Ethics-by-design: efficient, fair and inclusive resource allocation using machine learning
Name
lsac012.pdf
Description
Published version
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610.42 KB
Format
Adobe PDF
Checksum (MD5)
f3c631cd62d3e5833d56932412d436f5
Author(s)
Papalexopoulos, Theodore P
Bertsimas, Dimitris
Cohen, I Glenn
Goff, Rebecca R
Stewart, Darren E
Trichakis, Nikolaos
Date Issued
January 1, 2022
Journal
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
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.
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.
MIT Department
Massachusetts Institute of Technology. Operations Research Center
Terms of Use
Creative Commons Attribution-NonCommercial-NoDerivs License
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DOI of Published Version
https://doi.org/10.1093/jlb/lsac012
