ARTIFICIAL INTELLIGENCE IN MODERN POLITICS: TRANSFORMATION OF DEMOCRATIC GOVERNANCE MECHANISMS
Abstract
The article considers modern approaches to the analysis and forecasting of political processes using algorithmic modeling and artificial intelligence technologies. The pur- pose of the article was to study the theoretical and analytical experience regarding the transformation of mechanisms for predicting political processes and democratic govern- ance under the influence of artificial intelligence technologies. It is proven that the use of models contributes to a deeper understanding of the interaction between individual political preferences of citizens and the structural characteristics of the political system. It is found that the effective use of modeling in political research involves the consistent implementation of a number of scientific tasks, among which the process of verifying results in the context of the integration of artificial intelligence technologies and big data analysis is of particular importance. The role of artificial intelligence in the transformation of socio-political processes and methods of their research is highlighted. It is considered that under the influence of the development of social networks, the political behavior of a modern individual is char- acterized by the growth of political alienation, the formation of a simulative picture of self-government and increased dependence on the algorithms of digital platforms. Empir- ical and conceptual approaches are used to study the impact of artificial intelligence on the mobilization potential of citizens and the development of algorithmic forecasting of political processes. It is proven that digital technologies and algorithmic modeling contribute to the forma- tion of new forms of political participation, in particular mass online discussions, which allow a significant number of citizens to actively participate in deliberative mechanisms of policy formation. It is found that governments are increasingly actively implementing digital tools to improve the efficiency of public services and involve citizens in the pro- cesses of democratic governance.
References
Cohen, M. D., Riolo, R. L., & Axelrod, R. (2001). The role of social structure in the maintenance of cooperative regimes. Rationality and Society, 13(1), 5–32 [in English]. [Cohen M. D., Riolo R. L., Axelrod R. The role of social structure in the maintenance of cooperative regimes. Rationality and Society. 2001. № 13(1).
P. 5–32. DOI: https://doi.org/10.1177/104346301013001001]
Denysiuk, S., & Shyian, A. (2008). Modelling of political processes. In V. Horbatenko (Ed.), Applied Political Science. Kyiv: Akademiia [in Ukrainian]. [Денисюк С. Г., Шиян А. А. Моделювання політичних процесів. У: Горбатенко В. П. (ред.). Прикладна політологія: навч. посіб. Київ: Академія, 2008. 472 с.
Duberry, J. (2022). Artificial Intelligence and Democracy. Edward Elgar Publishing [in English]. [Duberry J. Artificial Intelligence and Democracy. Edward Elgar Publishing, 2022. 234 p, DOI: doi.org/10.4337/9781788977319.00014]
Filippucci, F., Gal, P., Jona-Lasinio, C., Leandro, A., & Nicoletti, G. (2024). The impact of artificial intelligence on productivity, distribution and growth. OECD Artificial Intelligence Papers [in English]. [Filippucci F., Gal P., Jona-Lasinio C., Leandro A., Nicoletti G. The Impact of Artificial Intelligence on Productiv-
ity, Distribution and Growth. OECD Artificial Intelligence Papers. 2024. DOI: https://doi.org/10.1787/8d900037-en]
Gelman, A., Hullman, J., Wlezien, C., & Morris, G. E. (2020). Information, incentives, and goals in election forecasts. Judgment and Decision Making, 15(5), 863–880. https://doi.org/10.1017/S1930297500007981 [in English]. [Gelman A., Hullman J., Wlezien C., Morris G. E. Information, incentives, and goals in election forecasts. Judgment and Decision Making. 2020. Vol. 15(5). P. 863–880. DOI: https://doi.org/10.1017/S1930297500007981.]
Hanneman, R. A., Collins, R., & Mordt, G. (1995). Discovering theory dynamics by computer simulation: Experiments on state legitimacy and imperialist capitalism. Sociological Methodology, 25, 1–46 [in English]. [Hanneman R. A., Collins R., Mordt G. Discovering theory dynamics by computer simulation: Experiments on state legitimacy and imperialist capitalism. Sociological Methodology. 1995. Vol. 25. P. 1–46. DOI: https://doi.org/10.2307/271060 ]
Holland, J. H. (1995). Hidden Order: How Adaptation Builds Complexity. Perseus Books [in English]. [Holland J. H. Hidden Order: How Adaptation Builds Complexity. Perseus Books, 1995. 513 p.]
Kaufman, S. A. (1996). At Home in the Universe: The Search for the Laws of Self-Organization and Complexity. Oxford University Press [in English]. [Kaufman S. A. At Home in the Universe: The Search for the Laws of Self-Organization and Complexity. Oxford University Press, 1996. 413 p. URL: https://books.google.me/
books?id=o-Owb5IDkSQC&printsec=frontcover#v=onepage&q&f=false (дата звернення: 17.02.2026).]
