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On Predicting Recidivism: Epistemic Risk, Tradeoffs, and Values in Machine Learning

Published online by Cambridge University Press:  23 July 2020

Justin B. Biddle
Justin B. Biddle*
Affiliation:
School of Public Policy, Georgia Institute of Technology Atlanta, GA, USA
*
Email: justin.biddle@pubpolicy.gatech.edu
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Abstract

Recent scholarship in philosophy of science and technology has shown that scientific and technological decision making are laden with values, including values of a social, political, and/or ethical character. This paper examines the role of value judgments in the design of machine-learning (ML) systems generally and in recidivism-prediction algorithms specifically. Drawing on work on inductive and epistemic risk, the paper argues that ML systems are value laden in ways similar to human decision making, because the development and design of ML systems requires human decisions that involve tradeoffs that reflect values. In many cases, these decisions have significant—and, in some cases, disparate—downstream impacts on human lives. After examining an influential court decision regarding the use of proprietary recidivism-prediction algorithms in criminal sentencing, Wisconsin v. Loomis, the paper provides three recommendations for the use of ML in penal systems.

Keywords

Values in science and technologyartificial intelligencefairnessjusticeequalityethicsrace
Type
Article
Information
Canadian Journal of Philosophy , Volume 52 , Issue 3: Engaging with Science, Values, and Society , April 2022 , pp. 321 - 341
Copyright
© The Author(s) 2020. Published by Canadian Journal of Philosophy

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