LLM聊天机器人助进食障碍康复，但存在潜在风险需警惕
Private Yet Social: How LLM Chatbots Support and Challenge Eating Disorder Recovery
- 26名患者与聊天机器人互动10天，通过倾诉获得情感支持
- 73%的对话中出现不当回应，因用户信任而未被察觉
- 适合心理干预设计者参考，关注安全机制与用户信任平衡
进食障碍（ED）是需要长期管理的复杂心理疾病。基于大语言模型（LLM）的聊天机器人可提供即时支持，但其在敏感场景中的可靠性与安全性仍存疑。本研究观察了26名进食障碍患者与专为康复设计的聊天机器人WellnessBot在10天内的互动。参与者普遍感到通过向机器人倾诉经历而获得赋能，这种交流兼具私密性与社交感。然而，研究发现存在大量不当回复，尤其对进食障碍患者可能造成伤害，部分原因在于用户对机器人可靠性的无条件信任。基于此，研究提出安全有效的LLM干预设计建议。
原文摘要 · Abstract (English)
Eating disorders (ED) are complex mental health conditions that require long-term management and support. Recent advancements in large language model (LLM)-based chatbots offer the potential to assist individuals in receiving immediate support. Yet, concerns remain about their reliability and safety in sensitive contexts such as ED. We explore the opportunities and potential harms of using LLM-based chatbots for ED recovery. We observe the interactions between 26 participants with ED and an LLM-based chatbot, WellnessBot, designed to support ED recovery, over 10 days. We discovered that our participants have felt empowered in recovery by discussing ED-related stories with the chatbot, which served as a personal yet social avenue. However, we also identified harmful chatbot responses, especially concerning individuals with ED, that went unnoticed partly due to participants' unquestioning trust in the chatbot's reliability. Based on these findings, we provide design implications for safe and effective LLM-based interventions in ED management.
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