在大型语言模型中固有的医学相关年龄歧视的评估
Tomohisa Seki1, Yoshimasa Kawazoe1,2, Hiromasa Ito1
1Department of Healthcare Information Management, The University of Tokyo Hospital.
Studies in health technology and informatics
|August 8, 2025
概括
大型语言模型 (LLM) 在医疗环境中显示年龄偏差,表现出不愿意治疗老年人. 这凸显了需要框架来确保AI医疗保健应用中的公平性.
科学领域:
- 医学伦理 医学伦理
- 人工智能的人工智能
- 老年学是一门学科.
背景情况:
- 全球人口老龄化,越来越需要解决年龄歧视问题.
- 医疗领域表现出明显的年龄歧视,但像大型语言模型 (LLM) 这样的AI工具缺乏类似的公平评估.
- 年龄歧视对老年人获得医疗保健和结果产生负面影响.
研究的目的:
- 在大型语言模型 (LLM) 中评估与医学相关的年龄歧视.
- 在医疗保健场景中,在LLM中可视化潜在的年龄偏差.
- 确定在医学中需要公平的人工智能框架.
主要方法:
- 开发一个专门的数据集来评估LLM的响应.
- 对与年龄有关的偏见的LLM输出分析.
- 视觉化技术用于说明LLMs中的年龄歧视模式.
主要成果:
- 许多LLM在医疗环境中表现出刻板印象的年龄歧视.
- 观察到的偏见包括不愿干预老年人的治疗.
- 在LLM回复中也发现了自我年龄主义.
结论:
- 目前,LLM表现出年龄偏差,特别是在医疗应用中.
- 开发框架来缓解AI中的年龄歧视对于公平的医疗保健至关重要.
- 确保LLM的公平性对于其负责任地融入医疗领域至关重要.
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