Counter-Terrorism: The Risk of Performativity in Big Data-Based Mass Surveillance

Abstract

Starting in 2015, the terrorist organization known as the “Islamic State” (IS) experienced a gradual decline in territorial control within Syria and Iraq due to military success on the part of various armies and militias, and it was finally defeated as a political entity in February 2019. As predicted by terrorism experts and announced by the leaders of IS, the gradual fall of the IS led to an increase in terrorist attacks in Europe and the US. To combat these threats, governments and their security services are relying increasingly on big data-based mass surveillance. This paper is concerned with the structure and effectiveness of big data surveillance and strategies in countering Islamist violence. We develop a Bayesian analysis of the effectiveness and performativity of big data-based surveillance and counterterrorism methods, where performativity describes the fact that sometimes the very measures used to prevent terror can themselves contribute to the creation of Islamist violence. Furthermore, we evaluate the ethical dimensions of big data surveillance under the assumption of performativity.