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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 preference formulation conditioned on patient state s, organizational context c, and clinician capability kappa, where kappa decomposes into execution capability kappa-exec and alignment capability kappa-align; and a dual learning architecture that jointly trains a reward model and a capability model via alternating optimization, preventing a failure mode we term suppression bias-the systematic suppression of correct-but-difficult recommen","title":"Learning from Disagreement: Clinician Overrides as Implicit Preference Signals for Clinical AI in Value-Based Care","url":"https://arxiv.org/abs/2604.28010","vendor":"arxiv_cs_ai"},"summary":"arXiv:2604.28010v2 Announce Type: replace-cross \nAbstract: 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. 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