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.

最近的科技哲学研究表明，科技决策充满了价值观，包括社会、政治和/或伦理特征的价值观。本文研究了价值判断在机器学习 (ML) 系统设计中的作用，特别是在累犯预测算法中的作用。该论文利用归纳和认知风险方面的工作，认为 ML 系统以类似于人类决策的方式充满价值，因为 ML 系统的开发和设计需要人类决策，其中涉及反映价值的权衡。在许多情况下，这些决定对人类生活产生了重大的——在某些情况下，甚至是完全不同的——下游影响。Wisconsin v. Loomis，该论文为在刑罚系统中使用 ML 提供了三项建议。