On Predicting Recidivism: Epistemic Risk, Tradeoffs, and Values in Machine Learning. Issue 3 (23rd April 2022)

• Record Type: Journal Article
• Authors: Biddle, Justin B.
• Is Part Of: Canadian journal of philosophy. Volume 52:Issue 3(2022)
• Page Start: 321
• Page End: 341
• Publication Date: 2022-04-23
• Subjects: Values in science and technology -- artificial intelligence -- fairness -- justice -- equality -- ethics -- race; Philosophy -- Periodicals; 105
• DOI: 10.1017/can.2020.27
• Languages: English
• ISSNs: 0045-5091

Abstract

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.