Ethics-by-design: efficient, fair and inclusive resource allocation using machine learning. Issue 1 (28th April 2022)

• Record Type: Journal Article
• Authors: Papalexopoulos, Theodore P; Bertsimas, Dimitris; Cohen, I Glenn; Goff, Rebecca R; Stewart, Darren E; Trichakis, Nikolaos
• Is Part Of: Journal of law and the biosciences. Volume 9:Issue 1(2022)
• Publication Date: 2022-04-28
• Subjects: Analytics -- ethics by design -- machine learning -- organ allocation -- organ transplantation -- resource allocation; Life sciences -- Law and legislation -- Periodicals; Science and law -- Periodicals; Law and biology -- Periodicals; Biotechnology -- Law and legislation -- Periodicals; Bioethics -- Periodicals; 344.0957
• DOI: 10.1093/jlb/lsac012
• Languages: English
• ISSNs: 2053-9711

Abstract

Abstract: The distribution of crucial medical goods and services in conditions of scarcity is among the most important, albeit contested, areas of public policy development. Policymakers must strike a balance between multiple efficiency and fairness objectives, while reconciling disparate value judgments from a diverse set of stakeholders. We present a general framework for combining ethical theory, data modeling, and stakeholder input in this process and illustrate through a case study on designing organ transplant allocation policies. We develop a novel analytical tool, based on machine learning and optimization, designed to facilitate efficient and wide-ranging exploration of policy outcomes across multiple objectives. Such a tool enables all stakeholders, regardless of their technical expertise, to more effectively engage in the policymaking process by developing evidence-based value judgments based on relevant tradeoffs.