构建安全可信的共情型心理聊天机器人，融合人类专家协作提升服务质量。
Enhancing Mental Health Support through Human-AI Collaboration: Toward Secure and Empathetic AI-enabled chatbots
- 采用联邦学习框架保护数据隐私，减少模型偏见。
- 引入临床医生持续验证，提升回应的情感深度与专业性。
- 适合关注数字心理健康、人机协同应用的研究者与开发者。
心理支持获取受限，尤其在边缘群体中因结构性与文化障碍难以及时获得帮助。本文探讨基于大语言模型（GPT v4、Mistral Large、LLama V3.1）的AI聊天机器人作为可扩展解决方案的潜力，评估其在心理情境下生成共情、有意义回应的能力。尽管这些模型能生成结构化回复，但在情感深度与适应性上仍不及人类治疗师。此外，因数据集不可靠及缺乏与心理健康专业人士的协作，信任度、偏见与隐私问题依然存在。为此，我们提出一种联邦学习框架，保障数据隐私、降低偏见，并通过临床医生持续验证提升响应质量。该方法旨在发展出安全、有证据支持、可信、共情且低偏见的心理健康支持AI系统，推动AI在数字心理健康领域的应用。
原文摘要 · Abstract (English)
Access to mental health support remains limited, particularly in marginalized communities where structural and cultural barriers hinder timely care. This paper explores the potential of AI-enabled chatbots as a scalable solution, focusing on advanced large language models (LLMs)-GPT v4, Mistral Large, and LLama V3.1-and assessing their ability to deliver empathetic, meaningful responses in mental health contexts. While these models show promise in generating structured responses, they fall short in replicating the emotional depth and adaptability of human therapists. Additionally, trustworthiness, bias, and privacy challenges persist due to unreliable datasets and limited collaboration with mental health professionals. To address these limitations, we propose a federated learning framework that ensures data privacy, reduces bias, and integrates continuous validation from clinicians to enhance response quality. This approach aims to develop a secure, evidence-based AI chatbot capable of offering trustworthy, empathetic, and bias-reduced mental health support, advancing AI's role in digital mental health care.
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