Assessment of the effectiveness of international standards and regulatory acts governing artificial intelligence models for Ukraine
Bibliographic record
Abstract
The article presents a comprehensive study of the effectiveness of international standards and regulatory acts in the field of artificial intelligence (AI) with the aim of determining the most appropriate model for their implementation in Ukraine. The aim of the work is to conduct a comparative assessment of the leading approaches to AI regulation developed in the EU, the US, Canada, the UK, South Korea, China and other countries, as well as recognised international ISO/IEC standards. The study is based on the comprehensive use of multi-criteria analysis methods: pairwise comparisons, determination of weight coefficients on the Fishburn scale, ranking methods, scoring and numerical evaluation. The assessment was carried out using a system of unconditional and conditional criteria, including validity, flexibility, ethics, complexity, progressiveness, level of transparency of algorithms, prospects for integration, availability of institutional support, and impact on innovative development. The use of the MathCad environment allowed for mathematical modelling and calculation of integral performance indicators. The results showed that the most balanced and promising for implementation in Ukraine are the European approach (EU AI Act) and the Canadian approach (AIDA), which demonstrate a high level of regulatory maturity, transparency of regulatory procedures, the presence of an ethical component, and effective institutional support. The American approach (NIST AI RMF) and the ISO/IEC 23894 standard took intermediate positions due to their versatility and flexibility. In contrast, the Chinese model showed the lowest adaptability to Ukrainian conditions due to the dominance of centralised control principles. The proposed assessment methodology can be used to develop a national AI regulation strategy in Ukraine aimed at ensuring a balance between security, ethics and innovative technological development.
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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 | 0.030 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".