supplementary materials for this article are available online.