From Symptoms to Systems: An Expert-Guided Approach to Understanding Risks of Generative AI for Eating Disorders

TL;DR

This study identifies and categorizes the potential risks of generative AI systems related to eating disorders through expert interviews, highlighting areas for improved safeguards and risk assessment.

Contribution

The paper introduces a novel expert-guided taxonomy of AI risks for eating disorders, based on qualitative analysis of clinician and researcher insights.

Findings

Seven categories of AI-related risks identified

Certain user interactions may intensify eating disorder risks

Implications for safeguard design and risk assessment

Abstract

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.