AI and Suicide Prevention: A Cross-Sector Primer
For AI labs, mental health practitioners, and policymakers, it provides a common reference point to address the lack of clinical validation and oversight in AI chatbots used for suicide prevention.
This primer, developed from a 2026 multistakeholder workshop, reviews clinical best practices and frontier AI systems' detection/response to suicide and self-injury queries, mapping challenges across model, product, and policy layers to highlight priority areas for cross-industry alignment.
AI chatbots already function as de facto mental health support tools for millions of people, including people in crisis. Yet, they lack the clinical validation, shared standards, and coordinated oversight that their societal role demands. This primer was developed in conjunction with a multistakeholder workshop hosted by Partnership on AI in 2026, convening AI labs, mental health practitioners, people with lived experience, and policymakers, to provide a common cross-sector reference point for the current state of the field of AI and suicide prevention. It begins with an overview of clinical best practices, then turns to how frontier AI systems (as of winter 2026) detect and respond to suicide and non-suicidal self-injury (NSSI) queries. Together, these provide insight into what it would take to design and implement AI tools that not only better prevent suicide and NSSI, but also promote overall well-being. Drawing on clinical literature, publicly available AI lab policies, an emerging landscape of evaluation frameworks, and conversations with leaders across the AI and mental health fields, we map challenges posed by general-purpose AI chatbots for mental health across model, product, and policy layers, ultimately highlighting priority areas where cross-industry alignment is both urgently needed and achievable.