DIGITAL TRANSFORMATION OF ANTI-CORRUPTION POLICY IN THE PUBLIC ADMINISTRATION SYSTEM: THE ROLE OF INNOVATIVE TOOLS AND ARTIFICIAL INTELLIGENCE
DOI:
https://doi.org/10.34132/pard2026.31.07Keywords:
public administration, anti-corruption policy, innovative tools, artificial intelligence, digitalization, transparency, accountability, integrityAbstract
The article provides a comprehensive scientific analysis of the digital transformation of anti-corruption policy within the system of public administration, with a particular focus on the role of innovative tools and artificial intelligence technologies in enhancing transparency, accountability, and integrity. The relevance of the topic is determined by the need to modernize anti-corruption mechanisms in the context of public sector digitalization, European integration processes, and the implementation of international good governance standards. The purpose of the article is to theoretically substantiate the conceptual foundations for integrating digital technologies and AI systems into anti-corruption policy and to assess their potential and associated risks in ensuring public accountability. The research is based on an interdisciplinary approach, combining institutional, systemic, comparative, and analytical methods. The study demonstrates that digital tools—such as open data platforms, e-procurement systems, risk-based monitoring algorithms, and big data analytics–significantly transform traditional anti-corruption control mechanisms by enabling proactive detection of corruption risks. The application of machine learning algorithms and data analytics allows the identification of anomalies in public finance, procurement, land management, and other sectors that were previously beyond the reach of conventional oversight mechanisms. At the same time, the article systematizes the main challenges associated with AI deployment in anti-corruption contexts, including data bias and reliability issues, algorithmic errors (false positives and false negatives), opacity of “black box” models, risks of algorithmic capture, information asymmetry, and insufficient institutional readiness. It emphasizes the necessity of maintaining an appropriate balance between AI autonomy and meaningful human oversight, in line with international frameworks such as the OECD AI Principles and the EU AI Act.
References
1. National Agency on Corruption Prevention. (2023, December 14). Holova NAZK vystupyv na Antykoruptsiinii konferentsii OON ta zaklykav derzhavy investuvaty v systemy, yaki vymiriuiut dobrochesnist [NACP Head spoke at the UN Anti-Corruption Conference and called on states to invest in systems that measure integrity]. Retrieved from: [in Ukrainian].
2. Deliversky, J. (2025). The role of digitalization in the fight against corruption. Environment. Technology. Resources. Proceedings of the 16th International Scientific and Practical Conference, 5, 69–72. Retrieved from: [in English].
3. Petkun, S., & Melnyk, I. (2025). Suchasni trendy tsyfrovoi transformatsii publichnoho upravlinnia [Modern trends of digital transformation of public administration]. Publichno-upravlinski ta tsyfrovi praktyky, 2(5), 98–107. Retrieved from: [in Ukrainian].
4. Tokarieva, K., & Berezovskyi, D. (2023). Zastosuvannia suchasnykh tsyfrovykh tekhnolohii u publichnomu upravlinni (na prykladi finansovoi sfery) [Application of modern digital technologies in public administration (on the example of the financial sphere)]. Pravo ta innovatsii, 3(43), 54–63. Retrieved from: [in Ukrainian].
5. Kostenko, Yu. O. (2025). Suchasni napriamy reformuvannia DPS Ukrainy: antykoruptsiina polityka, tsyfrovizatsiia ta yevropeiski standarty [Modern directions of reforming the STS of Ukraine: Anti-corruption policy, digitalization, and European standards]. Publichne pravo, 3(59), 24–35. Retrieved from: [in Ukrainian].
6. Dumchykov, M. O. (2025). Tsyfrovi platformy u borotbi z koruptsiieiu: innovatsiini mekhanizmy zakhystu vykryvachiv ta perspektyvy yikh udoskonalennia [Digital platforms in the fight against corruption: Innovative mechanisms for whistleblower protection and prospects for their improvement]. Naukovi zapysky Lvivskoho universytetu biznesu ta prava. Seriia ekonomichna. Seriia yurydychna, 44, 108–114. Retrieved from: [in Ukrainian].
