医疗保健专业人员的性别代表性在大型语言模型生成的故事中
Bradley D Menz1, Nicole M Kuderer2, Benjamin Chin-Yee3,4
1College of Medicine and Public Health, Flinders University, Adelaide, South Australia, Australia.
JAMA network open
|September 23, 2024
概括
大型语言模型 (LLM) 显示在医疗保健角色中的性别代表性有偏见. 护士是98%的女性,而医生和外科医生显示出显著的性别差异,突出需要改善LLM多样性.
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) 越来越多地用于教育和医疗保健.
- 确保LLM通过公平和多样化的信息生成至关重要,以防止强化刻板印象.
- 偏见的LLM产出可以影响未来几代人在关键职业中的愿望.
研究的目的:
- 评估关于医生,外科医生和护士的LLM产生的故事中的性别代表性.
- 调查个性和职业资历描述因素如何影响这些医疗保健角色中的性别比例.
- 确定有关医学性别的LLM输出中的潜在偏见.
主要方法:
- 使用公开可访问的LLM (GPT-3.5-turbo,GPT-4,Gemini-pro,Llama-2-70B-chat) 的横截面模拟研究.
- 产生了6000个故事,其中包括医生,外科医生和护士.
- 另外43,200个提示探讨了人格和资历描述因素对性别代表性的影响.
主要成果:
- 在LLM产生的故事中,护士是98%的女性.
- 医学博士的女性代表从50%到84%不等,而外科医生的女性代表从36%到80%不等.
- 人格特征,如可取性和资历影响了性别代表性,在某些模型中,高级角色不太可能被描绘为女性.
结论:
- 法学士开发人员必须更新工具,以确保在医疗保健角色中公平的性别代表性.
- 随着在医疗保健和教育领域的采用越来越多,对LLM的持续监测是必要的.
- 在LLM中促进多元化的代表性对于反映社会需求和避免有害的刻板印象至关重要.
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