加强科临床决策支持:通过人工智能治理解决算法偏见
Benjamin A Goldstein1, Dinushika Mohottige2, Sophia Bessias3
1Department of Biostatistics and Bioinformatics, School of Medicine, Duke University, Durham, North Carolina; AI Health, School of Medicine, Duke University, Durham, North Carolina.
概括
临床决策支持 (CDS) 工具可能包含算法偏差. 了解像代理变量这样的偏见来源是决定是否应该在CDS工具中使用敏感数据 (如种族) 的关键.
科学领域:
- 医疗信息学 医疗信息学
- 医学伦理 医学伦理
- 人工智能在医学中的应用
背景情况:
- 临床决策支持 (CDS) 工具在科和一般临床实践中越来越多地使用.
- 人们越来越担心CDS工具中的算法偏差,这促使人们对种族等敏感变量的使用进行了调查.
研究的目的:
- 用电子健康记录数据开发的CDS工具中分析算法偏差的来源.
- 探索复杂的问题,即是否应该在CDS工具中包含敏感变量,如种族.
- 突出卫生系统治理在导航这些道德考虑中的作用.
主要方法:
- 确定和分解三种主要偏差来源:代理变量,可观察性问题和潜在异质性.
- 在CDS工具中对敏感变量的功能进行定性和定量分析.
- 审查与CDS治理委员会机构经验.
主要成果:
- CDS工具中的算法偏差可能来自代理变量,可观察性问题和数据异质性.
- 关于包括敏感变量的决定往往更多地取决于定性因素,而不是定量数据.
- 卫生系统治理委员会对于指导在CDS中敏感变量方面的决策至关重要.
结论:
- 为了开发公平的CDS工具,需要对算法偏差来源有更深入的理解.
- 将敏感变量纳入CDS工具需要仔细考虑它们的特定功能和潜在影响.
- 培养一个专注于敏感变量和模型开发和治理中的公平的社区实践是必不可少的.
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