A Left Realist Critique of the Political Value of Adopting Machine Learning Systems in Criminal Justice

Published in ECML 2020 Workshop on Data Science for Social Good, 2020

In this paper we discuss the political value of the decision to adopt machine learning in the field of criminal justice. While a lively discussion in the community focuses on the issue of the social fairness of machine learning systems, we suggest that another relevant aspect of this debate concerns the political implications of the decision of using machine learning systems. Relying on the theory of Left realism, we argue that, from several points of view, modern supervised learning systems, broadly defined as functional learned systems for decision making, fit into an approach to crime that is close to the law and order stance. Far from offering a political judgment of value, the aim of the paper is to raise awareness about the potential implicit, and often overlooked, political assumptions and political values that may be undergirding a decision that is apparently purely technical.