路边聊天机器人：审计大语言模型向临床医生、照护者与患者推荐的心理健康干预
Curbside Chatbots: Auditing the Mental Health Interventions Large Language Models Recommend to Clinicians, Caregivers, and Patients
Matthew Flathers, Jill Noorily, Phuong Anh Nguyen, Mark Kalinich, Mina Fallah, Victoria Lisowski, Christine Shi, Rebekah M. Bodner 等 17 位作者
AI 速读 基于摘要
该研究审计了41个大语言模型在临床医生、照护者和患者三种提问视角下推荐的心理健康干预，发现推荐行为随视角变化，且模型有时会优先推荐自己评分偏低的选项。
cs.CY · cs.CL · cs.HC
查看原文摘要
Users are asking language models mental health questions and are receiving answers. We audited what 41 language models recommend to users when the same clinical information reaches them from three footings: a clinician describing a patient, a caregiver describing a loved one, or a person describing themselves. Across 19 case vignettes and five questions, we collected 35,055 free-text responses, extracted every intervention named with a validated judge, and had 29 models rate the full option lists on a seven-point Likert scale for the same cases. Models largely recommend what they rate well, with recommendation behavior differing by footing, and many models offering clinicians a consultation-style response and lay users hedged, simplified advice. When asked about recommendations for a specific intervention type, models more often named options they themselves rated below zero as initial choices, usually with conditions attached. We will release the instrument, code, and full corpus on publication as a living audit.