Оцінка ефективності застосування міжнародних стандартів та регуляторних актів з регулювання моделей штучного інтелекту для України
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.007 | 0.013 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 it