The Role of Artificial Intelligence in Preventing Corruption

Abstract

Corruption remains a persistent challenge threatening public trust and economic development worldwide. This study examines how artificial intelligence contributes to preventing corruption within public institutions. It explores machine learning, network analysis, and predictive tools now used across procurement, taxation, and healthcare systems. The research addresses what legal framework should govern these technologies while ensuring accountability, fairness, and due process. Using qualitative doctrinal and document analysis, this study reviews current laws, regulations, and recent scholarly literature published within the past five years. Findings show artificial intelligence significantly improves detection speed and accuracy, yet existing laws rarely address algorithmic bias or the right to challenge automated findings. This gap leaves institutions and individuals exposed to unfair or unaccountable outcomes. The study proposes embedding transparency, human oversight, and appeal mechanisms into legal reform. It concludes that effective corruption prevention requires balancing technological capability with meaningful legal protection for those affected by these systems.