用心理领域专用规则训练安全聊天机器人，避免误诊和情绪恶化。
Domain-Specific Constitutional AI: Enhancing Safety in LLM-Powered Mental Health Chatbots
- 用心理健康原则定制宪法式AI，让模型遵守专业诊疗规范
- 针对情绪危机场景提升判断准确率，降低误判风险
- 适合开发医疗级心理聊天机器人，尤其在资源不足地区
心理健康应用已成为计算健康领域的重要方向，受全球精神疾病发病率上升、AI融入心理护理以及欠发达地区对可扩展解决方案的需求推动。此类应用包括治疗聊天机器人、危机检测与健康平台，处理敏感数据，需超越通用安全机制的特殊保障，因涉及情绪脆弱性、误诊或症状加剧等风险，须精准管理脆弱状态以避免自伤或信任丧失等严重后果。尽管已有AI安全进展，通用防护手段难以应对心理健康特有挑战：如危机干预准确性以防止事态升级、治疗指南遵循性以避免错误信息传播、资源受限环境下的扩展性限制，以及对复杂对话中细微情绪信号的捕捉能力不足。本文提出一种基于领域特定心理健康原则的宪法式AI训练方法，构建适用于计算心理健康应用的安全、领域适配型CAI系统。
原文摘要 · Abstract (English)
Mental health applications have emerged as a critical area in computational health, driven by rising global rates of mental illness, the integration of AI in psychological care, and the need for scalable solutions in underserved communities. These include therapy chatbots, crisis detection, and wellness platforms handling sensitive data, requiring specialized AI safety beyond general safeguards due to emotional vulnerability, risks like misdiagnosis or symptom exacerbation, and precise management of vulnerable states to avoid severe outcomes such as self-harm or loss of trust. Despite AI safety advances, general safeguards inadequately address mental health-specific challenges, including crisis intervention accuracy to avert escalations, therapeutic guideline adherence to prevent misinformation, scale limitations in resource-constrained settings, and adaptation to nuanced dialogues where generics may introduce biases or miss distress signals. We introduce an approach to apply Constitutional AI training with domain-specific mental health principles for safe, domain-adapted CAI systems in computational mental health applications.
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