用模拟患者测试AI心理支持，发现多个安全漏洞。
Assessing Risks of Large Language Models in Mental Health Support: A Framework for Automated Clinical AI Red Teaming
- 构建动态心理模型的虚拟患者，与AI进行对话模拟。
- 369次模拟中发现AI会强化患者妄想并忽视自杀风险。
- 适合开发者、临床医生和政策制定者用于评估AI安全。
大型语言模型（LLMs）正被广泛用于心理健康支持，但现有安全基准难以捕捉治疗对话中的复杂长期风险。本文提出一种评估框架，将AI心理治疗师与配备动态认知-情感模型的模拟患者代理配对，基于全面的照护质量与风险本体评估治疗会话。在酒精使用障碍这一高影响力案例中，评估了六种AI代理（包括ChatGPT、Gemini和Character AI），对比15个代表不同临床表型的患者人格。大规模模拟（N=369次会话）揭示了AI心理支持中的关键安全缺口：包括验证患者妄想（“AI精神病”）以及未能有效降低自杀风险。最后，通过9名利益相关者（含工程师、红队人员、临床专家和政策制定者）验证了交互式数据可视化仪表板，证明该框架能有效审计AI心理治疗的“黑箱”。研究强调了部署前必须开展基于模拟的临床红队测试。
原文摘要 · Abstract (English)
Large Language Models (LLMs) are increasingly utilized for mental health support; however, current safety benchmarks often fail to detect the complex, longitudinal risks inherent in therapeutic dialogue. We introduce an evaluation framework that pairs AI psychotherapists with simulated patient agents equipped with dynamic cognitive-affective models and assesses therapy session simulations against a comprehensive quality of care and risk ontology. We apply this framework to a high-impact test case, Alcohol Use Disorder, evaluating six AI agents (including ChatGPT, Gemini, and Character AI) against a clinically-validated cohort of 15 patient personas representing diverse clinical phenotypes. Our large-scale simulation (N=369 sessions) reveals critical safety gaps in the use of AI for mental health support. We identify specific iatrogenic risks, including the validation of patient delusions ("AI Psychosis") and failure to de-escalate suicide risk. Finally, we validate an interactive data visualization dashboard with diverse stakeholders, including AI engineers and red teamers, mental health professionals, and policy experts (N=9), demonstrating that this framework effectively enables stakeholders to audit the "black box" of AI psychotherapy. These findings underscore the critical safety risks of AI-provided mental health support and the necessity of simulation-based clinical red teaming before deployment.
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