增强人工智能医疗系统的公平性,使用属性中立框架
Lianting Hu1,2,3,4, Dantong Li2,3,4, Huazhang Liu3,4
1The Data Center, Wuhan Children's Hospital (Wuhan Maternal and Child Healthcare Hospital), Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430016, Hubei, China.
Nature communications
|October 9, 2024
概括
本研究引入了属性中立框架,以减少医疗保健人工智能 (AI) 的偏见. 属性中和器 (AttrNzr) 提高了不同群体的AI公平性,同时保持了诊断准确性.
科学领域:
- 人工智能在医学中的应用
- 医疗保健 公平 医疗保健 公平
- 机器学习用于医疗保健
背景情况:
- 医疗保健中的人工智能 (AI) 面临着不公平和不平等的挑战.
- 偏见的人工智能模型可能会学习敏感属性和健康数据之间的不良相关性.
- 在受保护群体之间,人工智能的不平等表现阻碍了公平的医疗保健部署.
研究的目的:
- 引入一个新的框架来解开和中和人工智能医疗保健模型中的偏见属性.
- 开发一种方法,以改善不同患者子组的代表性和公平性.
- 在不影响性能的情况下减轻人工智能诊断模型中的不公平性.
主要方法:
- 介绍了属性中性框架和属性中和器 (AttrNzr).
- 在保护属性不易预测的情况下生成中和数据.
- 使用中和数据进行疾病诊断模型 (DDM) 的训练.
主要成果:
- 与其他缓解算法相比,AttrNzr有效地减少了DDM中的不公平.
- 属性中性框架保持了DDM的整体疾病诊断性能.
- AttrNzr在中和多个属性方面表现出实用性,并且仅在训练阶段.
结论:
- 属性中立框架为医学中的AI公平性挑战提供了一个以数据为中心,与模型无关的解决方案.
- 通过中和偏见的数据,AttrNzr解决了不公平的根本原因.
- 这种方法具有创造更公平的人工智能支持医疗系统的巨大潜力.
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