Possibilities of Applying the Chinese Social Credit System to Combat Terrorism
DOI:
https://doi.org/10.12797/Politeja.17.2020.69.09Keywords:
Social Credit System, terrorism, international security, Big Data, ChinaAbstract
2011 saw the start of the pilot phase of the Social Credit System. The societies of democratic states interpreted it as an extreme example of human rights violation. In turn, what is usually forgotten is that the SCS is not the first citizen assessment system, because similar systems have been successfully functioning since 1960s in democratic countries. Scientific analyses of SCS operations are usually limited to its negative consequences. There are fewer attempts by governments of democratic states to assess the use of elements of SCS and algorithmic data analysis, for example in order to increase the level of security of citizens. As a result, this article presents the research hypothesis that elements of the SCS may be successfully applied also in democratic states for the purpose of more effective combating of terrorism. With modern methods of analyzing Big Data sets, it is possible, for example, to accelerate recognition of terrorist networks, support identification of sources of radicalization in online communities and increase the effectiveness of anti-terrorist strategies in order to protect citizens against contemporary terrorist threats. For such a system to be as effective as possible, it should take over some assumptions of the SCS which are explained in this article: Firstly, it should be centralized and controlled by the state. Secondly, the information obtained and processed should be used solely for the purposes of the state security policy, i.e. in the scope smaller than in the case of China. Thirdly, the data should be obtained from multiple sources, public and private ones, in order to increase the accuracy of predictions. Fourthly, the violation of the principles of social coexistence might result in specific penalties, and compliance therewith – in rewards.
Downloads
References
CONTEST The United Kingdom’s Strategy for Countering Terrorism, June 2018, at <https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/716907/140618_CCS207_CCS0218929798-1_CONTEST_3.0_WEB.pdf>.
Creemers R., “China’s Social Credit System: An Evolving Practice of Control”, 9 May 2018, SSRN Electronic Journal, at <https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3175792>.
Cukier K., Mayer-Schonberger V., Big data. Rewolucja, która zmieni nasze myślenie, pracę i życie, Warszawa 2014.
Dziwisz D., “Algorytmiczna przyszłość – ucieczka od wolności ku ‘opcji domyślnej’”, in P. Szymczyk, K. Maciąg (eds.), Człowiek a technologia cyfrowa – przegląd aktualnych doniesień, Lublin 2018.
“Enabling Distributed Security in Cyberspace. Building a Healthy and Resilient Cyber Ecosystem with Automated Collective Action”, Department of Homeland Security, 23 March 2011, at <https://www.dhs.gov/xlibrary/assets/nppd-cyber-ecosystem-white-paper-03-23-2011.pdf>.
“Global Terrorism Index 2017. Measuring and Understanding the Impact of Terrorism”, Institute for Economics and Peace, at <http://visionofhumanity.org/app/uploads/2017/11/Global-Terrorism-Index-2017.pdf>, 8 May 2019.
Home Secretary Announces New Counter-terrorism Strategy, 4 June 2018, at <https://www.gov.uk/government/speeches/home-secretary-announces-new-counter-terrorism-strategy>.
Inkster N., China’s Cyber Power, London 2016, https://doi.org/10.4324/9780429031625.
Jussawalla M., Taylor R., Information Technology Parks of the Asia Pacific: Lessons for the Regional Digital Divide, London 2003.
Landon-Murray M., “Big Data and Intelligence: Applications, Human Capital, and Education”, Journal of Strategic Security, vol. 9, no. 2 (2016), at <https://scholarcommons.usf.edu/jss/vol9/iss2/6>.
Larson C., “Twitter Data Mining Reveals the Origins of Support for Islamic State”, MIT Technology Review, 23 March 2015, at <https://www.technologyreview.com/s/536061/twitter-data-mining-reveals-the-origins-of-support-for-islamic-state/>.
Larson C., “Who Needs Democracy When You Have Data?”, MIT Technology Review, 20 August 2018, at <https://www.technologyreview.com/s/611815/who-needs-democracywhen-you-have-data/>.
Lowenthal M.M., “Intelligence Education: Quo Vadimus?”, American Intelligence Journal, vol. 31, no. 2 (2013).
Maurtvedt M., The Chinese Social Credit System. Surveillance and Social Manipulation: A Solution to ‘Moral Decay’?, 2017, <https://www.duo.uio.no/bitstream/handle/10852/60829/Master-s-Thesis--Martin-Maurtvedt--27-11-2017.pdf>.
Peters G., “Counterterrorism: Trying to Predict the Future”, Army Technology, 16 September 2015, at <https://www.army-technology.com/features/featurecounterterrorism-tryingto-predict-the-future-4654343/>.
Rapaport A., ‘Quite a few Terrorists lost their lives owing to Big Data’, 3 January 2015, at <https://www.israeldefense.co.il/en/content/quite-few-terrorists-lost-their-lives-owingbig-data>.
Salm L., “70% of Employers are Snooping Candidates’ Social Media Profiles”, CareerBuilder, 15 June 2017, at <https://www.careerbuilder.com/advice/social-media-survey-2017>.
Saunders J., “Pitfalls of Predictive Policing”, Rand Corporation, 11 October 2016, at <https://www.rand.org/blog/2016/10/pitfalls-of-predictive-policing.html>.
Schaefer B., “Predicting Terrorism: Implications for Big Data in Public Safety”, Georgetown Security Studies Review, 8 April 2018, at <http://georgetownsecuritystudiesreview.org/2018/04/08/predicting-terrorism-implications-for-big-data-in-public-safety/>.
Treverton G.F., “New Tools for Collaboration. The Experience of the U.S. Intelligence Community”, CSIS, January 2016, at <https://csis-prod.s3.amazonaws.com/s3fs-public/legacy_files/files/publication/160111_treverton_newtools_web.pdf>.
“Using Big Data Effectively in the Fight Against Terrorism”, Defence Contracts Online, at <https://www.contracts.mod.uk/do-features-and-articles/using-big-data-effectively-inthe-fight-against-terrorism/>.
Van Puyvelde D., Coulthart S., Shahriar Hossain M., “Beyond the Buzzword: Big Data and National Security Decision-making”, International Affairs, vol. 93, no. 6 (2017).
Zhou C., Credit Information Database in China, conference paper, Kuala Lumpur, 5-9 November 2012, at <https://www.ifc.org/wps/wcm/connect/e722b080438c5bc481f5b9869243d457/Session_8_C.Zhou_credit+database+in+China.pdf?MOD=AJPERES>.
Downloads
Published
How to Cite
Issue
Section
License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.