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 nonML 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.