Can Artificial Intelligence Author Laws: A Perspective from Russia
Bibliographic record
Abstract
This article discusses the issue of the introduction of digital technologies into policy-making. The article describes several systems of policy-making in the Russian Federation. In addition, the article discusses the issue of the introduction of a new System of policy-making in the light of the digital transformation of the Russian economy. The paper analyzes the capacities of digital technologies, including artificial intelligence (AI), in the context of their application in policy-making. The authors conclude that there are prerequisites and opportunities for deeper automation of the policy-making. This can improve the quality of the bills, can increase public involvement in the policy-making process, and speed up the development and adoption of new regulations. An intelligent system can develop legislative bills that are of superior technical quality and are non-contradictory in the context of both national and international legal systems. Digitalization processes should naturally lead to changes in the mechanism of policy-making, which in turn should lead to its greater automation. Moreover, insufficient automation today can become an obstacle in the digital transformation of the Russian economy. The authors conclude that in the future it would be possible for intellectual systems to author bills. The general development of AI systems shows that given the parameters of the problem and given the circumstance when the machine would be able to detect a center of social tensions in the community, the intelligent system itself would be capable of making proposals in the field of legislative regulation. The application of intelligent systems in policy-making is not without its drawbacks. Such systems are not transparent in the legal and technical sense and can also transfer human beliefs into the texts of the regulations. These problems can be addressed through public scrutiny and the introduction of a licensing system, however even this would create a number of new practical challenges.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.022 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".