Mapping Meta-Governance and Artificial Intelligence in the public sector

Authors

DOI:

https://doi.org/10.21874/e606pf49

Keywords:

meta-governance, artificial intelligence, public sector

Abstract

The growing demand for innovation and efficiency has propelled the adoption of strategies such as meta-governance and Artificial Intelligence (AI) in the public sector. However, studies that systematize the benefits and challenges associated with the combined application of these approaches remain limited. Objective: The objective was to map and analyze the main benefits and challenges related to the use of meta-governance and AI in enhancing governance and decision-making in the public sector. Materials and Methods: This study adopts a Systematic Literature Mapping (SLM), based on the Population, Intervention, Comparison, Outcome, and Context (PICOC) framework, using searches conducted in the Scopus database and study selection guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol, resulting in the inclusion of 22 articles published between 2014 and 2024. Results: The findings indicate that meta-governance and AI are complementary strategies in the digital transformation of the public sector, although they face barriers such as algorithmic opacity, lack of technical training, and absence of specific regulation. Conclusion: The synergy between these strategies can strengthen public governance, promoting more effective, transparent, and evidence-based decision-making.

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Author Biographies

  • Rodrigo José Lima Almeida, Universidade Federal do Tocantins (UFT), Palmas – TO, Brasil

    PhD candidate in Governance and Digital Transformation, Federal University of Tocantins (UFT). Master’s degree in Public Policy Management and Social Security, Federal University of Recôncavo da Bahia (UFRB). Public servant of the State of Tocantins.

  • Daniela Mascarenhas de Queiroz Trevisan, Universidade Federal do Tocantins (UFT), Palmas – TO, Brasil

    PhD and Master’s degree in Computational Modeling, Federal University of Tocantins (UFT). Permanent faculty member of the Graduate Program in Governance and Digital Transformation, Federal University of Tocantins (UFT).

  • Leonardo de Andrade Carneiro, Universidade Federal do Tocantins (UFT), Palmas – TO, Brasil

    PhD in Regional Development, Federal University of Tocantins (UFT). Collaborating professor in the Graduate Program in Governance and Digital Transformation, Federal University of Tocantins (UFT). Member of the Higher Education Council, Military Police of the State of Tocantins (PMTO).

  • Rafael Lima de Carvalho, Doutor em Engenharia de Sistemas e Computação, Universidade Federal do Rio de Janeiro (UFRJ). Professor adjunto, Universidade Federal do Tocantins (UFT).

    PhD in Systems and Computer Engineering from the Federal University of Rio de Janeiro (UFRJ). Adjunct Professor at the Federal University of Tocantins (UFT).

  • Gentil Veloso Barbosa, Universidade Federal do Tocantins (UFT), Palmas – TO, Brasil

    PhD in Systems and Computer Engineering, Federal University of Rio de Janeiro (UFRJ). Professor, Federal University of Tocantins (UFT).

  • Valéria Perim da Cunha, Universidade Federal do Tocantins (UFT), Palmas – TO, Brasil

    PhD candidate in Governance and Digital Transformation, Federal University of Tocantins (UFT). Master’s degree in Intellectual Property and Technology Transfer for Innovation, Federal University of Tocantins (UFT).

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Published

2026-09-10

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Vol. 77 No. 2 (2026): Revista do Serviço Público, v.77, n.2, 2026

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How to Cite

Mapping Meta-Governance and Artificial Intelligence in the public sector. (2026). Revista Do Serviço Público, 77(2), 451-472. https://doi.org/10.21874/e606pf49