Alignment Plausibility: A New Standard for Assuring AI in Healthcare
Gwydion Williams, Sara Zannone, Bilal A Mateen
Why It Matters
What makes this one worth your time
As AI systems increasingly provide mental health support, ensuring their alignment with clinical safety standards is crucial to prevent harm and ensure patient benefit.
Proposes 'alignment plausibility' to ensure AI safety in healthcare by mirroring clinical practice standards.
Summary
The paper proposes a framework called 'alignment plausibility' for ensuring the safety of large language models in healthcare by aligning their operations with the normative commitments of clinical practice. It suggests a three-level approach involving explicit value specification, training, and oversight to ensure these models are aligned with safe and positive health outcomes.
Key contributions
- Introduction of 'alignment plausibility' as a regulatory construct for AI in healthcare.
- Proposal of a three-level alignment framework mirroring human clinical practice.
Notable insights
- The concept of 'alignment plausibility' draws an analogy to biological plausibility, suggesting a structured way to argue for AI safety in healthcare.
Possible limitations
- Not stated in the abstract
Abstract
arXiv:2607.07766v1 Announce Type: new 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.