在一般用途嵌入模型中管理偏见的有意的方法
Wei-Hung Weng1, Andrew Sellergen1, Atilla P Kiraly1
1Google, Mountain View, CA, USA.
The Lancet. Digital health
|January 26, 2024
概括
医疗保健中的通用预训练嵌入 (GPPEs) 人工智能应该保留数据信息以避免偏见. 下游模型需要精心设计和多样化的评估,以确保公平性和防止性能问题.
科学领域:
- 医疗保健中的机器学习
- 人工智能伦理学 人工智能伦理学
- 医学图像分析 医学图像分析
背景情况:
- 医疗保健中的机器学习引发了对偏见和加剧护理差异的担忧.
- 医疗图像可能包含敏感的属性信号,这对算法公平性构成了挑战.
- 为医疗保健设计无偏见的通用预训练嵌入器 (GPPEs) 是复杂的.
研究的目的:
- 调查GPPEs的最佳设计策略,以减轻下游医疗保健模型中的偏差.
- 分析从GPPEs中删除敏感属性的意外后果.
- 强调严格的评估和多元化的团队在开发公平的人工智能模型中的重要性.
主要方法:
- 审查和综合先前发布的关于医疗保健中机器学习偏差的数据.
- 对GPPE设计的概念分析及其对下游模型性能的影响.
- 在GPPEs中保留信息的论点和属性删除的危险.
主要成果:
- 试图从GPPEs中删除敏感属性可能会导致下游模型的意外偏差和不良性能.
- 理想情况下,GPPEs应该保留与原始数据一样多的信息.
- 上游组件的技术中立性并不能保证一个公正的端到端系统.
结论:
- 在GPPEs中优先保留信息,而不是删除敏感属性.
- 下游模型必须仔细设计,评估偏差,并进行审计.
- 多样化的团队和多样化的患者队伍对于开发公平的医疗保健AI至关重要.
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