Alignment Plausibility: A New AI Assurance Standard for Healthcare

This paper introduces "alignment plausibility" as a novel standard for assuring the safety and ethical deployment of AI in healthcare, particularly focusing on Large Language Models (LLMs) used for mental health support. The authors contend that current safety responses to LLMs are often reactive, addressing immediate harms while neglecting subtle, long-term risks like dependency or the amplification of distorted beliefs. To structurally ensure LLM safety, the paper proposes a three-tiered alignment framework, drawing parallels with how human clinical practice is assured. This framework includes: first, explicit specification of values grounded in codified clinical normative commitments; second, training regimes that embed these values directly into the model; and third, continuous oversight mechanisms during deployment to detect drift and long-term harms, akin to clinical supervision. Alignment plausibility, therefore, serves as a structured demonstration of a system's consistency across its values, training, and oversight, aiming to build principled trust in AI systems for positive health outcomes and harm prevention.