用多智能体系统提升心理健康支持公平性，实时响应情绪状态。
Copewell: A Multi-Agent Swarm Architecture for Equitable Mental Wellness Support
- 多源数据融合评估，减少算法偏见。
- 根据情绪状态动态分配专业智能体，提升干预精准度。
- 兼顾对话与感官干预，适合心理服务资源匮乏人群。
全球近十亿人受心理健康问题影响，但低收入和中等收入国家75%的患者因人力短缺、成本高和污名化而无法获得治疗。现有基于AI的健康解决方案多依赖单一对话界面，用户流失率高，且无法根据动态情绪状态提供可衡量的即时缓解。本文提出Copewell，一种以人为本的多智能体集群架构，旨在通过技术手段扩大心理健康支持的可及性。该架构包含三项创新：(1) 融合自评、生理和情境数据的多源评估框架，以减轻算法偏见；(2) 基于Russell情感环形模型的情绪价值-唤醒度映射，实现用户向专业智能体的智能路由；(3) 结合对话支持与循证感官干预协议的双模式服务。研究还探讨了其社会技术设计考量，包括隐私优先架构、由专门伦理监督智能体保障的嵌入式伦理审查，以及基于从业者参与的设计迭代。早期实践者协作与试点部署为设计优化提供了依据，并指明未来实证评估方向。本工作推动负责任AI发展，证明技术架构可在初期即落实公平与安全原则。
原文摘要 · Abstract (English)
Mental health disorders affect nearly one billion people globally, yet 75% of individuals in low- and middle-income countries receive no treatment due to workforce shortages, cost barriers, and stigma. Current AI-powered wellness solutions predominantly rely on single-mode conversational interfaces that suffer high abandonment rates and fail to provide measurable, immediate relief calibrated to users' dynamic emotional states. This paper presents Copewell, a novel multi-agent swarm system designed to expand access to mental wellness support through human-centered AI principles. Our architecture introduces three technical innovations: (1) a multi-source assessment framework integrating self-reported, physiological, and contextual data to mitigate algorithmic bias; (2) valence-arousal emotion mapping using Russell's Circumplex Model of Affect to route users to specialized AI agents; and (3) dual-mode intervention delivery combining conversational support with evidence-based sensory wellness protocols. We examine the sociotechnical design considerations underlying Copewell's development, including a privacy-first architecture, embedded ethical oversight through a dedicated Ethics Supervisor agent, and participatory design informed by mental health practitioners. Early practitioner engagement and beta deployment inform design decisions and identify directions for future empirical evaluation. This work contributes to responsible AI discourse by demonstrating how technical architecture can operationalize equity and safety principles from inception.
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