Artificial Intelligence in Public Procurement: Legal Frameworks, Ethical Challenges, and Policy Solutions for Transparent and Efficient Governance

Abstract

Public procurement systems worldwide grapple with inefficiency, opacity, and corruption, undermining public trust and squandering taxpayer funds. Artificial Intelligence (AI) presents a transformative opportunity to enhance transparency, streamline processes, and mitigate fraud in procurement and infrastructure financing. This paper examines the legal and policy implications of integrating AI into public procurement, focusing on its potential to strengthen compliance, detect anomalies, and optimize decision-making while addressing inherent risks. The discussion begins by outlining the legal frameworks governing procurement, including domestic regulations (e.g., the U.S. Federal Acquisition Regulation) and international standards (e.g., UNCITRAL Model Law), identifying gaps in enforcement and oversight. It then explores AI applications—such as automated contract analysis, fraud detection algorithms, and predictive vendor risk assessments—that can improve efficiency and accountability. However, deploying AI also introduces legal and ethical challenges, including algorithmic bias, due process concerns, and accountability gaps in automated decision-making. To harness AI responsibly, this paper proposes policy measures such as mandatory algorithmic audits, interdisciplinary oversight bodies, and pilot programs for public agencies. It emphasizes the need for collaboration between legal experts, technologists, and policymakers to ensure AI adoption aligns with public interest and legal integrity. Ultimately, while AI offers significant benefits, its implementation must be carefully regulated to uphold fairness, transparency, and the rule of law in public procurement.