专家引导下梳理生成式AI对进食障碍的七大风险
From Symptoms to Systems: An Expert-Guided Approach to Understanding Risks of Generative AI for Eating Disorders
- 通过临床专家访谈构建风险分类体系
- 发现AI交互会触发进食障碍的典型症状
- 适合医疗安全、AI伦理研究者参考
生成式AI可能对进食障碍易感人群构成严重风险。现有防护措施往往忽视细微但具有临床意义的线索，导致诸多风险未被识别。为深入理解这些风险，我们对15位在进食障碍领域有专长的临床医生、研究人员及倡导者进行了半结构化访谈。采用溯因式定性分析，构建了一个由七个类别组成的专家引导型风险分类体系：（1）提供泛化健康建议；（2）鼓励非正常行为；（3）支持症状掩饰；（4）生成瘦身灵感内容；（5）强化负面自我认知；（6）过度关注身体；（7）固化进食障碍的狭隘认知。结果表明，特定用户与生成式AI的互动方式可能与进食障碍的临床特征产生交叉，从而加剧风险。本文讨论了其在风险评估、防护设计及与领域专家共同评估实践中的启示。
原文摘要 · Abstract (English)
Generative AI systems may pose serious risks to individuals vulnerable to eating disorders. Existing safeguards tend to overlook subtle but clinically significant cues, leaving many risks unaddressed. To better understand the nature of these risks, we conducted semi-structured interviews with 15 clinicians, researchers, and advocates with expertise in eating disorders. Using abductive qualitative analysis, we developed an expert-guided taxonomy of generative AI risks across seven categories: (1) providing generalized health advice; (2) encouraging disordered behaviors; (3) supporting symptom concealment; (4) creating thinspiration; (5) reinforcing negative self-beliefs; (6) promoting excessive focus on the body; and (7) perpetuating narrow views about eating disorders. Our results demonstrate how certain user interactions with generative AI systems intersect with clinical features of eating disorders in ways that may intensify risk. We discuss implications of our work, including approaches for risk assessment, safeguard design, and participatory evaluation practices with domain experts.
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