From β-Aware to β-Optimizing AI for Preference-Sensitive Clinical Decisions: Achieving Beneficence

Abstract

AI decision-support systems are increasingly deployed in clinical settings where biological treatment effects are small and outcomes depend materially on patient choice. In prior work, we showed that such preference-sensitive decisions reveal a correctness failure of prediction-centric AI: systems trained to rank options by average biological effects act illegitimately under near equipoise unless they detect preference-sensitive regimes, defer premature ranking, and elicit patient preferences neutrally. That β-aware framework secures non-maleficence by enforcing restraint where biological superiority cannot decide. This paper addresses the downstream question: once safety is assured, what forms of optimization are causally, technically, and ethically admissible? We introduce βoptimization, a constrained framework for AI decision support in preference-sensitive care. Rather than inferring or shaping preferences, β-optimization treats explicitly elicited patient values and feasibility constraints as measured inputs and seeks improvement by maximizing concordance between evidence and what patients value and can sustain. We formalize concordance-based objectives appropriate to observer-dependent decision regimes, specify architectural constraints that preserve neutrality, deferral, and causal separation, and show how large language model-based systems can condition recommendations on elicited preferences without exercising illegitimate authority. We further propose concordance-first evaluation metrics-epistemic, practical, and decisional-for settings where prediction accuracy is ill-posed. Together, β-awareness and βoptimization define a regime-aware theory of AI decision support for preference-sensitive domains: first, do not decide when biology cannot decide; then, once preferences are measured, help decisions succeed-without shaping them.

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