Intelligent and Autonomous Systems in Government
Bibliographic record
Abstract
Artificial intelligence (AI)-driven autonomous and intelligent systems are increasingly shaping human life, with government-led AI projects playing a crucial role in both enhancing societal well-being and influencing AI policy. Implementing AI at national or regional scales presents some unique challenges including ensuring widespread access across diverse populations, guaranteeing fairness and accountability, and effectively communicating the impact of these technologies to the public. Our special issue presents six articles highlighting real-world experiences from ongoing and recently concluded government projects. The articles describe research that leverage autonomy and intelligence for various initiatives including safeguarding citizens and infrastructure from drone-based aerial threats, inspecting civilian infrastructure, cyber-security, conversational AI and responsible use of AI. We envisage that these articles will guide researchers with insights and best practices for ethically and effectively deploying AI in diverse government projects worldwide.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.043 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.012 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".