Alignment Plausibility: A New Standard for Assuring AI in Healthcare
06:00 · July 11, 2026 · arXiv cs.AI RSS
Large language models (LLMs) have become significant providers of mental health support, yet they remain products of an attention economy whose operational and commercial targets favour sustained engagement over the friction that effective psychological support often requires. Developers' safety responses have been largely reactive, addressing the most visible and acute harms while subtler, longer-term patterns of risk (e.g., dependency, boundary erosion, the amplification of distorted beliefs) receive less attention. We contend that making LLMs structurally safe requires alignment organised at three levels that mirror how society assures the safety of human clinical practice: 1) explicit value specification grounded in the codified normative commitments of clinical practice; 2) training that embeds those values in the model; and 3) oversight that detects drift and longer-term harm during deployment, much as clinical supervision does for human practice. Organising alignment in this way yields a construct we call alignment plausibility - a structured demonstration that a system's values, training regime, and oversight mechanisms are together consistent with safe and positive outcomes. We propose alignment plausibility as a regulatory construct (by drawing analogy to the established construct of biological plausibility) for AI in health: a principled way to argue for, or against, trust that systems are aligned to positive health outcomes, will cause no harm even where capable of doing so, and will ultimately lead to patient benefit.
Summary
Large language models are already delivering substantial volumes of mental health support, yet their design incentives remain rooted in an attention economy that rewards prolonged interaction rather than the deliberate friction often required for effective psychological care. Current safety measures tend to address only the most immediate and visible harms, leaving subtler, cumulative risks such as user dependency, erosion of professional boundaries, and reinforcement of distorted beliefs largely unexamined.
To address these gaps, the authors argue that structural safety for clinical LLMs requires alignment organised at three levels that parallel existing safeguards for human practitioners. The first level demands explicit specification of values drawn from the codified normative commitments of clinical practice. The second level embeds those values through targeted training regimes. The third level introduces ongoing oversight mechanisms capable of detecting value drift and longer-term harms once models are deployed, analogous to clinical supervision.
Organising alignment across these three layers produces a construct termed alignment plausibility: a structured demonstration that a system’s declared values, training procedures, and post-deployment oversight are jointly consistent with safe, beneficial outcomes. By analogy with the established regulatory notion of biological plausibility, alignment plausibility is proposed as a formal criterion that regulators and developers can use to justify, or withhold, trust that an LLM will support positive health results without causing harm.
Why it matters
This research is highly relevant to the Dutch AI market's strong emphasis on ethical, transparent, and regulated AI, particularly in high-risk sectors like healthcare. It provides a structured framework that aligns well with EU AI Act compliance, offering researchers and policymakers a principled approach to AI safety and oversight.
