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With a rising number of law enforcement agencies facing budgetary cuts, many turn to data science in an attempt to maintain service quality with fewer resources. A number of thus adopted solutions–including facial recognition, predictive policing, and risk assessments–have been contested by researchers and journalists alike. Yet comparatively little research is done at the strategy level, which determines where data science will be deployed in the first place. In this study, we interview 40 practitioners from Police Scotland, investigating what they believe to be crucial to successfully incorporate data science in their ways of working. Bucking the external trend, the participants distanced themselves from tools like facial recognition and risk assessment. Instead of focusing on individual use-cases, their primary concerns for the future were around (i) systemic issues around data is collection and use, (ii) goal misalignment between leadership and operational levels, (iii) the fear that datafication may undervalue important aspects of policing, and (iv) appropriate ways of interaction between data science teams and operational officers. Alongside the insights particular to Police Scotland, our work reaffirms how participatory approaches can go beyond the technical, and uncover structural and political barriers to success.
Kearney et al. (Mon,) studied this question.