The new profiling: Algorithms, black boxes, and the failure of anti-discriminatory safeguards in the European Union

Abstract This article argues that with increasingly large databases and computational power, profiling as a key part of security governance is experiencing major changes. Targeting mobile populations in order to enact security via controlling and sifting the good from the bad, profiling techniques accumulate and process personal data. However, as advanced algorithmic analytics enable authorities to make sense of unprecedented amounts of information and derive patterns in a data-driven fashion, the procedures that bring risk into being increasingly differ from those of traditional profiling. While several scholars have dealt with the consequences of black-boxed and invisible algorithmic analytics in terms of privacy and data protection, this article engages the effects of knowledge-generating algorithms on anti-discriminatory safeguards. Using the European-level efforts for the establishment of a Passenger Name Record (PNR) system as an example, and on the theoretical level connecting distinct modes of profiling with Foucauldian thought on governing, the article finds that with pattern-based categorizations in data-driven profiling, safeguards such as the Charter of Fundamental Rights of the European Union or the EU data-protection framework essentially lose their applicability, leading to a diminishing role of the tools of the anti-discrimination framework.