人口代表性在3个领先的人工智能文本到图像生成器中的代表性
Rohaid Ali1, Oliver Y Tang1,2, Ian D Connolly3
1Department of Neurosurgery, The Warren Alpert Medical School of Brown University, Providence, Rhode Island.
JAMA surgery
|November 15, 2023
概括
人工智能图像生成器延续了偏见,低估了女性和非白人外科医生,尤其是实习生. 需要采取保障措施,防止人工智能在外科手术等职业中放大有害的刻板印象.
科学领域:
- 人工智能的人工智能
- 医疗专业的人口统计数据
- 人工智能中的社会偏见
背景情况:
- 人工智能 (AI) 文本到图像生成器越来越复杂.
- 人们担心这些人工智能模型会延续社会偏见,包括基于职业的刻板印象.
- 了解AI在专业职业中对人口群体的代表性至关重要.
研究的目的:
- 评估三个领先的人工智能文本到图像模型中外科医生代表的人口统计准确性.
- 将人工智能生成的外科医生图像与现实世界的外科医生和实习生的人口统计数据进行比较.
- 确定AI在外科专业中描绘种族和性别的潜在偏见.
主要方法:
- 一项横截面研究分析了来自3种流行的文本到图像模型的8个外科专业的2400个AI生成图像.
- 七位独立审稿人根据种族 (白人与非白人) 和性别 (女性与男性) 来对人工智能制作的图像进行了分类.
- 用于比较的人口统计数据来源于美国医学院协会和美国医学协会.
主要成果:
- 两种人工智能模型生成了超过98%白人和男性外科医生的图像,与现实数据相比,非白人和女性人数明显不足.
- 虽然一个模型 (DALL-E 2) 显示出与外科医生有的人口平等性,但所有三种模型都低估了实习生代表性.
- 在某些情况下,基于地理位置的提示增加了非白人外科医生的代表性,但没有显著影响女性代表性.
结论:
- 领先的AI文本到图像生成器目前放大了社会偏见,特别是关于女性和非白人外科医生的代表性不足.
- 所有评估的模型都未能准确地代表外科实习生的人口统计数据.
- 这些发现强调了人工智能开发人员迫切需要实施护和反机制,以减轻专业刻板印象的扩大.
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