Exploration of Audit Technologies in Public Security Agencies: Empirical Research from Portugal
Bibliographic record
Abstract
The integration of artificial intelligence (AI) in the public sector is driving significant advancements in governance and management, changing the way public organizations operate. In particular, AI technologies have a profound impact on auditing practices, enhancing efficiency and accountability. This article aims to explore how AI can improve audit processes in a Portuguese public security agency, focusing on its transformative potential in streamlining tasks such as data extraction, analysis, and verification. Using a qualitative research approach, the study employs custom Python algorithms to examine the integration of key indicators into the audit process, specifically through the analysis of economic classification and expenditure limits. The findings demonstrate that personalized algorithms can reduce manual workloads, improve accuracy, and strengthen compliance with financial regulations, providing valuable contributions for decision-making. However, challenges such as data privacy and infrastructure investment remain, emphasizing the need for further research. Future studies should focus on adapting AI-based auditing models to various public administration contexts, addressing organizational changes, and advancing public governance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".