Neural Networks & Government Spending - Algorithmic Decision- Making in Public Procurement

Abstract

This review critically examines Neural Networks & Government Spending: Algorithmic Decision-Making in Public Procurement, a timely exploration of the transformative impact of artificial intelligence (AI) on public sector procurement processes. The book investigates how neural networks are increasingly shaping decisions in government spending, displacing traditional bureaucratic discretion with data-driven algorithms. Drawing from real-world case studies, the author explores the potential of AI to enhance efficiency, detect procurement fraud, and mitigate corruption, while simultaneously highlighting the legal, ethical, and operational risks posed by opaque and biased algorithms. Central to the discussion is the tension between algorithmic efficiency and the democratic need for transparency and accountability. The book interrogates the use of private technology firms in designing procurement systems, raising concerns about vendor lock-in, explainability, and public sector dependency on proprietary AI models. The book underscores the importance of governance structures, advocating for open-source models, stakeholder engagement, and regulatory safeguards to prevent algorithmic injustice and ensure public trust. Through comparative analyses and forward-looking perspectives, the book extends its implications beyond procurement to broader areas of public finance, calling for adaptive legislation and ethical oversight in the digital age. Ultimately, the work presents a compelling argument for integrating AI into public procurement not as a replacement for human judgment, but as an assistive tool governed by robust accountability mechanisms.