人工智能中残疾的描绘文本到图像生成:背景的影响和残疾的医疗化
Benjamin Ertman1, Bridget Xia2, Mona Sloane2
1Department of Psychology, University of Virginia.
Rehabilitation psychology
|September 29, 2025
概括
人工智能图像生成在医疗保健环境中显示出较少的残疾多样性. 像DALL-E-3这样的文本到图像模型在被医疗背景提示时会产生较少多样化的残疾表示,这凸显了需要改进的需要.
科学领域:
- 人工智能的人工智能
- 生物医学信息学 生物医学信息学
- 计算机科学 计算机科学
背景情况:
- 文本到图像 (TTI) 系统是一种人工智能 (AI),从文本描述中生成高分辨率图像.
- 现有的研究表明,在TTI系统中存在种族/民族和性别偏见.
- 在TTI系统中,残疾偏见仍未得到充分研究.
研究的目的:
- 为了比较AI生成的残疾人图像中的残疾人代表性,在一般情况下与医疗保健环境相比.
- 调查TTI系统中关于残疾的潜在偏见.
主要方法:
- 使用OpenAI的DALL-E-3 TTI系统,为四个提示生成50张图像:"残疾人"",残疾患者"",残疾医生"和"残疾医生和无残疾患者".
- 该研究评估了DALL-E-3在生成提示图像方面的成功率.
- 生成的图像根据残疾类型和人口特征进行编码.
主要成果:
- 当被提示使用"残疾人"时,DALL-E-3以100%的成功率产生了各种不同类型的残疾.
- 对于"残疾患者",DALL-E-3实现了100%的成功,但70%的图像描绘了刻板印象的身体残疾.
- "残疾医生"提示结果准确率为92%,主要显示身体 (94%) 或感官 (6%) 的残疾.
- 在与残疾医生和非残疾患者的提示中,64%的产生的医生缺乏残疾,70%的医生描绘了残疾患者.
结论:
- 对于残疾人来说,人工智能产生的图像多样性在医疗环境中显著下降.
- 这项研究强调了TTI模型开发人员解决和减轻代表性损害的关键需求.
- 随着TTI技术的普及,进一步的研究和开发至关重要.
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