AI-Powered Platforms for Institutional Accountability: A Comparative Analysis of AI Algorithms for Anti-Corruption Systems

Session

Computer Science and Communication Engineering

Description

Institutional accountability is crucial in combating corruption, and Artificial Intelligence (AI) offers significant opportunities to enhance transparency and efficiency in governance. This paper presents a comparative analysis of various AI algorithms to determine the most suitable for developing an AI-driven platform designed to detect irregularities in asset declarations and financial records. By automating the cross-referencing of data from public records, tax filings, procurement databases, and civil registries, the envisioned platform aims to provide real-time insights for authorities to identify discrepancies and conflicts of interest. The study evaluates multiple machine learning techniques based on their capabilities in pattern recognition, handling large datasets, scalability, and interpretability. Each algorithm is assessed for its strengths and limitations in specific tasks within the anti-corruption framework, such as relationship detection, fraud identification, and real-time analysis. Ethical implications of deploying AI in governance are also explored, particularly in balancing data privacy with the need for transparency. Design considerations include implementing robust security measures and adhering to local and international data governance standards. This paper offers a comprehensive roadmap for selecting the optimal AI algorithm in the development of AI-powered anti-corruption systems. By addressing technical, ethical, and operational challenges, the comparative analysis ensures the platform's adaptability and effectiveness in combating corruption across various sectors and regions.

Keywords:

Institutional, Accountability, Machine Learning, Algorithms, Data Cross-Referencing, Real Time Analysis

Proceedings Editor

Edmond Hajrizi

ISBN

978-9951-982-15-3

Location

UBT Kampus, Lipjan

Start Date

25-10-2024 9:00 AM

End Date

27-10-2024 6:00 PM

DOI

10.33107/ubt-ic.2024.407

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Oct 25th, 9:00 AM Oct 27th, 6:00 PM

AI-Powered Platforms for Institutional Accountability: A Comparative Analysis of AI Algorithms for Anti-Corruption Systems

UBT Kampus, Lipjan

Institutional accountability is crucial in combating corruption, and Artificial Intelligence (AI) offers significant opportunities to enhance transparency and efficiency in governance. This paper presents a comparative analysis of various AI algorithms to determine the most suitable for developing an AI-driven platform designed to detect irregularities in asset declarations and financial records. By automating the cross-referencing of data from public records, tax filings, procurement databases, and civil registries, the envisioned platform aims to provide real-time insights for authorities to identify discrepancies and conflicts of interest. The study evaluates multiple machine learning techniques based on their capabilities in pattern recognition, handling large datasets, scalability, and interpretability. Each algorithm is assessed for its strengths and limitations in specific tasks within the anti-corruption framework, such as relationship detection, fraud identification, and real-time analysis. Ethical implications of deploying AI in governance are also explored, particularly in balancing data privacy with the need for transparency. Design considerations include implementing robust security measures and adhering to local and international data governance standards. This paper offers a comprehensive roadmap for selecting the optimal AI algorithm in the development of AI-powered anti-corruption systems. By addressing technical, ethical, and operational challenges, the comparative analysis ensures the platform's adaptability and effectiveness in combating corruption across various sectors and regions.