一个工具箱,用于浮出水面的健康公平伤害和偏见在大型语言模型中的偏见
Stephen R Pfohl1, Heather Cole-Lewis2, Rory Sayres3
1Google Research, Mountain View, CA, USA. spfohl@google.com.
Nature medicine
|September 23, 2024
概括
评估大语言模型 (LLM) 对于健康公平至关重要. 我们的研究开发了方法和数据集,以识别LLM答案中的偏见,发现各种方法揭示了其他评估中遗漏的问题.
科学领域:
- 医疗保健中的人工智能
- 健康 公平 研究 健康 公平 研究
- 自然语言处理自然语言处理.
背景情况:
- 大型语言模型 (LLM) 为复杂的健康信息提供了潜力,但有可能造成伤害并加剧健康差异.
- 对LLM中与公平相关的失败进行可靠的评估对于开发促进健康公平的系统至关重要.
- 现有的评估方法可能无法充分地显示LLM产生的医疗信息中的偏见.
研究的目的:
- 介绍资源和方法,以识别长期形式的偏见,LLM产生的医学答案.
- 引入EquityMedQA,一个用于评估LLM公平性的对抗查询数据集.
- 使用Med-PaLM 2 LLM进行大规模实证案例研究,以评估与股权相关的偏见.
主要方法:
- 开发了一个多因素框架,用于对LLM生成的偏见答案进行人类评估.
- 创建了EquityMedQA,这是一个由七个数据集组成的数据集,其中包含了对抗性查询.
- 采用代参与式方法,包括对Med-PaLM 2答案的审查,用于框架和数据集设计.
- 通过Med-PaLM 2进行了大规模的实证案例研究.
主要成果:
- 开发的方法成功地揭示了LLM产生的医疗答案中的偏见,而这些偏见可能会错过更狭窄的评估.
- 经验研究表明了多因素框架和EquityMedQA数据集在识别潜在危害方面的有效性.
- 调查结果强调需要多样化的评估方法和多样化的评级者背景.
结论:
- 本文所介绍的资源和方法对于揭示医疗保健应用的LLM中存在的偏差非常有价值.
- 虽然不是全面的,但该方法有助于实现开发人工智能系统的目标,以促进可访问和公平的医疗保健.
- 鼓励进一步开发和应用这些方法,以全面评估和促进人工智能系统的公平健康结果.
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