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AbstractHow 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.

AbstractThis article analyses ambiguity and complexity in proactive policing practices and identifies the paradox that despite the focus on increasing proactivity, police work remains strongly reactive. Drawing on a set of Norwegian case studies of policing in different domains, the article shows how under an overarching objective of 'combating crime', the distinctions between non-coercive (mainly proactive) forms of prevention or (mainly reactive) methods such as investigation or intelligence are seen as unimportant. This creates a demand for professionals working across different crime types, leading to a shift towards high policing in everyday life and tension between experts and generalists. Other, unintended consequences include a fragmentation of tasks and a more general and abstract way of policing. The result is pluralization and multiagency partnership strategies, where the police conduct high-policing tasks and external actors conduct low-policing tasks. These findings point to the emergence of new forms of hybrid of policing.