Prediction as Extraction of Discretion

Abstract

I argue that data-driven predictions work primarily as instruments for systematic extraction of discretionary power - the practical capacity to make everyday decisions and define one's situation. This extractive relation reprises a long historical pattern, in which new methods of producing knowledge generate a redistribution of epistemic power: who declares what kind of truth about me, to count for what kinds of decisions? I argue that prediction as extraction of discretion is normal and fundamental to the technology, rather than isolated cases of bias or error. Synthesising critical observations across anthropology, history of technology and critical data studies, the paper demonstrates this dynamic in two contemporary domains: (1) crime and policing demonstrates how predictive systems are extractive by design. Rather than neutral models led astray by garbage data, pre-existing interests thoroughly shape how prediction conceives of its object, its measures, and most importantly, what it does not measure and in doing so devalues. (2) I then examine the prediction of productivity in the long tradition of extracting discretion as a means to extract labour power. Making human behaviour more predictable for the client of prediction (the manager, the corporation, the police officer) often means making life and work more unpredictable for the target of prediction (the employee, the applicant, the citizen).