将AI素养教育嵌入心理治疗聊天系统，防范用户过度披露隐私风险。
Therapeutic AI and the Hidden Risks of Over-Disclosure: An Embedded AI-Literacy Framework for Mental Health Privacy
- 在聊天系统中内嵌AI素养提示，引导用户理性披露信息。
- 用户易因信任错位或设计诱导而过度分享无关个人信息。
- 适合关注心理健康AI应用安全与伦理的研究者与开发者。
大型语言模型（LLMs）正越来越多地应用于心理健康领域，从结构化的治疗辅助工具到非正式的基于聊天的情绪支持助手。尽管这些系统提升了可及性、可扩展性和个性化水平，但其在心理健康护理中的整合带来了尚未充分研究的隐私与安全挑战。与传统临床互动不同，基于LLM的治疗通常缺乏明确的信息收集、处理、存储或再利用机制。缺乏临床指导的用户可能因错误的信任、对数据风险认识不足，或受系统对话设计影响，过度披露个人敏感信息，这些信息有时与他们的主要问题无关。这种过度暴露不仅引发隐私担忧，还增加了模型偏见、误解释和长期数据滥用的风险。本文提出一种框架，将人工智能（AI）素养干预措施直接嵌入心理健康对话系统，并规划一项研究以评估其对披露安全性、信任感和用户体验的影响。
原文摘要 · Abstract (English)
Large Language Models (LLMs) are increasingly deployed in mental health contexts, from structured therapeutic support tools to informal chat-based well-being assistants. While these systems increase accessibility, scalability, and personalization, their integration into mental health care brings privacy and safety challenges that have not been well-examined. Unlike traditional clinical interactions, LLM-mediated therapy often lacks a clear structure for what information is collected, how it is processed, and how it is stored or reused. Users without clinical guidance may over-disclose personal information, which is sometimes irrelevant to their presenting concern, due to misplaced trust, lack of awareness of data risks, or the conversational design of the system. This overexposure raises privacy concerns and also increases the potential for LLM bias, misinterpretation, and long-term data misuse. We propose a framework embedding Artificial Intelligence (AI) literacy interventions directly into mental health conversational systems, and outline a study plan to evaluate their impact on disclosure safety, trust, and user experience.
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