The Surveillance State and Counter-terrorism
The Surveillance State and Counter-terrorism
The article presents findings on the effectiveness of mass surveillance technology. One of the approaches to determining success is the number of attacks thwarted and lives saved. However, “American intelligence officials have emphasized that counting success stories is not a measure with which one should evaluate a program’s effectiveness” (p. 93). Additionally, despite arguments of the effectiveness and otherwise, there is no clear measure of surveillance effectiveness so far with many measures still insufficient. It has been argued that “the purpose of intelligence is to inform policy makers, to improve the quality of their decision making. Measuring the impact of strategic intelligence on the decision-making process it informs is difficult” (p. 100). (M, 2018)
This book chapter argues for reducing information sharing on social media to reduce surveillance of security apparatus. The chapter advises military and other security personnel to adopt a ‘need to care’ approach as opposed to ‘need to share’ in their social media interactions. The need-to-share approach has been linked to the 9/11 terror attacks: “‘Need to know’ had prevented internal sharing of information. Because of the 9/11 attacks, there was a deliberate internal shift in the ways that sensitive information was to be treated. ‘Need to know’ was no longer the default. The US shifted its position from ‘need to know’ to ‘need to share’” (p. 159). In the ‘need to care’ approach, “we can see that information, even if it is accessible and thus not secret, can and should be considered private and so ought to be treated carefully” (p. 165). (Henschke, 2021) The article addresses law enforcement and intelligence officers whose personal information can be subject to surveillance and hence is relevant to counter-terrorism efforts.
Parra-Arnau, J., & Castelluccia, C. (2018). On the cost-effectiveness of mass surveillance. IEEE Access, 6, 46538-46557. https://doi.org/10.1109/ACCESS.2018.2866310
Mass surveillance systems have been deployed and laws advanced to allow governments to spy on their people to flag terrorists and reduce risk of terror attacks. However, the cost-effectiveness of these technologies is in question. Particularly, “it is questionable whether this solution is cost-efficient, since it is expected, according to the false positive paradox, that many of the resources will also be spent analyzing the data of innocent people” (p. 46538). Surveillance systems, although used to identify some terror suspects, have been ineffective in many ways. “For less ambitious objectives, surveillance is not cost-effective, either: in the period 2013-2016 in Spain, and also in the U.K., at least 50% of terrorist would have been captured at the cost of inspecting a suspect list of around 1 million people” (p. 46555). The numbers show that such a huge suspect list may not be feasible to follow and investigate. Even if it was possible, only half of terror attacks would be prevented.
Sharma, S., & Nijjar, J. (2018). The racialized surveillant assemblage: Islam and the fear of terrorism. Popular Communication, 16(1), 72-85. https://doi.org/10.1080/15405702.2017.1412441
The article argues that modern surveillance systems used by Western governments have been based on racialized conceptions of radicalism and terrorism. As a result, the programs are not effective in actually catching the real terrorists. Racialized surveillance and policing has been experienced in that “Muslims, whether of migrant status or citizens of Western nations, have been subjected to intense and degrading forms of counter-terrorism policing and punishment that include profiling, tracking, arrest, detention without a right to a fair trial, and rendition” (pp. 73-74). Moreover, research has shown that mass-surveillance is actually ineffective given the small percentage of actual terrorists. It is argued that “surveillance at a mass-scale may be only effective if the base rate is relatively high. For example, identifying real terrorists (by achieving a probability of near 1), necessitates fatuous assumptions, such as, there being one million actual terrorists in the USA, along with an exceptional 90% accurate detection rate” (p. 79). (Nijjar, 2018)These figures reveal that mass-surveillance systems may be guided by racialized policing of communities of color and the ‘racialized other’ as opposed to evidence of the technology’s effectiveness in counterterrorism.
The authors of this article identify the challenges of using machine learning for counter-terrorism. They identify that terrorism patterns are very diverse, creating a challenge for such algorithms. Isolated digital footprints of terrorists make “the training of machine learning algorithms more difficult as the uniqueness of many attacks increases the probability of inadequate training and consequently inaccurate algorithms” (p. 2979). One of the justifications of using these mass surveillance algorithms has been that they focus on metadata and not the data of target populations. However, “it has been shown that machine-learning algorithms are able to infer much more sensitive information from the acquired metadata, allowing for a much deeper look into people’s lives” (p. 2981). (Verhelst, 2020)This means using mass surveillance metadata, the people operating those algorithms can learn a lot of details including identity and communication information can be intercepted. Therefore, the rationale provided for mass surveillance algorithms is flawed.
References
Henschke, A. (2021). Counter Terrorism. Retrieved from https://books.google.com/books?hl=en&lr=&id=sHA4EA…
M, C. (2018). The effectiveness of SUrvailance technology. Retrieved from https://doi.org/10.1080/01972243.2017.1414721
Nijjar, J. (2018). The racialized surveillant assemblage: Islam and the fear of terrorism. Retrieved from https://www.tandfonline.com/doi/abs/10.1080/154057…
Verhelst, H. (2020). Machine learning against terrorism. Retrieved from https://link.springer.com/article/10.1007/s11948-0…
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