Canadian Journal of Philosophy:1-21 (forthcoming)

Authors
Justin B. Biddle
Georgia Institute of Technology
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 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.
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DOI 10.1017/can.2020.27
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References found in this work BETA

Inductive Risk and Values in Science.Heather Douglas - 2000 - Philosophy of Science 67 (4):559-579.
Bias and Values in Scientific Research.Torsten Wilholt - 2009 - Studies in History and Philosophy of Science Part A 40 (1):92-101.

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Citations of this work BETA

A Taxonomy of Transparency in Science.Kevin C. Elliott - forthcoming - Canadian Journal of Philosophy:1-14.
The New Worries About Science.Janet A. Kourany - forthcoming - Canadian Journal of Philosophy:1-19.

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