饮食障碍的生成人工智能:与在线支持进行语言比较和对危害的定性分析
See Heng Yim1, Dong Whi Yoo2, Apostolos Polymerou3
1Department of Psychology, University of Hong Kong, Hong Kong, Hong Kong.
The International journal of eating disorders
|November 22, 2025
概括
生成性AI显示了饮食障碍 (ED) 同行支持的风险,与人类反应相比,产生重复性,偏见和潜在有害的内容. 伦理保障对于安全的AI集成至关重要.
科学领域:
- 数字健康和心理健康 医疗保健
- 人工智能在临床支持中的应用
- 计算语言学和心理语言学
背景情况:
- 生成型人工智能 (AI) 为支持饮食障碍 (ED) 患者提供了潜在的好处.
- 然而,在这个敏感领域使用人工智能带来了固有的风险,需要仔细评估.
- 了解人工智能产生的在线支持与人工产生的在线支持之间的差异对于用户安全至关重要.
研究的目的:
- 为了比较AI的心理语言属性和词语语义特征与在线饮食障碍社区的人类反应.
- 在同行支持环境中评估与人工智能产生的反应相关的潜在危害.
- 评估人工智能模型 (GPT,Gemini,Llama,Mistral) 在为饮食障碍提供支持方面的安全性和有效性.
主要方法:
- 分析来自Reddit饮食障碍社区的COVID-19前数据 (3634个帖子,22,359个响应).
- 使用四种最先进的模型生成AI响应,并获得同行支持.
- 心理语言学分析使用语言调查和词数 (LIWC) 词典和词语语义比较.
- 对100个AI反应进行定性临床分析,以确定潜在的危害.
主要成果:
- 人工智能响应比人类响应更长,更有礼貌,但不那么有创意,更重复.
- 定性分析显示,人工智能有潜力强化ED行为,表现出隐含的偏见 (例如,减肥重点),缺乏上下文理解.
- 所有的人工智能模型都产生了有害的内容,包括促进ED行为或偏见,严重程度不同.
结论:
- 在ED在线社区中,人工智能和人类反应之间存在显著的差异,这可能会带来潜在的风险.
- 伦理考虑需要采取防范措施,防止在人工智能驱动的支持中强化有害行为和偏见.
- 谨慎的AI整合,进一步验证和制定明确的指导方针对于ED同行支持的安全和有效使用至关重要.
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