7. Kumar, S., Singh, J. P., & Kaur, G. (2020). Big data analytics for detecting fraud and corruption in public procurement systems. International Journal of Information Management, 51, Article 102045. Retrieved from: [in English].
8. Korzhov, S. S. (2025). Intehratsiia blokchein-tekhnolohii u systemy elektronnoho uriaduvannia yak mekhanizm protydii koruptsiinym skhemam [Integration of blockchain technologies into e-governance systems as a mechanism for countering corruption schemes]. Uzhhorodskyi naukovyi zhurnal, 03, 133–137. Retrieved from: [in Ukrainian].
9. Dumchykov, M. O. (2024). Vykorystannia OSINT tekhnolohii dlia vyiavlennia koruptsiinykh pravoporushen: suchasni pidkhody ta vyklyky [Use of OSINT technologies for detecting corruption offenses: modern approaches and challenges]. Akademichni vizii, 36. Retrieved from: [in Ukrainian].
10. Goldsmith, S., & Yang, J. T. (2024, October). AI and the transformation of accountability and discretion in urban governance. Data-Smart City Solutions at the Bloomberg Center for Cities at Harvard University. Retrieved from: [in English].
11. Shterma, T. V., Lukivskyi, A. S., & Lukivskyi, S. D. (2025). Vprovadzhennia shtuchnoho intelektu u modeli ekonomichnoho upravlinnia dlia minimizatsii koruptsiinykh ryzykiv [Implementation of artificial intelligence in economic management models to minimize corruption risks]. Zdobutky ekonomiky: perspektyvy ta innovatsii, 15. Retrieved from:
12. National Agency on Corruption Prevention. (2023, December). Efektyvnist finansovoho kontroliu i tsyfrovi antykoruptsiini rishennia: NAZK prezentuvalo dosvid Ukrainy na zasidanni RAI [Effectiveness of financial control and digital anti-corruption solutions: NACP presented Ukraine's experience at the RAI meeting]. Retrieved from:
13. National Agency on Corruption Prevention. (2024, February 22). Chotyry roky NAZK 2.0: tsyfrova transformatsiia [Four years of NACP 2.0: Digital transformation]. Retrieved from:
14. National Agency on Corruption Prevention. (n.d.). Tsyfrovizatsiia, partysypatsiia i chitki zakony: zastupnyk Holovy NAZK pro antykoruptsiini zapobizhnyky u vidbudovi [Digitalization, participation, and clear laws: NACP Deputy Head on anti-corruption safeguards in reconstruction]. Retrieved from: [in English].
15. OECD. (2025). Governing with artificial intelligence: The state of play and way forward in core government functions. OECD Publishing. Retrieved from: [in English].
16. OECD. (2022). OECD framework for the classification of AI systems (OECD Digital Economy Papers No. 323). OECD Publishing. Retrieved from: [in English].
17. Zinnbauer, D. (2025). Artificial intelligence in anti-corruption – a timely update on AI technology (U4 Brief 2025:1). U4 Anti-Corruption Resource Centre. Retrieved from: [in English].
18. Andersen, T. B. (2009). E-government as an anti-corruption strategy. Information Economics and Policy, 21(3), 201–210. [in English].
19. Elbahnasawy, N. G. (2014). E-government, internet adoption, and corruption: An empirical investigation. World Development, 57, 114–126. Retrieved from: [in English].
20. Gurin, J. (2014). Open governments, open data: A new lever for transparency, citizen engagement, and economic growth. SAIS Review of International Affairs, 34(1), 71–82. [in English].
21. Kossow, N., & Dykes, V. (2018). Embracing digitalisation: How to use ICT to strengthen anti-corruption. Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) GmbH. Retrieved from: [in English].
22. Kossow, N., & Kukutschka, R. M. (2017). Civil society and online connectivity: Controlling corruption on the net? Crime, Law and Social Change, 68(4), 451–468. [in English].
23. Hariyani, D. et al. A literature review on transformative impacts of blockchain technology on manufacturing management and industrial engineering practices. Green Technologies and Sustainability, 3(3), Article 100169. Retrieved from: [in English].
24. Aarvik, P. (2019). Artificial intelligence: A promising anti-corruption tool in development settings? (U4 Report 2019:1). U4 Anti-Corruption Resource Centre. Retrieved from: [in English].
