Artem Balyakin, Marina Nurbina, Sergey Taranenko
NRC Kurchatov Institute, Moscow, Russia
Cite: Balyakin A., Nurbina M., Taranenko S. Some Features of Social Structures and Institutions Transformation in the Digital Age. J. Digit. Sci. 4(1), 30 – 42 (2022). https://doi.org/10.33847/2686-8296.4.1_3
Abstract. The paper examines the peculiarities of digitalization processes influence on the architecture of emerging socio-economic relations. The legal regulation issues of digital technologies and the shifts they cause in public life are considered. The relations arising in connection with the regulation of big data are compared. The evolution of big data into smart content is described. The phenomenon of the “digital twin” is considered, as well as its impact on the social sphere. The tendency to move away from the policy of direct prohibitions in the field of digital technologies and the transition to the control of physical entities (data centers) and the regulation of methods and approaches to data processing (algorithms) is shown. It is noted that the existing expectations from digitalization are overstated. At the same time, the increasing influence of digital technologies significantly changes the existing socio-economic landscape, generating new risks. The answer to these challenges should be the joint work of authorities, business, society and the expert community on the formation of digital culture. It is shown that an important aspect should be the development of expert systems that translate qualitative characteristics into quantitative indicators.
Keywords: Big data, legal regulation, artificial intelligence, digital twin, digitalization, social systems, transformation.
Acknowledgments
This work was supported by RFBR grant № 20-010-00576.
Authors thank Zhulego V.G. for useful discussions.
References
- Walker, M., Burton, B.: Hype Cycle for Emerging Technologies (2015), https://www.gartner.com/en/documents/3100227, last accessed 2022/03/31.
- Gartner Top 10 Strategic Technology Trends For 2020, https://www.gartner.com/smarterwithgartner/gartner-top-10-strategic-technology-trends-for-2020/, last accessed 2022/02/21.
- Manuka, J., Chui, M., Brown, B., Bughin, J., Hobbs, R., Roxburgh, C., Byers, A.: Big data: The next frontier for innovation, competition, and productivity / McKinsey Global Institute (2011).
- Measuring the Digital Transformation: A Roadmap for the Future (2019), https://www.oecd.org/publications/measuring-the-digital-transformation-9789264311992-en.htm, last accessed 2022/03/31.
- Melnik, M., Antipova, T. (2020) Organizational Aspects of Digital Economics Management. In: ICIS 2019. Lecture Notes in Networks and Systems, vol 78, pp. 148-162. Springer, Cham. https://doi.org/10.1007/978-3-030-22493-6_14.
- Ross, G., Antipova, T., Konyavsky, V. (2022) Methodology for Innovative Projects’ Financing in IT Business. Lecture Notes in Networks and Systems, 381, pp. 257–268. https://doi.org/10.1007/978-3-030-93677-8_22.
- Nurakhov N.N. (2020) The Basic Processes of Creating a Megascience Project. Lecture Notes in Networks and Systems, 78, pp. 329-339. https://doi.org/10.1007/978-3-030-22493-6_29.
- Saaty, T.L.: Decision making with dependence and feedback: The Analytic Network Process. Pittsburgh: RWS Publications (1996).
- Saaty, T.L. (2008) The Analytic Hierarchy and Analytic Network measurement processes: Applications to decisions under risk / European Journal of Pure and Applied Mathematics, 1(1), pp. 122-196.
- Balyakin A.A., Domnich A.S., Zhulego V.G., Taranenko S.B. Designing the Future: Nonlinear Dynamics in Economic Models. Integral Scientific and Practical Interindustry Journal. 2011. No. 1 (57), pp. 33-35.
- Lynch, C.: Big data: How do your data grow? Nature, 455, 28-29 (2008).
- Mayer-Schönberger, V., Cukier, K.: Big Data: A Revolution That Will Transform How We Live, Work, and Think. Moscow: Mann, Ivanov & Ferber (2014).
- Balyakin, A.A., Nurbina, M.V., Taranenko, S.B. (2021) Ethics in Big Data: Myth or Reality. In: Á. Rocha et al. (eds.) Information Technology and Systems, AISC 1330, pp. 14–22.
- A. Pentland Social Physics: How Social Networks Can Make Us Smarter. Penguin Books. 2015
- Cathy O’Neil Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy Crown, 2016
- Ivanov V.V., Malinetskii G.G. Contours of digital reality. Humanitarian technological revolution and the choice of the future. Lenand, Moscow, 2018, 344 p.
- Kai-Fu Lee, AI Superpowers: China, Silicon Valley, and the New World Order, Boston, Mass, 2018
- Balyakin, A.A., Nurbina, M.V., Taranenko, S.B. (2021) Some Current Aspects of Big Data Evolution. In T. Antipova (Ed.). ICADS 2021, AISC 1352, pp. 444–450. https://doi.org/10.1007/978-3-030-71782-7_39.
- Dawkins, Richard: The Extended Phenotype, Oxford University Press, p. 109 (1982).
- FAIR Principles, https://www.go-fair.org/fair-principles/, last accessed 2022/05/13.
- Balyakin, A.A., Malyshev, A.S.: Big data management in research infrastructures. In: Open Systems. DBMS. 2020(3), pp. 40-42. Moscow (2020).
- Birhane, A.: Algorithmic injustice: a relational ethics approach. Perspective, Patterns 2, vol. 2(2), 100205, February 12 (2021).
