预警人工智能关于认知偏差的预警
Jonathan Wang1,2, Donald A Redelmeier1,2,3,4,5
1Evaluative Clinical Sciences Program, Sunnybrook Research Institute, Toronto, ON, Canada.
概括
预先警告人工智能 (AI) 模型对认知偏见的预警并没有显著减少医学建议中的错误. 当使用人工智能生成的医疗建议时,临床医师的警至关重要.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 认知科学 认知科学
背景情况:
- 生成性预训练变压器大语言模型 (LLM) 可以表现出类似人类的认知偏见.
- 这些偏见可能会影响人工智能产生的医疗建议的准确性和安全性.
研究的目的:
- 评估一个明确的预警是否可以减轻人工智能模型中的认知偏见,从而产生医疗建议.
- 评估预警对人工智能响应的长度和内容的影响.
主要方法:
- 用10个临床细微的病例来测试带有和没有预警提示的特定偏差.
- 预警组的回应与对照组的回应进行了比较,对时间长度,对偏见的讨论以及整体偏见的减少进行了比较.
主要成果:
- 预警增加了50%的响应长度和100倍以上的偏见讨论频率.
- 尽管讨论越来越多,但预警只减少了6.9%的总体偏差,没有完全消除偏差.
- 人工智能模型可以被提示讨论种族和性别偏见,但这不能保证减轻推理陷.
结论:
- 明确的预警不足以充分缓解医学建议的大型语言模型中的认知偏见.
- 临床医生的批判性推理技能对于解释人工智能产生的医疗建议至关重要.
- 需要进一步的研究来开发强大的方法来减少医学AI的偏见.
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