Tools for adaptating Ukraine’s artificial intelligence ecosystem to meet European Union standards
Bibliographic record
Abstract
This article delves into the preparation of Ukraine’s AI industry for the adoption of EU standards. The author evaluates six tools outlined in the 2023 Roadmap for the Regulation of AI in Ukraine and their potential application within the AI ecosystem. They are designed to foster the advancement of AI technologies in Ukraine while ensuring compliance with EU standards. It is imperative for government authorities to establish favorable conditions to facilitate the seamless integration of the EU AI Law in the future. The research demonstrates the auxiliary measures that can be employed to synchronize the Ukrainian legislation with the advancement of AI ecosystem. These adaptation tools also play a pivotal role in driving the industry’s growth. This discussion pertains to realizing the scientific, technical, and socio-economic potential of Ukraine’s information and communication technology sphere. The article discusses the significance of regulatory sandboxes and outlines methodologies for testing AI technologies and systems. It defines the tasks of labeling input data for machine learning and output data for generative AI, as well as labeling the AI systems themselves. The author explains the drafting of atypical acts within the EU legal system, such as white papers and codes of conduct, for adaptation. The article provides examples of instructions and recommendations for industry development in compliance with the EU AI Act standards. Furthermore, the author summarizes the role of each tool and suggests expanding the Roadmap to include software for developing and AI educational courses. The study contributes to the ongoing public debate on whether Ukraine requires an AI strategy alongside a government concept. It also includes examples of how the researched tools have been implemented in leading countries such as Canada, Great Britain, Japan, Singapore, the USA. Additionally, it showcases international initiatives within the G7 framework (International Code of Conduct for Organizations Developing Advanced AI Systems) and the Council of Europe (HUDERIA).
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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 | 0.018 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".