评估与医疗保健相关任务的大型语言模型的文本生成中的种族和种族偏见:跨部门研究
John J Hanna1,2,3, Abdi D Wakene3, Andrew O Johnson1
1Information Services, ECU Health, Greenville, NC, United States.
Journal of medical Internet research
|March 13, 2025
概括
这项研究发现,四种流行的大型语言模型 (LLM) 在生成艾滋病毒释放指令时显示出最小的种族和种族偏见. 需要进一步的研究来建立医疗保健方面的偏见测量标准.
科学领域:
- 医疗保健中的人工智能
- 自然语言处理自然语言处理.
- 健康 公平 卫生 公平
背景情况:
- 在医疗保健中使用的大型语言模型 (LLM) 中,人们越来越担心种族和种族偏见.
- 对公开偏见的提示存在LLM保护措施,但微妙的偏见仍然是一个问题.
研究的目的:
- 在四个LLM (GPT-3.5-turbo,GPT-4,Gemini-1.0-pro,Llama3-70b) 中调查潜在的种族和种族偏见.
- 评估产生医疗保健消费者导向文本中的偏见,而不是公开偏见的查询.
主要方法:
- 横截面研究促使四个LLM生成艾滋病毒释放指令.
- 不同的患者种族/种族 (非洲裔美国人,亚洲人,西班牙裔白人,非西班牙裔白人) 在非识别的元数据中.
- 分析了情感,主观性,阅读方便性和单词频率的LLM输出.
主要成果:
- 种族/族群之间在语言或可读性测量方面没有统计学上显著的差异.
- 对于GPT-4的实体计数中存在的轻微显著差异没有得到后期分析的支持.
结论:
- 四个评估的LLM在医疗保健文本生成中显示出种族/种族的相对不变性.
- 迫切需要标准化方法来衡量LLM产生的健康内容中的偏见.
- 需要进一步的验证和影响性研究.
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