| Abstract: |
Corruption continues to be one of the most stubborn barriers to good governance, sustainable economic development and public trust in authorities worldwide, with traditional administrative, legal and audit-based mechanisms having repeatedly shown they have limited ability to detect and address it in real-time [1]. The domain of artificial intelligence (AI) has recently become the latest crosscutting tool, spanning aspects from computer science to public administration as well as law and behavioural economics. This research studies the application of artificial intelligence tools such as machine learning, natural language processing, anomaly detection and predictive analytics for detecting anomalies in procurement, tax collection, public service deliverance and monetary transaction [3]. The study utilizes a multiple methods approach, including a structured survey questionnaire with government officials, IT professionals and citizens as well as secondary data analysis of e-governance and AI-based anti-corruption initiatives carried out in different jurisdictions[4]. We then created five analytical tables (awareness levels, perceived effectiveness, sectoral applicability, barriers to adoption and trust in AI driven systems) summarizing the results of each area presented followed by an individual interpretation. The results show that even if AI improves transparency and make discretionary decision-making less, its potential is limited due to the availability and quality of data, lack of technical capacity at the implementation stage, algorithmic bias and low strength laws on accountability in emerging technologies [5]. |
