生成性人工智能和饮食障碍:在图像描绘中揭露刻板印象,并制定研究议程
1SEED Lifespan Strategic Research Centre, School of Psychology, Faculty of Health, Deakin University, Geelong, Victoria, Australia.
The International journal of eating disorders
|November 19, 2025
概括
像ChatGPT-5这样的生成AI经常刻板描绘饮食障碍. 在图像生成中明确提示人口多样性可以为心理健康AI创建更具包容性和代表性的视觉效果.
科学领域:
- 人工智能在心理健康中的作用
- 计算机视觉和图像生成
- 临床心理学和饮食障碍 临床心理学和饮食障碍
背景情况:
- 生成型人工智能越来越多地用于心理健康支持,引发了人们对精神病状况刻板印象描绘的担忧.
- 人工智能中饮食障碍的视觉表现可以延续有害的刻板印象,影响公众的认知和临床理解.
- 确保人工智能产生的内容具有包容性和道德上的合理性,对于其在精神卫生保健中负责任地采用至关重要.
研究的目的:
- 调查ChatGPT-5如何在视觉上表示饮食障碍.
- 确定基于提示的操作是否可以产生更具包容性和多样化的AI生成的饮食障碍图像.
- 建立一个安全,道德和临床相关的人工智能应用在心理健康的框架.
主要方法:
- 聊天GPT-5被提示为神经性厌食症,神经性嗜食症,过度饮食障碍和身体形象困难生成图像.
- 使用了标准提示和指示人口统计学变化 (年龄,性别,种族,身体类型) 的提示.
- 生成的图像被视觉分析,以评估包容性和多样性改进.
主要成果:
- 默认提示导致了刻板印象的图像,主要是年轻的白人女性,具有与饮食障碍相关的特定身体类型.
- 通过单个偏见减少提示,在包容性方面发现了轻微的改进.
- 用明确的多样性指令生成多个图像导致了跨年龄,性别和种族更具包容性的表征,尽管仍然存在一些刻板印象.
结论:
- 聊天GPT-5默认为饮食障碍的刻板印象视觉模板.
- 指导人工智能模型产生多样化的输出显著提高了心理健康状况的代表性包容性.
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