Towards the Status of Classification of Artificial Intelligence as a Subject of Law
Bibliographic record
Abstract
As a result of digital transformation in the 21st century, large volumes of data have been accumulated, respective algorithms have been created, and digital advancements are being implemented in almost all key areas. That experience has emphasised the role and importance of technologies, including artificial intelligence (AI), in modern life. Both in the public and private sectors it is possible to achieve a number of goals and objectives remotely, from different countries around the world, using automated approaches. Operating in the digital world without specific boundaries assigns more global meaning to the digital transformation process and significantly increases the scope of opportunities. To adapt to digital reality, certain traditional approaches should be changed, including in the area of jurisprudence. While information and communication technologies are rapidly developing, there is a need for legal regulation of technology-related issues. However, at the same time, new norms should support digital revolution and innovative approaches. In this context, it is necessary to determine the legal status of AI. Therefore, in the wake of the development of an international digital order, the present article aims to explore the strategic, ethical and legal frameworks of AI. This will help to determine to what extent it is possible to assign to AI the status of a subject of law.
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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 | 0.047 | 0.058 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.013 | 0.125 |
| Scholarly communication | 0.035 | 0.032 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.012 | 0.019 |
| 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".