公平作为一个事后的想法:在模型开发者-临床医生用户合作中公平的美国观点
John Banja1, Judy Wawira Gichoya2, Nicole Martinez-Martin3
1Center for Ethics, Emory University, Atlanta, Georgia, United States of America.
PLOS digital health
|November 20, 2023
概括
机器学习伦理指南强调公平,但缺乏明确的定义. 实现公平性需要开发人员和用户之间的协作,特定环境的方法,而不是一个适合所有人的解决方案.
科学领域:
- 人工智能中的伦理学
- 机器学习治理 机器学习治理
- 社会技术系统 社会技术系统
背景情况:
- 许多伦理准则强调了机器学习 (ML) 模型中公平的重要性.
- 现有的指导方针往往缺乏标准化的定义或方法来确保公平.
- 公平性经常被视为"事后考虑",而不是核心设计原则.
研究的目的:
- 从美国的道德角度批判性地检查ML中的各种公平公式和方法.
- 反对一种普遍的,适合所有人的一种方法来实现ML公平性.
- 提出一个协作框架,在特定的用例中定义和运行公平性.
主要方法:
- 分析现有的ML伦理准则和公平性表述.
- 对实现公平性的定性和统计方法进行了审查.
- 讨论涉及ML开发人员和临床用户的协作模型.
主要成果:
- 没有一个单一的公平性定义或方法在所有ML用例中普遍适用.
- 公平取决于环境,需要量身定制的方法.
- 开发人员和用户之间可能会出现关于公平性的分歧,需要持续对话.
结论:
- 确保ML中的公平性需要特定的环境,模型开发人员和用户之间的协作努力.
- 公平必须是ML生命周期的组成部分,而不是事后的想法.
- 持续的合作对于推进ML应用中的公平性要求至关重要.
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