什么是公平的? 定义健康的机器学习中的公平性
Jianhui Gao1, Benson Chou1, Zachary R McCaw2
1Department of Statistical Sciences, University of Toronto, Toronto, Ontario, Canada.
Statistics in medicine
|September 15, 2025
概括
确保机器学习 (ML) 模型是公平的,对于公平的医疗保健至关重要. 本研究探讨了ML公平性概念,测量方法以及预防健康差异的健康应用中的未来挑战.
科学领域:
- 医疗信息学 医疗信息学
- 机器学习 机器学习
- 医学伦理 医学伦理
背景情况:
- 机器学习 (ML) 模型越来越多地用于临床决策.
- 确保ML模型的安全性,有效性和公平性对于防止加剧健康差距至关重要.
- 了解ML模型如何导致不公平的结果对于公平的医疗保健至关重要.
研究的目的:
- 检查健康的ML中公平性的概念化.
- 调查医疗保健中不公平的ML决策背后的原因.
- 审查在现实世界卫生应用中衡量公平性的方法.
主要方法:
- 在健康的ML中对公平性概念的文献综述.
- 分析基于群体,个人和因果关系的公平框架.
- 讨论在以健康为重点的ML应用中运行公平性的讨论.
主要成果:
- 审查了群体,个人和因果关系框架内的共同公平性概念.
- 该研究确定了在医疗保健中使用的ML模型中不公平的潜在来源.
- 在现实世界卫生应用中讨论了ML公平性的各种测量方法.
结论:
- 对健康的ML的公平性需要仔细的概念化和测量.
- 解决公平性是防止ML模型扩大现有的健康差异的关键.
- 未来的研究应该专注于在临床ML应用中运行公平性,以确保公平的患者结果.
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