From Penalty to Prevention: A Computational Model of Corruption Reduction Using Algorithmic Governance and Digital Oversight
DOI:
https://doi.org/10.70411/MJHAS.3.1.2026324Keywords:
Corruption reduction, Algorithmic governance, Digital oversight, Computational model, transparencyAbstract
This study presents a computational version that integrates algorithmic regime and virtual oversight to address corruption through proactive, prevention-oriented structures. Using agent-based and system dynamics simulations, the version shows that the algorithmic oversight, including AI-powered audits, transparency dashboards and blockchain verification, reduces the occurrence of corruption, enhances detection capabilities, lowers compliance costs, and improves public confidence.
These findings highlight the capability of virtual gadgets to reform governance structures by embedding integrity, transparency, and performance into institutional procedures. However, adopting such systems is not without challenges. Ethical risks, including set-of-rules bias, overlapping automation, and lecturer boundaries, must be carefully managed through fairness-by-design approaches, third-party audits, and continuous human oversight.
The success of the algorithmic regime depends on significant institutional reforms, public values, and its alignment with prevailing trends, including constitutional AI and ethical public administration.
References
Aarvik, P. (2019). Artificial Intelligence–a promising anti-corruption tool in development settings. U4 Anti-Corruption Resource Centre.
Abdulwasaa, M. A., Kawale, S. V., Abdo, M. S., Albalwi, M. D., Shah, K., Abdalla, B., & Abdeljawad, T. (2024). Statistical and computational analysis for corruption and poverty model using Caputo-type fractional differential equations. Heliyon, 10(3).
Ayobami, A. T., Mike-Olisa, U., Ogeawuchi, J. C., Abayomi, A. A., & Agboola, O. A. (2023). Algorithmic Integrity: A Predictive Framework for Combating Corruption in Public Procurement through AI and Data Analytics. Journal of Frontiers in Multidisciplinary Research, 4(2), 130-141.
Çani, E. M., Çuka, M., & Mazelliu, A. Transparency and Accountability in AI Systems: A Realistic Approach in Albania. In Artificial Intelligence in Legal Systems (pp. 50-62). Chapman and Hall/CRC.
Castelo, N. (2024). Perceived corruption reduces algorithm aversion. Journal of Consumer Psychology, 34(2), 326-333.
Ceva, E., & Jiménez, M. C. (2022). Automating anticorruption? Ethics and Information Technology, 24(4), 48.
Elnawawy M, S., Okasha, A. E., & Hosny, H. A. (2022). Agent-based models of administrative corruption: An overview. International Journal of Modelling and Simulation, 42(2), 350-358.
Gaurav, S., Heikkonen, J., & Chaudhary, J. (2025). Governance-as-a-service: A multi-agent framework for ai system compliance and policy enforcement. arXiv preprint arXiv:2508.18765.
Gutiérrez, J. D., & Muñoz-Cadena, S. (2025). Artificial intelligence in Latin America's public policy cycles. In Handbook of Public Policy in Latin America (pp. 405-422). Edward Elgar Publishing.
Ibrahimy, M. M., Norta, A., & Normak, P. (2024). Blockchain-based governance models supporting corruption-transparency: A systematic literature review. Blockchain: Research and Applications, 5(2), 100186.
Kasa, R., Réthi, G., Hauber, G., & Szegedi, K. (2023). Simulation of corruption decisions—an agent-based approach. Sustainability, 15(3), 2561.
Kaye, K., Dixon, P., & Gellman, R. (2023). Risky Analysis: Assessing and Improving AI Governance Tools. An International Review of AI Governance Tools and Suggestions for Pathways Forward.
Nichols, P. M. (2025). Does compliance with the global anticorruption regime require the use of artificial intelligence? American Business Law Journal, 62(3), 145-164.
Odilla, F. (2024). Unfairness in AI Anti-Corruption Tools: Main Drivers and Consequences: Unfairness in AI Anti-Corruption Tools. Minds and Machines, 34(3), 28.
Park, J. S., Zou, C. Q., Shaw, A., Hill, B. M., Cai, C. J., Morris, M. R., Willer, R., Liang, P., & Bernstein, M. S. (2025). Simulating human behavior with ai agents. In.
Petheram, A., Pasquarelli, W., & Stirling, R. (2019). The next generation of anti-corruption tools: Big data, open data & artificial intelligence. Available at researchreport2019_thenextgenerationofanti-corruptiontools_bigdataopendataartificialintelligence. pdf (europa. eu)(Accessed 28 February 2024).
Raji, I. D., Xu, P., Honigsberg, C., & Ho, D. (2022). Outsider oversight: Designing a third party audit ecosystem for ai governance. Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society,
Rysin, M., & Sukh, Y. (2024). Digital solutions as an effective approach to combat corruption in public procurement. Democratic Governance, 2(17), 18-29.
Schmitz, C., Rystrøm, J., & Batzner, J. (2025). Oversight structures for agentic AI in public-sector organizations. Proceedings of the 1st Workshop for Research on Agent Language Models (REALM 2025),
Srinivasan, T., & Patapati, S. (2025). Democracy-in-Silico: Institutional Design as Alignment in AI-Governed Polities. arXiv preprint arXiv:2508.19562.
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