Law and technology: The impact of innovations on the legal system and its regulation
Bibliographic record
Abstract
The relevance of this study is due to the introduction of technologies into the legal sphere, as well as their rapid development, which causes the inconsistency of conventional legislation with the emerging social relations. Thus, the purpose of this study was to research the impact of digital technologies on the modern legal society and their legislative regulation to formulate ways to improve and further develop this area. The methods used in this study were the following: historical, comparative legal, statistical, forecasting. The main results of this study are as follows: the concepts of technology, innovation, digitalisation, and artificial intelligence were investigated; the legal regulation of these concepts in both Ukrainian and foreign legislation was examined. The study also identified the main problems and risks associated with the use of digital technologies, including problems related to user security, personal data protection, copyright. Solutions and legislative changes regulating the field of technology were also covered using evidence from the United States of America, Switzerland, Japan, the United Kingdom, Canada. The study analysed the impact of artificial intelligence on the ethical aspects of the work of a lawyer. The study also highlighted the future vision and consequences of the use of technology in various spheres of public life. It was found that digitalisation and the introduction of technology into public spheres of life require flexibility and readiness for change from the legal sphere, as well as the need to strike a balance between innovative changes and the guarantee of fundamental human rights. Considering the international standards that were investigated, it was found that the key area that requires additional protection in the digital age is data privacy and confidentiality. The findings of this study can be used as a basis for improving the legislative framework that governs relations in the field of technology use by lawyers, sociologists, and legislators
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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 | 0.009 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.028 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".