Intelligent and Autonomous Systems in Government
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it