Artificial Intelligence Adoption in Public Administration: Evolution, Challenges and Global Perspectives
Bibliographic record
Abstract
The main objective of this article is to analyze and discuss whether there is a solid evolution in the use of Artificial Intelligence (AI) within public administrations globally. The study is supported by Institutional Theory; and methodologically, it promotes an analysis of the literature and reports addressing the use and evolution of AI in public administration worldwide, particularly in the countries that are part of the Organization for Economic Cooperation and Development (OECD). The aim was to identify the most relevant measures to improve the performance and increase the productivity of public services by employing the automation of administrative processes and the reduction of bureaucracy; by improving decision-making based on real-time data analysis; and by offering personalized services to citizens. The results of the discussions and analysis of the literature and reports demonstrated that the evolution of AI use in public administration is closely related to the structuring of a smart government, capable of meeting society’s demands for efficient public administration. It’s also important to note that expectations for the use of AI in public administrations globally are diverse and complex, which highlights the magnitude of the challenges and opportunities associated with its use. These transformations are having a beneficial impact on government operations, as AI generates opportunities that impact efforts to improve public management performance.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".