在人工智能中评估多样性和刻板印象,产生了医疗保健提供者的代表性
Anjali Agrawal1, Gauri Gupta2, Anushri Agrawal3
1Department of Computer Science, The University of Texas at Austin, Austin, TX, United States.
Frontiers in digital health
|May 12, 2025
概括
生成型人工智能反映了医疗保健专业的现实世界多样性,但也延续了刻板印象. 这项研究提供了一个框架来分析人工智能生成的图像在医疗保健中的偏见.
科学领域:
- 人工智能的人工智能
- 医疗成像医学成像
- 社会学 社会学 社会学
背景情况:
- 生成型人工智能模型可以模拟社会数据,并提出关于它们对不同人口的代表性的问题.
- 现有的医疗保健劳动力数据显示了多样性和潜在偏见的模式.
研究的目的:
- 评估医疗保健提供者的AI生成图像中的多样性和识别刻板印象.
- 开发一个框架来量化人工智能产生的医学图像中的偏见.
主要方法:
- 使用DALL-E 3生成360图像的医疗保健专业人员与特定的种族和性别标识符.
- 使用共识评分评估图像多样性,并使用谷歌视觉对象/动作标签分析刻板印象.
主要成果:
- 人工智能生成的医疗保健提供者图像显示了适度的性别 (3.2/5) 和种族 (2.8/5) 多样性,反映了劳动力统计数据.
- 谷歌视觉分析揭示了AI图像中的性别和种族相关的刻板印象,包括外表,表情和服装.
结论:
- 生成型人工智能反映了医疗保健专业中现有的社会多样性模式和偏见.
- 开发的基于机器学习的框架可以为人工智能在医疗保健工作人员培训和招聘方面的政策提供信息,减轻偏见.
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