Chain of Risks Evaluation (CORE): A framework for safer large language models in public mental health

Large language models (LLMs) have gained significant attention for their capabilities in natural language understanding and generation. However, their widespread adoption potentially raises public mental health concerns, including issues related to inequity, stigma, dependence, medical risks, and security threats. This review aims to offer a perspective within the actor-network framework, exploring the technical architectures, linguistic dynamics, and psychological effects underlying human-LLMs interactions. Based on this theoretical foundation, we propose four categories of risks, presenting increasing challenges in identification and mitigation: universal, context-specific, user-specific, and user-context-specific risks. Correspondingly, we introduce CORE: Chain of Risk Evaluation, a structured conceptual framework for assessing and mitigating the risks associated with LLMs in public mental health contexts. Our approach suggests viewing the development of responsible LLMs as a continuum from technical to public efforts. We summarize technical approaches and potential contributions from mental health practitioners that could help evaluate and regulate risks in human-LLMs interactions. We propose that mental health practitioners could play a crucial role in this emerging field by collaborating with LLMs developers, conducting empirical studies to better understand the psychological impacts on human-LLMs interactions, developing guidelines for LLMs use in mental health contexts, and engaging in public education.