使用大型语言模型来审计模型医疗保健偏见
Zara N Ansari1, Aaron Fanous2, Jesutofunmi A Omiye3
1Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, U.S.A., zansari6@stanford.edu.
Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
|February 27, 2026
概括
大型语言模型 (LLM) 可以检测人工智能系统中的偏差. 更小,更具成本效益的LLM可以成为精确的偏差检测器,特别是在使用高级提示技术时,如思维线程.
科学领域:
- 人工智能的人工智能
- 自然语言处理自然语言处理.
- 医疗保健信息学 医疗保健信息学
背景情况:
- 大型语言模型 (LLM) 在医疗保健方面表现有前途,但表现出人口偏见.
- 由于大数据量,手动偏差审计是不切实际的.
- 法律法规可能会对其他模型进行偏差审计,但它们的有效性各不相同.
研究的目的:
- 评估LLM大小和提示策略如何影响偏差检测.
- 使用医疗保健数据集,比较不同LLM的偏差检测性能.
- 确定成本效益高的LLM解决方案,用于人工智能的偏见审计.
主要方法:
- 使用了斯坦福医疗保健红色团队数据集,其中包含提示,输出和偏见标签.
- 已经测试了GPT-3.5-turbo,GPT-4o,lama3.3和o1-mini的偏差检测能力.
- 采用提示技术,包括思维线程,以评估它们对偏见检测的影响.
主要成果:
- 像o1-mini这样的较小型号实现了比GPT-4o更高的精度和F1得分.
- 大型模型中的自我批评特征并没有显著改善偏见检测.
- 思考线索促使所有测试模型的偏差检测得到大幅增强.
结论:
- 较小的LLM可以在偏差检测方面有效且具有成本效益,特别是当精度是关键时.
- 提示技术对于改进基于LLM的偏见审计至关重要.
- 选择用于偏差检测的LLM应与特定的指标优先级保持一致.
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