在临床人工智能中废除"种族纠正"的微妙之处
Moustafa Abdalla1,2, LLana James3, David S Jones2
1Department of Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02115, United States.
Journal of the American Medical Informatics Association : JAMIA
|February 6, 2026
概括
在临床AI中消除种族校正是复杂的. 朴素的解决方案失败了,需要明智的策略来实现公平的AI开发和透明的模型.
科学领域:
- 临床的人工智能 (AI)
- 健康 公平 卫生 公平
- 算法偏见是一种算法偏见.
背景情况:
- 临床AI中的种族纠正延续了历史上的偏见,反映了传统医学中的问题.
- 简单地从人工智能模型中删除种族是无效的,因为嵌入的历史数据有偏见.
研究的目的:
- 分析在临床AI中消除种族校正的复杂性.
- 识别偏见消除的简单解决方案的陷.
- 为公平的人工智能模型开发提出系统策略.
主要方法:
- 分析了四个标准化场景,说明了临床AI中的种族纠正.
- 检查种族纠正变量,明确种族包含,代理变量推断和种族特定模型.
主要成果:
- 消除种族纠正的直观方法在任何分析的场景中都不能消除偏见.
- 历史数据中的遗留效应显著阻碍了偏差删除.
- 有必要进行深思熟虑,情境敏感的干预.
结论:
- 在临床AI中结束种族纠正需要有意识的,特定于背景的策略.
- 涉及多种利益相关者对于公平的人工智能至关重要.
- 提高模型的透明度和可审计性至关重要.
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