Analysis of Intrabranch and Legal Regulation of Artificial Intelligence Technologies Using the Example of International Experience, the Experience of Foreign Countries and the Russian Federation
Bibliographic record
Abstract
The purpose of the study is to analyze intrabranch and legal regulating relations related to the development and application of artificial intelligence technologies. The documents of the strategic development of the industry, regulatory documents, and other documents directly and indirectly related to artificial intelligence technologies were studied. For example, the following are: the act of the Asilomar Conference, acts of the Council of Europe, acts of the European Union, the act of the Organization for Economic Cooperation and Development, the G20 Act, regulatory and technical documents of the United States, China, Canada, Denmark, France, the Russian Federation, as well as some bills. The analysis revealed: the insufficiency of regulatory regulation of the artificial intelligence branch, the shortcomings of national regulation of the artificial intelligence branch in some countries, the dependence of norms on the political regime, the duration and untimeness of the development of regulations, the lack of coherence in the development and application of artificial intelligence technologies at the interstate level.
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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 | 0.011 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".