From β-Blind to β-Aware AI for Preference-Sensitive Clinical Decisions: Achieving Non-Maleficence

From β-Blind to β-Aware AI for Preference-Sensitive Clinical Decisions: Achieving Non-Maleficence | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 10 April 2026 V2 Latest version Share on From β-Blind to β-Aware AI for Preference-Sensitive Clinical Decisions: Achieving Non-Maleficence Authors : Ogan Gurel 0000-0002-0624-647X [email protected] and James N Weinstein Authors Info & Affiliations https://doi.org/10.22541/au.177499023.34125090/v2 143 views 79 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Large language models (LLMs) increasingly support clinical decisions by synthesizing population-level evidence and estimating average biological treatment effects (α). However, they are not designed to represent the causal contribution of patient choice (β)-a limitation that becomes clinically decisive in preference-sensitive care, where biological differences between options are small. Using the Spine Patient Outcomes Research Trial (SPORT) as an orienting case-a uniquely designed trial in which randomization and patient choice coexisted-we show that preference-mediated effects were clinically meaningful under conditions of near biological equipoise. In such settings, α-centric decision-support systems systematically misrank options because they lack access to β. We formalize this limitation as "α-bias" and "β-blindness," and propose a regime-routed architecture-Detect → Elicit → Recommend → Learn-with explicit deferral rules, neutrality constraints, provenance, and auditable guardrails. When outcomes hinge on choice rather than biological superiority, elicitation is a precondition for recommendation, not an optional refinement. These principles extend beyond medicine to any domain in which outcomes depend on decision-contingent preferences rather than fixed parameters. When outcomes depend on choice rather than biology, improving choices is improving outcomes. A companion paper addresses how β-aware systems may permissibly improve concordance once these safety conditions are satisfied and thus AI decision support moves to βoptimization. Supplementary Material File (5- ai paper beta-aware v32.pdf) Download 572.99 KB Information & Authors Information Version history V1 Version 1 31 March 2026 V2 Version 2 10 April 2026 Copyright This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License Keywords alpha-bias beta-aware beta-blind causal inference clinical decision support systems large language models patient preference elicitation preference-mediated effects preference-sensitive decision making Authors Affiliations Ogan Gurel 0000-0002-0624-647X [email protected] The University of Texas at Arlington View all articles by this author James N Weinstein Microsoft Research View all articles by this author Metrics & Citations Metrics Article Usage 143 views 79 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Ogan Gurel, James N Weinstein. From β-Blind to β-Aware AI for Preference-Sensitive Clinical Decisions: Achieving Non-Maleficence. Authorea . 10 April 2026. DOI: https://doi.org/10.22541/au.177499023.34125090/v2 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . Format Please select one from the list RIS (ProCite, Reference Manager) EndNote BibTex Medlars RefWorks Direct import Tips for downloading citations document.getElementById('citMgrHelpLink').addEventListener('click', function() { popupHelp(this.href); return false; }); $(".js__slcInclude").on("change", function(e){ if ($(this).val() == 'refworks') $('#direct').prop("checked", false); $('#direct').prop("disabled", ($(this).val() == 'refworks')); }); View Options View options PDF View PDF Figures Tables Media Share Share Share article link Copy Link Copied! Copying failed. 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