ARTIFICIAL INTELLIGENCE IN POLITICAL ACTIVITY: MAIN AREAS OF USE
PDF (Українська)

Keywords

neural networks
artificial intelligence (AI)
political activity
cybersecurity
digital diplomacy
competition
ethical norms

How to Cite

But, S. (2025). ARTIFICIAL INTELLIGENCE IN POLITICAL ACTIVITY: MAIN AREAS OF USE. Public Management and Policy, (5(9). https://doi.org/10.70651/3041-2498/2025.5.04

Abstract

Today, artificial intelligence (AI) is considered not only in the context of technological innovation, but also as an active tool for influencing socio-political processes, a phenomenon that transforms the established structure of models of internal development and international relations. The purpose of the article is to study the aspects of the impact of artificial neural networks on the processes of political activity in the modern environment. The study determines the key areas of integration of AI tools in modern socio-political processes. The author emphasizes the related risks and challenges caused by the specifics of legal regulation, potential threats of uncontrolled use of AI for political purposes, and ethical aspects. The author considers innovative approaches to the development of political activity against the background of new realities caused by the active development of neural networks. The functionality of AI in the context of developing new political strategies, influencing political competition, and promoting the emergence of new leaders is identified. The influence of neural networks on the processes of using big data and algorithms for effective forecasting and modeling of socio-political development is investigated. The potential threats to the studied process, including cybercrime, human rights violations, and inequality in access to technology, are determined. The need to develop standards for regulating the development and use of AI in political activity, enshrined in the international legal framework, is actualized. The potential of AI as a key factor in the dynamics of the global political landscape and the transformation of the system of socio-political relations is substantiated. New approaches to the involvement of neural networks in the system of political activity development are proposed, which take into account both the potential of AI and the accompanying complex risks. The study actualizes the need to intensify international cooperation to guarantee compliance with ethical standards and develop effective mechanisms for regulating the use of AI potential.

https://doi.org/10.70651/3041-2498/2025.5.04
PDF (Українська)

References

1. Ali, H., Farman, H., Yar, H., Khan, Z., Habib, S., & Ammar, A. (2022). Deep learning-based election results prediction using Twitter activity. Soft Computing, 26(16), 7535–7543. https://doi.org/10.1007/s00500-021-06569-5

2. Amoore, L. (2023). Machine learning political orders. Review of International Studies, 49(1), 20–36. https://doi.org/10.1017/S0260210522000031

3. García-Díaz, J. A., Colomo-Palacios, R., & Valencia-García, R. (2022). Psychographic traits identification based on political ideology: An author analysis study on Spanish politicians’ tweets posted in 2020. Future Generation Computer Systems, (130), 59–74. https://doi.org/10.1016/j.future.2021.12.011

4. Groumpos, P. A. (2023). Critical historic overview of artificial intelligence: Issues, challenges, opportunities and threats. Artificial Intelligence and Applications, 1(4), 181–197. https://doi.org/10.47852/bonviewAIA3202689

5. Kurmangali, M., Kukeyeva, F., & Aktay, D. (2024). Theoretical and Methodological Approaches to Studying Artificial Intelligence in the Context of International Relations and International Law. Journal of Central Asian Studies, 93(1), 4–21. https://doi.org/10.52536/3006-807X.2024-1.01

6. Muchlinski, D., Yang, X., Birch, S., Macdonald, C., & Ounis, I. (2021). We need to go deeper: Measuring electoral violence using convolutional neural networks and social media. Political Science Research and Methods, 9(1), 122–139. https://doi.org/10.1017/psrm.2020.32

7. Revak, I. O., & Gren, R. T. (2021). Osoblyvosti formuvannia bezpechnoho kiberprostoru v umovakh rozvytku tsyfrovoi ekonomiky [Peculiarities of the formation of secure cyberspace in the digital economy]. Innovative Economy, (3–4). 164–169. https://doi.org/10.37332/2309-1533.2021.3-4.23 (in Ukrainian)

8. Rong, Z., & Gang, Z. (2021). An artificial intelligence data mining technology based evaluation model of education on political and ideological strategy of students. Journal of Intelligent & Fuzzy Systems, 40(2), 3669–3680. https://doi.org/10.3233/JIFS-189401

9. Skybun, O. Zh. (2021). Kiberbezpeka system elektronnykh komunikatsii orhaniv derzhavnoi vlady Ukrainy [Cybersecurity of electronic communications systems of public authorities of Ukraine]. Visnyk Natsionalnoi akademii derzhavnoho upravlinnia pry Prezydentovi Ukrainy. Seriia: Derzhavne upravlinnia, (1), 30–39. http://nbuv.gov.ua/UJRN/vnaddy_2021_1_6 (In Ukrainian)

10. Soltanifar, M., Hughes, M., & Göcke, L. (2021). Digital entrepreneurship: Impact on business and society. Springer Nature. http://doi.org/10.1007/978-3-030-53914-6

11. Sopilko, I.M. (2021). Informatsiina bezpeka ta kiberbezpeka: porivnialno-pravovyi aspekt [Information security and cybersecurity: a comparative legal aspect]. Yurydychnyi visnyk Povitriane i kosmichne pravo. 2(59). 110–115. https://doi.org/10.18372/2307-9061.59.15603 (in Ukrainian)

12. Yun, G., Ravi, R. V., & Jumani, A. K. (2023). Analysis of the teaching quality on deep learning-based innovative ideological political education platform. Progress in Artificial Intelligence, 12(2), 175–186. https://doi.org/10.1007/s13748-021-00272-0

13. Edelman Trust Institute. (2024). Edelman Trust Barometer Global Report. Edelman. https://www.edelman.com/sites/g/files/aatuss191/files/2024-02/2024%20Edelman%20Trust%20Barometer%20Global%20Report_FINAL.pdf

Creative Commons License

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

Copyright (c) 2025 Sergii But