Researchers propose 'Alignment Plausibility' to assure AI in healthcare
New framework demands three-level safety for AI mental health support
Large language models are increasingly used for mental health support, but they remain products of an attention economy that prioritizes engagement over therapeutic friction. In a new arXiv paper, researchers Gwydion Williams, Sara Zannone, and Bilal A Mateen argue that current safety responses are reactive, focusing on acute harms while ignoring subtler, long-term risks such as dependency, boundary erosion, and amplification of distorted beliefs.
The authors propose 'alignment plausibility' as a structured demonstration that a system's values, training, and oversight together ensure safe outcomes. This mirrors how human clinical practice is assured: explicit value specification from codified clinical ethics, training that embeds those values into the model, and ongoing oversight to detect drift during deployment. The construct draws an analogy to biological plausibility, offering a principled way to argue for or against trust in AI systems for health, aiming to ensure they cause no harm and lead to patient benefit.
- The framework proposes three levels of alignment mirroring human clinical safety: value specification, training, and oversight.
- It targets subtle risks like dependency and boundary erosion in mental health LLMs, not just acute harms.
- Alignment plausibility is presented as a regulatory construct analogous to biological plausibility in medicine.
Why It Matters
Provides a principled, regulatory framework to trust AI in healthcare, addressing long-term patient safety risks.