人种在人工智能和机器学习模型中的作用和使用与健康相关
Martin C Were1,2, Ang Li3, Bradley A Malin2,4,5
1Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, United States.
Journal of medical Internet research
|July 31, 2025
概括
本研究探讨了种族在健康人工智能 (AI) 和机器学习 (ML) 模型中的复杂作用. 它提供了一个框架和"要考虑的点",以解决AI/ML开发和部署中与种族有关的挑战.
科学领域:
- 医疗信息学 医疗信息学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 将种族纳入与健康相关的AI和ML模型的讨论越来越多.
- 现有的框架不足以解决这些复杂的问题.
研究的目的:
- 为健康领域的种族相关挑战提供系统分析AI/ML.
- 为涉及这些问题的利益相关者提供一个指导框架.
主要方法:
- 对与种族有关的挑战进行横断的景观分析.
- 围绕AI和ML生命周期对分析进行结构化.
- 为决策制定"需要考虑的点".
主要成果:
- 在AI/ML生命周期中确定了与种族有关的关键挑战.
- 提出了一种结构化的方法来检查和解决这些挑战.
- 强调需要一个整体的框架.
结论:
- 需要一个全面的框架来指导有关种族的负责任的AI/ML开发.
- 在AI/ML生命周期中对种族相关问题的系统检查至关重要.
- 利益相关者需要为道德AI/ML实施提供可操作的"要考虑的点".
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