Semantic Scholar Open Access 2020 65 sitasi

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

J. Biddle

Abstrak

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.

Topik & Kata Kunci

Psychology

Penulis (1)

J. Biddle

Format Sitasi

APA MLA BibTeX

Biddle, J. (2020). On Predicting Recidivism: Epistemic Risk, Tradeoffs, and Values in Machine Learning. https://doi.org/10.1017/can.2020.27

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Informasi Jurnal

Tahun Terbit: 2020
Bahasa: en
Total Sitasi: 65×
Sumber Database: Semantic Scholar
DOI: 10.1017/can.2020.27
Akses: Open Access ✓