Applied Decision Analytics

Vol. 1 No. 1 (2025): Applied Decision Analytics
Articles

Balancing Efficiency and Risk in Public Sector Artificial Intelligence with Data Envelopment Analysis and Portfolio Approaches

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  • Doroteja Mitrović,
  • Gülay Demir,
  • Ibrahim Badi,
  • Mouhamed Bayane Bouraima
Doroteja Mitrović
Faculty of Organizational Sciences, University of Belgrade, Belgrade, Serbia
Gülay Demir
Sivas Cumhuriyet University, Vocational School of Health Services, Sivas, 58040, Türkiye
Ibrahim Badi
Department Mechanical Engineering, Libyan Academy-Misrata, 2949 Misrata, Libya
Mouhamed Bayane Bouraima
International School of Technical Education, Sichuan College of Architectural Technology, Deyang, 618000, Sichuan, China

Published 2025-10-03

Keywords

  • Artificial intelligence,
  • AI readiness,
  • Public sector efficiency,
  • Fuzzy risk assessment,
  • Resource optimization

Copyright (c) 2025 Doroteja Mitrović, Gülay Demir, Ibrahim Badi, Mouhamed Bayane Bouraima (Author)

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

Mitrović, D., Demir, G., Badi, I., & Bouraima, M. B. (2025). Balancing Efficiency and Risk in Public Sector Artificial Intelligence with Data Envelopment Analysis and Portfolio Approaches. Applied Decision Analytics , 1(1), 15-35. http://ada-journal.org/index.php/ada/article/view/4

Abstract

The integration of Artificial Intelligence (AI) in the public sector offers potential for improved efficiency but faces challenges related to readiness, infrastructure, and strategic planning. This study evaluates AI adoption across 151 countries using a novel framework that combines Data Envelopment Analysis (DEA), Fuzzy Logic, and Modern Portfolio Theory (MPT). DEA assesses efficiency levels, identifying top-performing nations and highlighting areas for improvement. Window analysis captures dynamic efficiency trends, providing a temporal perspective on AI readiness. Fuzzy Logic evaluates risks associated with AI implementation, focusing on key aspects such as governance, technology, and infrastructure. MPT optimizes resource allocation by balancing efficiency and risk, offering tailored investment strategies. The results identify Data Representativeness and Adaptability as critical dimensions for successful AI adoption, with significant disparities across nations. This approach provides actionable insights for policymakers to improve AI integration, reduce risks, and maximize benefits in public sector operations. The study underscores the need for dynamic, multi-dimensional strategies to address evolving challenges in AI implementation globally.

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