- Griffy-Brown C., Chun M., Miller H., Lazarikos D. How Do We Optimize Risk in Enterprise Architecture when Deploying Emerging Technologies? J. Digit. Sci. 3(1), 3 – 13 (2021). https://doi.org/10.33847/2686-8296.3.1_1.
- Balyakin A.A., Malyshev A.S., Nurbina M.V., Titov M.A. (2020) Big Data: Nil Novo Sub Luna. In: ICIS 2019. Lecture Notes in Networks and Systems, vol 78, pp. 364-373. Springer, Cham. https://doi.org/10.1007/978-3-030-22493-6_32.
- Harari, Y.N: 21 Lessons for the 21st Century. p. 416, Vintage Digital (2018).
- Ustinova, Y. (2021) The true and fair view concept: the palette of controversial points (of «worth banning» to «worth keeping»). J. Digit. Art Humanit., 2(1), 39-47. https://doi.org/10.33847/2712-8148.2.1_4.
- Balyakin, A.A., 1, Taranenko, S.B., Nurbina, M.V., Titov, M.A. Social Aspects of Big Data Technology Implementation. J. Digit. Sci., 1(1), 15-24 (2019). https://doi.org/10.33847/2686-8296.1.1_2.
- Sadowski, J.: When data is capital: Datafication, accumulation, and extraction. Research Article, 6(1), 1-12 (2019).
- Kemper, J., Kolkman, D.: Transparent to whom? No algorithmic accountability without a critical audience. Information, Communication & Society, 2081-2096 (2018).
- Kirsten, M.: Ethical Implications and Accountability of Algorithms. Journal of Business Ethics, 160(4), 835–850 (2019).
- Engin, Z., Treleaven, Ph. (2019). Algorithmic Government: Automating Public Services and Supporting Civil Servants in using Data Science Technologies. The Computer Journal, 62(3), pp. 448–460.
- Pariser, E.: The Filter Bubble: What the Internet Is Hiding from You. New York: Penguin Press. (2011).
- Viewpoint: China risks damaging science ties by forging own path on research ethics. https://sciencebusiness.net/news/viewpoint-china-risks-damaging-science-ties-forging-own-path-research-ethics, last accessed 2021/09/23.
- Chetverikov, A.O. (2018). Organizatsionno-pravovye formy bolshoy nauki (megasayens) v usloviyakh mezhdunarodnoy integratsii: sravnitelnoe issledovanie. Legal forms of big science (megascience) in the context of. Legal science, 1 (1), 13-27; 2 (2), 34-50.
- Von Weizsaecker, E., Wijkman, A.: Come On! Capitalism, Short-termism, Population and the Destruction of the Planet. Springer Science+Business Media LLC. (2018).
- Balyakin, A.A., Nurbina, M.V., Taranenko, S.B. (2020) Comparative legal features of the formation of a digital ecosystem. Int. Legal Courier 1–2(37–38), 42–45.
- The decree of the President of the Russian Federation. About the strategy for the development of the information society in the Russian Federation for 2017-2030. No. 203, 09.05.2017
- Order of the Government of the Russian Federation of July 28, 2017 No. 1632-r Program “Digital Economy of the Russian Federation”, http://static.government.ru/media/files/9gFM4FHj4PsB79I5v7yLVuPgu4bvR7M0.pdf, last accessed 2022/02/02.
- URL: https://e-cis.info/news/568/82077/, last accessed 2019/05/20.
- Fei Tao et al. (2018). Digital twin-driven product design framework. International Journal of Production Research, 57(12), pp. 3935–3953.
- Gartner Survey Reveals Digital Twins Are Entering Mainstream Use (2019) / Gartner. https://www.gartner.com/en/newsroom/press-releases/2019-02-20-gartner-survey-reveals-digital-twins-areentering-mai, last accessed 2019/03/31.
- Digital Twin Market by Technology, Type (Product, Process, and System), Application (predictive maintenance, and others), Industry (Aerospace & Defense, Automotive & Transportation, Healthcare, and others), and Geography – Global Forecast to 2026. (2020), https://www.marketsandmarkets.com/Market-Reports/digital-twin-market-225269522.html, last accessed 2020/05/13.
- Singh, M., Fuenmayor, E., Hinchy, E.P., Qiao, Y., Murray, N., Devine, D.: Digital Twin: Origin to Future. Appl. Syst. Innov, 4, 36 (2021).
- Balyakin, A.A., Nurakhov, N.N., Nurbina M.V. (2021). Digital Twins vs Digital Trace in Megascience Projects. In Á. Rocha et al. (Eds.). ICITS 2021, AISC 1330, pp. 534–539.
- Crease, R.P., Westfall, C.: The New Big Science Physics Today, 69 (5), pp. 30-36 (2016).
- URL: https://digital-strategy.ec.europa.eu/en/news/new-projects-enrich-ai-demand-platform, last accessed 2022/03/31.
- URL: https://www.feam.eu/wp-content/uploads/International-Health-Data-Transfer_2021_web.pdf, last accessed 2021/10/10.
- URL: https://sciencebusiness.net/news/data-protections-rules-harming-eu-leadership-health-research-says-report, last accessed 2022/05/22.
- URL: https://sciencebusiness.net/news/time-harmonise-artificial-intelligence-principles-experts-say, last accessed 2022/03/10.
Published online 12.06.2022