From principles to practice: The case for coordinated international LLMs supervision
Bibliographic record
Abstract
Abstract This paper examines the rise of monitoring schemes to coordinate supervisors and market authorities in addressing the cross-industry challenges posed by large language models’ deployment. As artificial intelligence (AI) intersects with the core mandate of market authorities dealing with financial stability, data protection, intellectual property, competition and telecommunications, effective oversight requires collaboration and information sharing. Using examples such as the Canadian Digital Regulator’s Forum, the UK’s Digital Regulation Cooperation Forum and the European Union’s AI Act implementation process, the paper illustrates how national and international institutional coordination can help operationalizing the high level principles on AI governance which are currently discussed in international fora. Ultimately, this approach aims to ensure responsible AI development while addressing risks and maximizing its societal benefits.
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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 | 0.107 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.078 |
| Scholarly communication | 0.022 | 0.017 |
| Open science | 0.004 | 0.020 |
| Research integrity | 0.013 | 0.016 |
| Insufficient payload (model declined to judge) | 0.008 | 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".