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Artificial Intelligence and Public Sector Auditing: Challenges and Opportunities for Supreme Audit Institutions

Author

Listed:
  • Dolores Genaro-Moya

    (Department of International and Spanish Economics, Faculty of Economics and Business Studies, University of Granada, 18071 Granada, Spain)

  • Antonio Manuel López-Hernández

    (Department of Accounting and Finance, Faculty of Economics and Business Studies, University of Granada, 18071 Granada, Spain)

  • Mariia Godz

    (Department of Computer Science and Artificial Intelligence, School of Computer and Telecommunication Engineering, University of Granada, 18014 Granada, Spain)

Abstract

The application of artificial intelligence (AI) is growing exponentially in public entities, contributing to the improvement of the design and provision of services, as well as to the internal management and efficiency of public institutions. However, the potential of artificial intelligence systems for the public sector also entails a set of risks related, among other areas, to privacy, confidentiality, security, transparency or bias and discrimination. The Supreme Audit Institutions (SAIs), when auditing public services and policies, must adapt their human and technological resources to this new scenario. This paper analyses the implications of AI penetration in the public sector, as well as the challenges that these technological developments pose to SAIs to improve effectiveness and efficiency in their auditing tasks. This paper presents a conceptual and exploratory analysis, informed by documentary evidence and case illustrations. Given the dynamic evolution of AI research, the findings should be interpreted as a contribution to ongoing debates, rather than definitive conclusions. It also reviews the status of the audits of systems based on algorithms carried out by some SAIs.

Suggested Citation

  • Dolores Genaro-Moya & Antonio Manuel López-Hernández & Mariia Godz, 2025. "Artificial Intelligence and Public Sector Auditing: Challenges and Opportunities for Supreme Audit Institutions," World, MDPI, vol. 6(2), pages 1-19, June.
    • Handle: RePEc:gam:jworld:v:6:y:2025:i:2:p:78-:d:1669832
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    References listed on IDEAS

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    2. Charlotte van Ooijen & Barbara Ubaldi & Benjamin Welby, 2019. "A data-driven public sector: Enabling the strategic use of data for productive, inclusive and trustworthy governance," OECD Working Papers on Public Governance 33, OECD Publishing.
    3. Morgan, Mary S., 2019. "Exemplification and the use-values of cases and case studies," LSE Research Online Documents on Economics 104057, London School of Economics and Political Science, LSE Library.
    4. Jamie Berryhill & Kévin Kok Heang & Rob Clogher & Keegan McBride, 2019. "Hello, World: Artificial intelligence and its use in the public sector," OECD Working Papers on Public Governance 36, OECD Publishing.
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