Bosco Garcia

Abstract. Large language models (LLMs) are increasingly proposed as scalable solutions to the global mental health crisis. But their deployment in psychiatric contexts raises a distinctive ethical concern: the problem of atypicality. Because LLMs generate outputs based on population-level statistical regularities, their responses—while typically appropriate for general users—may be dangerously inappropriate when interpreted by psychiatric patients, who often exhibit atypical cognitive or interpretive patterns. We argue that standard mitigation strategies, such as prompt engineering or fine-tuning, are insufficient to resolve this structural risk. Instead, we propose dynamic contextual certification (DCC): a staged, reversible and context-sensitive framework for deploying LLMs in psychiatry, inspired by clinical translation and dynamic safety models from artificial intelligence governance. DCC reframes chatbot deployment as an ongoing epistemic and ethical process that prioritises interpretive safety over static performance benchmarks. Atypicality, we argue, cannot be eliminated—but it can, and must, be proactively managed.

Abstract. We argue that the problem of the flow of time is a special problem, one unlike many other challenges in reconciling temporal phenomena with time in physics. After clarifying this point, we develop a Humean account of the flow of time according to which the psychological stream of experience explains why we think the world is tensed. On our account, the flow of time is not all inferential, as the invitation to think time flows comes from the deepest aspects of human experience, making it an offer we cannot refuse.