From Symptoms to Systems: An Expert-Guided Approach to Understanding Risks of Generative AI for Eating Disorders
This work addresses risks for vulnerable individuals with eating disorders, but it is incremental as it builds on existing concerns with expert-guided insights.
The study tackled the problem of generative AI risks for individuals with eating disorders by conducting expert interviews to develop a taxonomy of seven risk categories, such as encouraging disordered behaviors and creating thinspiration, demonstrating how user interactions may intensify these risks.
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.