25. Köbis, N., Starke, C., & Rahwan, I. (2022). The promise and perils of using artificial intelligence to fight corruption. Nature Machine Intelligence, 4, 418–424. Retrieved from:
26. López-Iturriaga, F. J., & Sanz, I. P. (2018). Predicting public corruption with neural networks: An analysis of Spanish provinces. Social Indicators Research, 140(3), 975–998. Retrieved from: [in English].
27. De Blasio, G., D’Ignazio, A., & Letta, M. (2022). Gotham city: Predicting ‘corrupted’ municipalities with machine learning. Technological Forecasting and Social Change, 184, Article 121964. Retrieved from: [in English].
28. Kossow, N., Windwehr, S., & Jenkins, M. (2021). Algorithmic transparency and accountability (Transparency International Anti-Corruption Helpdesk Answer). Transparency International. Retrieved from: [in English].
29. Laurance, B. (2024, April 11). Roads of destruction: We found vast numbers of illegal ‘ghost roads’ used to crack open pristine rainforest. The Conversation. Retrieved from: [in English].
30. Odilla, F., De Figueiredo, V., & Dos Santos Veloso, C. (2022). Citizens and their bots that sniff corruption: Using digital media to expose politicians who misuse public money. SBAP. Retrieved from: [in English].
31. Katona, E., & Fazekas, M. (2024). Hidden barriers to open competition: Using text mining to uncover corrupt restrictions to competition in public procurement (Working Paper series: GTI-WP/2024:01). Government Transparency Institute. Retrieved from: [in English].
32. Gallego, J., Rivero, G., & Martínez, J. (2021). Preventing rather than punishing: An early warning model of malfeasance in public procurement. International Journal of Forecasting, 37(1), 360–377. Retrieved from: [in English].
33. Gallego, J., Prem, M., & Vargas, J. F. (2022). Predicting politicians’ misconduct: Evidence from Colombia. Data & Policy, 4, Article 39. Retrieved from: [in English].
34. GOV.UK. (n.d.). AI playbook for the UK government. Retrieved from: [in English].
35. OECD. (2019). AI principles. [in English].
36. European Parliament. (2023). EU AI Act: First regulation on artificial intelligence. Retrieved from: [in English].
37. Future of Life Institute. (2024). High-level summary of the AI Act. EU Artificial Intelligence Act. Retrieved from: [in English].
38. Constantino, T. (2024, November 12). Women make up 29% of the AI workforce — here’s how to fix it. Forbes. Retrieved from: [in English].
39. Maslej, N., et al. (2025). The AI index 2025 annual report. AI Index Steering Committee, Institute for Human-Centered AI, Stanford University. Retrieved from: [in English].
40. Pillay, T. (2024, October 30). The gap between open and closed AI models might be shrinking. Here’s why that matters. Time. [in English].
41. Sanderson, C., et al. (2023). AI ethics principles in practice: Perspectives of designers and developers. IEEE Transactions on Technology and Society, 4(2), 171–187. Retrieved from: [in English].
42. Borgesius, F. Z., & van Bekkum, M. (2021, March 11). Digital welfare fraud detection and the Dutch SyRI judgment. IAPP. Retrieved from: [in English].
43. Netudyhata, К., Shtyrov, О. (2025). Rol derzhavy v upravlinni ekosystemamy v umovakh formuvannia suchasnoi tsyfrovoi ekonomiky. [The role of the state in ecosystem management in the conditions of the modern digital economy formation]. SWorldJournal, 3(30-03), 124–135. Retrieved from: [in Ukrainian].
44. Chen, S. (2019, February 4). Is China’s corruption-busting AI system being turned off for being too efficient? Tech in Asia. Retrieved from: [in English].
45. Ugale, G., & Hall, C. (2024). Generative AI for anti-corruption and integrity in government: Taking stock of promise, perils and practice (OECD Artificial Intelligence Papers No. 12). OECD Publishing. Retrieved from: [in English].
46. Zhao, H., et al. (2024). Explainability for large language models: A survey. ACM Transactions on Intelligent Systems and Technology, 15(2), Article 19. Retrieved from: [in English].
.png)
.png)
