Comparative Analysis of Generative AI Risks in the Public Sector
Bibliographic record
Abstract
The landscape of artificial intelligence (AI) has experienced a monumental shift with the emerging of Generative AI (GenAI), which has demonstrated to be a transformative tool across diverse sectors. GenAI outputs can span various digital formats, including text, images, videos, and audio, generating particular interest in the public sector. The growing interest of governments in integrating GenAI technologies in public sector operations is marked by the creation of emerging governance instruments and the formulation of soft laws, like standards, principles, and guidelines. This study aims to delve into the intricacies and potential risks associated with the deployment of GenAI within government. Through a qualitative content analysis, the research meticulously examines GenAI usage guidelines issued by Australia, Canada, New Zealand, the United Kingdom, and South Korea. The objective is to discern the risks acknowledged by these countries' soft laws and compare them with the risks identified by scholars in the field. The performed comparative analysis across countries suggest that the use of GenAI in the public sector raises common risks such as information leakage, data privacy, security, and concerns over public trust. By elucidating the varied risk perceptions across different national contexts, this study provides theoretical and practical implications related to the risks of GenAI within the public sector. Moreover, it sets a foundation for future research and policy development, ensuring that generative AI is used as a force for good in public governance.
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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 | 0.026 | 0.079 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".