Context is all you need: A causal framework for governing bias in clinical AI.

Authors

Alon Gorenshtein, Mahmud Omar, Yiftach Barash, Girish N Nadkarni, Eyal Klang

Published in

PLOS digital health. Volume 5. Issue 11. Pages e0001819. Epub Nov 06, 2026.

Abstract

Clinical AI systems can produce different recommendations when a patient's race, income, housing status, or gender identity changes, but a differential alone does not establish unjustified bias. What separates a clinically justified differential from an unjustified one is not its size or direction but its origin: whether the feature acts through a clinically justified pathway, or marks how patients of that group have been treated. That distinction is causal, and a model trained only to detect statistical association records that a feature carries information but never why. Existing evidence does not establish reliable recovery of this adjudication inside that model; we therefore argue for explicit causal and normative supervision, whether supplied by an external layer, built into training, or both. We set out a six-criterion rubric, four approaches to implementing it, and a two-sided evaluation reporting preserved clinical sensitivity alongside reduced bias.

PMID:
42993732
Bibliographic data and abstract were imported from PubMed on 07 Nov 2026.

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