PRECOBS: theory- and correlation-based construction of crime futures
Paper short abstract:
Predictive policing is not as revolutionary as commonly depicted. Rather, corresponding software uses quite conventional sources of information. I will illustrate this by drawing on empirical data of the development and utilization of the crime prediction software 'PRECOBS'.
Paper long abstract:
For several years now, digital technologies mainly consisting of methods of data mining and algorithmic decision making, commonly referred to as 'predictive policing', are employed by police departments throughout the world. When scrutinizing practices of predictive policing in detail, it becomes apparent that they are not as revolutionary as commonly depicted. Rather, such software tools mostly draw on criminological theories (e.g., rational-choice approaches) and a probabilistic approach to police data, which represent quite conventional sources of information. Hence, predictive policing actually does not foresee the future but (solely) helps police to identify potential future high-risk areas, by using historic (crime) data, interpreting these by using ordinary theories of crime and by correlating these insights again with present crime situation information, eventually transferring them algorithmically into actionable knowledge. In my presentation, I want to describe the human-machine-interaction of predictive policing in more detail, especially with reference to the embodied algorithmic values and norms as well as to the utilized data material. I will reconstruct the sociotechnical construction of crime futures by drawing on empirical data of the development and utilization of the crime prediction software 'PRECOBS', the leading crime prediction product in German-speaking countries. Another point I aim to stress: Especially because of their enablement and/or simplification of crime data analysis in general, predictive policing is unlikely to be a short-dated phenomenon but a strategy which is presumably to be an important part of police work in the future, ultimately giving rise to the 'datafication' of police work.
The power of correlation and the promises of auto-management. On the epistemological and societal dimension of data-based algorithms