透过玻璃黑暗:人工智能中种族身份的感知产生了医疗图片和图像
Kevlian Andrew1, Michael J Montalbano1
1Department of Anatomical Sciences, St. George's University, True Blue, St. George's, Grenada.
Medical science educator
|July 8, 2025
概括
像ChatGPT和Bing Image Creator这样的人工智能 (AI) 工具可以在医学教育内容中延续种族刻板印象. 需要仔细的快速工程来增加多样性,并避免在人工智能产生的医疗病例中加强有害偏见.
科学领域:
- 医学教育 医学教育
- 人工智能的人工智能
- 健康 公平 卫生 公平
背景情况:
- 人工智能工具越来越多地用于医学内容创建.
- 人们担心人工智能可能会强化消极的刻板印象,阻碍多样性,公平性和包容性 (DEI) 努力.
- 这项研究调查了人工智能在医疗病例生成中延续民族种族刻板印象的潜力.
研究的目的:
- 探索OpenAI的ChatGPT和微软的Bing A.I.的潜力. 图像创造者为了延续民族种族刻板印象.
- 评估人工智能产生的医疗图片和图像关于种族代表性的可变性.
- 了解快速工程如何影响医疗病例生成的AI输出.
主要方法:
- 使用ChatGPT和Bing图像创建器生成了五种已知种族关系的疾病的医疗图片和图像.
- 采用代提示,包括初始,自动提示和提示工程技术.
- 分析了人工智能生成的内容中描绘的民族种族身份.
主要成果:
- 最初的AI输出通常描绘的是白种人,偏离了流行病学数据.
- 快速工程和自我推广在生成的内容中增加了种族多样性.
- 然而,次要的促使有时导致了更少的多样性,并加强了传统的民族种族协会.
结论:
- 数据集偏差和人工智能的学习能力凸显了对人类监督的需要.
- 在医学教育中,对人工智能工具的警使用至关重要,以防止加强基于种族的刻板印象.
- 有效的快速工程可以减轻但不能完全消除人工智能生成的医疗内容中的偏见.
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