Kliuiev, K. G. (2016). Mathematical modelling as an effective method of political processes research. Political Life, 1–2, 19–23 [in Ukrainian]. [Клюєв К. Г. Математичне моделювання як ефективний метод дослідження політичних процесів. Політичне життя. 2016. № 1–2. С. 19–23. URL: http://nbuv.gov.ua/
UJRN/pollife_2016_1-2_5. (дата звернення: 16.02.2026).]
Landemore, H. (2024). Can artificial intelligence bring deliberation to the masses? In R. Chang & A. Srinivasan (Eds.), Conversations in Philosophy, Law, and Politics. Oxford Academic. https://doi.org/10.1093/oso/9780198864523.003.0003 [in English]. [Landemore H. Can Artificial Intelligence Bring Deliberation
to the Masses? In: Chang R., Srinivasan A. (Eds.). Conversations in Philosophy, Law, and Politics. Oxford Academic, 2024. 615 p. DOI: https://doi.org/10.1093/oso/9780198864523.003.0003.]
Macy, M., & Willer, R. (2002). From factors to actors: Computational sociology. Annual Review of Sociology, 28, 143–166 [in English]. [Macy M., Willer R. From factors to actors: Computational sociology. Annual Review of Sociology. 2002. Vol. 28. P. 143–166. DOI: https://doi.org/10.1146/annurev.soc.28.110601.141117]
Nisbet, E. C., Cooper, K. E., & Garrett, R. K. (2015). The partisan brain: How dissonant science messages lead conservatives and liberals to (dis)trust science. The Annals of the American Academy of Political and Social Science, 658, 36–66. https://doi.org/10.1177/0002716214555474 [in English].
[Nisbet E. C., Cooper K. E., Garrett R. K. The partisan brain: How dissonant science messages lead conservatives and liberals to (dis)trust science. The Annals of the American Academy of Political and Social Science. 2015. Vol. 658. P. 36–66. DOI: https://doi.org/10.1177/0002716214555474]
Simon, H. A. (1998). The Sciences of the Artificial. MIT Press [in English]. [Simon H. A. The Sciences of the Artificial. MIT Press, 1998. 125 p.] URL: https://monoskop.org/images/9/9c/Simon_Herbert_A_The_Sciences_of_the_Artificial_3rd_ed.pdf (дата звернення: 16.02.2026).]
Sviderska, O. (2020). Theoretical and methodological analysis of social media influence on political behaviour formation in modern society (case of Facebook, Twitter, Instagram, WhatsApp). Regional Studies, 20, 184–190 [in Ukrainian]. [Свідерська О. Теоретико-методологічний аналіз впливу соціальних мереж на формування політичної поведінки в сучасному суспільстві (на прикладі Facebook, Twitter, Instagram, WhatsApp). Регіональні студії. 2020. № 20. С. 184–190. URL: https://file.lib.in.ua/pdf/sviderska-olha-teoretyko-metodolohichnyi-analiz-vplyvu-sotsialnykh-merezh-na-formuvannia-politychnoi-povedinky.pdf (дата звернення: 16.02.2026).]
Valentino, N. A., Brader, T., Groenendyk, E. W., Gregorowicz, K., & Hutchings, V. L. (2011). Election night’s alright for fighting: The role of emotions in political participation. The Journal of Politics, 73(1), 156–170. https://doi.org/10.1017/S0022381610000939 [in English]. [Valentino N. A., Brader T., Groenendyk E. W., Gregorowicz K., Hutchings V. L. Election night’s alright for fighting: The role of emotions in political participation. The Journal of Politics. 2011. Vol. 73(1). P. 156–170. DOI: https://doi.org/10.1017/S0022381610000939.]
Yang, F., Cai, M., Mortenson, C., Fakhari, H., Lokmanoglu, A. D., Hullman, J., Franconeri, S., Diakopoulos, N., Nisbet, E. C., & Kay, M. (2023). Swaying the public? Impacts of election forecast visualizations on emotion, trust, and intention in the 2022 U.S. midterms. Northwestern University. URL: https://surl.li/nwzvgd (ac-
cessed: 18.02.2026) [in English]. [Yang F., Cai M., Mortenson C., Fakhari H., Lokmanoglu A. D., Hullman J., Franconeri S., Diakopoulos N., Nisbet E. C., Kay M. Swaying the public? Impacts of election forecast visualizations on emotion, trust, and intention in the 2022 U.S. midterms. Northwestern University. 2023. URL: https://surl.li/nwzvgd (дата звернення: 18.02.2026).]
