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.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

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11 Paul Staniland, Networks of Rebellion: Explaining Insurgent Cohesion and Collapse (Cornell University Press, 2018).

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22 See note 19 above.

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24 Peter L. Bergen, Holy War, Inc. Inside the Secret World of Osama Bin Laden (New York, NY: The Free Press, 2001).

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27 See note 5 above.

28 See note 6 above.

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37 Thomas Maurer, “Die Pluralität der Radikalisierung - Eine systemische Analyse der Theorieansätze zur Radikalisierungsforschung,” Journal for Deradicalization 13 (2017): 49–100.

38 Ibid.

39 Charles Kurzman, David Schanzer, and Ebrahim Moosa, “Muslim American Terrorism Since 9/11: Why So Rare?” The Muslim World 101 (2011): 464–83. doi:10.1111/j.1478-1913.2011.01388.x.

40 H. Grassegger & M. Krogerus, “Ich habe nur gezeigt, dass es die Bombe gibt,” Das Magazin, July 12, 2018, https://www.dasmagazin.ch/2016/12/03/ich-habe-nur-gezeigt-dass-es-die-bombe-gibt/.

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42 Bundeskriminalamt [BKA], “Interview: ‘Die Gefährderzahl hat sich verfünffacht’.” December 19, 2018, https://www.bka.de/DE/Presse/Interviews/2018/181219_InterviewMuenchTagesspiegel.html.

43 Bart Schuurman, “Research on Terrorism, 2007–2016: A Review of Data, Methods, and Authorship,” Terrorism and Political Violence 32, no. 5 (2020): 1011–26.

44 As of 2018 the BKA reported 760 potential terrorists in Germany. See Daniel Kahneman, Thinking, Fast and Slow (New York, NY: Farrah, Straus and Giroux, 2011).

45 It is a well-researched phenomenon that even with rich datasets and high accuracy; predictions on the individual level tend to be faulty, especially when dealing with low base rates. This is a common issue in many areas where statistical predictions are used, e.g., with narrow target groups in digital marketing. See Kahneman for “base rate fallacy”, respectively “base rate neglect”. See Mathias Leese, “Seeing Futures’—Politics of Visuality and Affect,” In Algorithmic Life: Calculative Devices in the Age of Big Data, ed. Louise Amoore and Volha Piotukh (Milton Park; New York, NY: Routledge, 2016), 148–64.

46 David A. Lake, “Rational Extremism: Understanding Terrorism in the Twenty-First Century Dialogue,” International Organization 1 (2002): 15–29.

47 Tobias Matzner, “Why Privacy is not Enough Privacy in the Context of ‘Ubiquitous Computing’ and ‘Big Data’,” Journal of Information, Communication & Ethics in Society 12, no. 2 (2014): 93–106.

48 See note 43 above.

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51 Nassim Nicholas Taleb, The Black Swan: The Impact of the Highly Improbable (New York, NY: Random House, 2007).

52 See note 50 above.