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Advancing AI governance with a unified theoretical framework: a systematic review

Pedro Robles & Daniel J. Mallinson

Perspectives on Public Management & Governance2025https://doi.org/10.1093/ppmgov/gvaf013review
AJG 2
Weight
0.46

What the paper says

Governments worldwide are challenged to regulate emergent AI technologies while fostering innovation. Existing AI governance frameworks, though numerous (over 100), often focus narrowly on specific sectors, lack adaptability to evolving technologies, and fail to balance ethical standards with public accountability. This systematic review identifies these gaps, particularly in frameworks that prioritize either the public or private sector. The Unified AI Governance Framework (UAIGF) offers a unified, flexible model that integrates core and peripheral principles across sectors. Unlike existing frameworks, which are often limited in scope or focus on specific sectors, the UAIGF adapts to both sectoral needs and the rapid technological advancements, providing governments with a comprehensive tool to navigate the evolving landscape of AI regulation. By synthesizing shared principles, the UAIGF bridges sectoral divides, ensuring both innovation and accountability in high-stakes areas like healthcare and finance. The UAIGF represents a necessary step toward addressing the fragmented efforts in AI governance, offering a cohesive and adaptable approach for future regulation.

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https://doi.org/https://doi.org/10.1093/ppmgov/gvaf013

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@article{pedro2025,
  title        = {{Advancing AI governance with a unified theoretical framework: a systematic review}},
  author       = {Pedro Robles & Daniel J. Mallinson},
  journal      = {Perspectives on Public Management & Governance},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1093/ppmgov/gvaf013},
}

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Advancing AI governance with a unified theoretical framework: a systematic review

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Evidence weight

0.46

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.37 × 0.4 = 0.15
M · momentum0.57 × 0.15 = 0.09
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.