Ethics-by-design: efficient, fair and inclusive resource allocation using machine learning
Author(s)
Papalexopoulos, Theodore P; Bertsimas, Dimitris; Cohen, I Glenn; Goff, Rebecca R; Stewart, Darren E; Trichakis, Nikolaos; ... Show more Show less
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<jats:title>Abstract</jats:title>
<jats:p>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.</jats:p>
Date issued
2022-01-01Department
Massachusetts Institute of Technology. Operations Research CenterJournal
Journal of Law and the Biosciences
Publisher
Oxford University Press (OUP)
Citation
Papalexopoulos, Theodore P, Bertsimas, Dimitris, Cohen, I Glenn, Goff, Rebecca R, Stewart, Darren E et al. 2022. "Ethics-by-design: efficient, fair and inclusive resource allocation using machine learning." Journal of Law and the Biosciences, 9 (1).
Version: Final published version