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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.

Vestby, A. & Vestby, J. (2019). Machine Learning and the Police: Asking the Right Questions. Policing: A Journal of Policy and Practice. doi: 10.1093/police/paz035

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Recommended reading by:

May-Britt V.R. Ronnebro, Chairperson Professional Commission