社会人口学修饰因素塑造了大型语言模型的伦理决策
Vera Sorin1, Panagiotis Korfiatis1, Jeremy D Collins1
1Department of Radiology, Mayo Clinic College of Medicine and Science, Mayo Clinic, Rochester, MN USA.
Journal of healthcare informatics research
|November 13, 2025
概括
医疗保健中的大型语言模型 (LLM) 容易受到社会人口学偏见的影响,从而改变道德决策. 审计和调整对于临床应用中公平的AI至关重要.
科学领域:
- 医疗保健中的人工智能
- 医学伦理 医学伦理
- 计算语言学 计算语言学
背景情况:
- 大型语言模型 (LLM) 在临床决策中的伦理一致性尚未得到充分理解.
- LLM可能会表现出受社会人口因素影响的偏见,影响医疗保健公平.
研究的目的:
- 调查LLM是否会改变医疗伦理决策,当面临社会人口统计线索时.
- 评估各种开源LLM对临床维尼特评估中偏差的易感性.
主要方法:
- 评估了9个开源的LLM,使用100个临床图片,有/没有道德选择.
- 引入了社会人口统计学修改器,重复每个场景每模型10次.
- 进行了大约50万次实验来分析LLM响应转移.
主要成果:
- 所有测试的LLM都根据社会人口统计细节 (p < 0.001) 显著改变了响应.
- 高收入线索促进了实用选择,而边缘化群体的线索增加了自主考虑.
- 没有LLM在所有场景中保持一致的道德决策,实用选择显示了最大的转变.
结论:
- 当前的LLM可能会受到社会人口统计学线索的过度影响,对公平的医疗保健构成风险.
- 需要审计和调整策略,以确保临床信息学LLM的伦理行为.
- 必须制定LLM,以尊重伦理原则,同时适应临床复杂性和多样性.
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