Alignment Plausibility: A New Standard for Assuring AI in Healthcare
Large language models used for mental health support are influenced by the attention economy, prioritizing engagement over effective therapy. This paper proposes a three-level alignment framework (value specification, training, oversight) and introduces 'alignment plausibility' as a regulatory standard, analogous to biological plausibility, to demonstrate that AI systems are aligned with safe, beneficial health outcomes.
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[Submitted on 8 Jul 2026]
Title:Alignment Plausibility: A New Standard for Assuring AI in Healthcare
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Abstract: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.
Comments: 8 pages, 1 figure
Subjects:
Artificial Intelligence (cs.AI)
Cite as: arXiv:2607.07766 [cs.AI]
(or arXiv:2607.07766v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2607.07766
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Gwydion Williams [view email] [v1] Wed, 8 Jul 2026 15:39:10 UTC (664 KB)
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