Algorithmic Suspicion and Big Data Searches

Abstract

This chapter assesses how existing legal frameworks are anachronistic and inadequate for governing police work in the age of big data. There is now a burgeoning body of legal scholarship analyzing the legal implications of big data policing, yet it is largely theoretical. By grounding legal debates about police use of data in empirical detail, the chapter makes the case that basic legal principles are inadequate not simply because they are anachronistic, but also because the legal debates are too narrow. There are a number of ways legal frameworks are overlooking the social side of big data. First, the way the conceptual categories that underpin legal doctrine—like individualized suspicion—are deployed and organized to make normative assessments do not reflect how decision-making plays out on the ground. Second, police are not simply scaling up data collection in the digital age; rather, different kinds of data are being produced. Despite the fact that there is a difference in kind—rather than just degree—old legal doctrine is still being laid on top of these data. Third, relying on extant legal mechanisms like the exclusionary rule involves using what is meant to be a check on state power at one point in time and space, whereas data is fundamentally social and, as such, has a life course. Fourth, unfettered big data policing creates new opportunities for information asymmetries and can threaten due process through a practice called “parallel construction.”