Learning from Disagreement: Clinician Overrides as Implicit Preference Signals for Clinical AI in Value-Based Care

We reframe clinician overrides of clinical AI recommendations as implicit preference data - the same signal structure exploited by reinforcement learning from human feedback (RLHF), but richer: the annotator is a domain expert, the alternatives carry real consequences, and downstream outcomes are observable. We present a formal framework extending standard preference learning with three contributions: a five-category override taxonomy mapping override types to distinct model update targets; a pr

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ethics.ai (30 April 2026), “Learning from Disagreement: Clinician Overrides as Implicit Preference Signals for Clinical AI in Value-Based Care,” evidence record 5156, https://ethics.ai/record/5156 (originally published by arXiv).

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