Machine Learning and the Police: Asking the Right Questions

Abstract

Abstract How can we secure an accessible and open democratic debate about police use of predictive analytics when the technology itself is a specialized area of expertise? Police utilize technologies of prediction and automation where the underlying technology is often a machine learning (ML) model. The article argues that important issues concerning ML decision models can be unveiled without detailed knowledge about the learning algorithm, empowering non-ML experts and stakeholders in debates over if, and how to, include them, for example, in the form of predictive policing. Non-ML experts can, and should, review ML models. We provide a ‘toolbox’ of questions about three elements of a decision model that can be fruitfully scrutinized by non-ML experts: the learning data, the learning goal, and constructivism. Showing this room for fruitful criticism can empower non-ML experts and improve democratic accountability when using ML models in policing.