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.

翻译：心理健康应用已成为计算健康领域的关键方向，这源于全球精神疾病发病率上升、人工智能在心理护理中的整合，以及服务不足社区对可扩展解决方案的需求。此类应用包括治疗聊天机器人、危机检测和处理敏感数据的健康平台，由于涉及情感脆弱性、误诊或症状加剧等风险，以及对脆弱状态的精准管理以避免自伤或信任丧失等严重后果，它们需要超越通用保障措施的专业化人工智能安全机制。尽管人工智能安全领域已取得进展，但通用保障措施仍不足以应对心理健康特有的挑战，包括：避免危机升级所需的干预准确性、防止错误信息的治疗指南遵循、资源有限环境下的规模限制，以及对细微对话的适应能力——通用模型可能在此类对话中引入偏见或遗漏痛苦信号。我们提出一种方法，将宪法人工智能训练与领域特定的心理健康原则相结合，为计算心理健康应用构建安全、领域适应的CAI系统。