Legal aspects of using the artificial intelligence in commercial activities: ethical side, copyright, judicial practice
Bibliographic record
Abstract
This paper examines the legal aspects of using artificial intelligence (AI) in commercial activities, with a focus on copyright protection, and current judicial practice. The research analyzes the features of the legal status of AI-generated objects and reviews the legislation of Ukraine and other countries in this field. The study pays particular attention to three approaches to determining authorship of works created by artificial intelligence and the application of sui generis rights to non-original objects. The paper highlights that most countries' legislation, including Ukraine's, recognizes only natural persons as authors, excluding AI as a subject of copyright. While objects created with AI as an auxiliary tool may have authorship attributed to the user, objects generated by AI without human participation can be protected under special sui generis rights, which regulate property rights but do not grant moral rights. Through analysis of case law from the United States, Japan, China, the United Kingdom, Germany, and Canada, the paper demonstrates the global trend toward maintaining the requirement of "human authorship" as a key principle in intellectual property legislation. Courts consistently rule that only natural persons can be authors or inventors, and works or inventions created exclusively by artificial intelligence do not qualify for copyright or patent protection. The authors conclude that developing comprehensive legislation in the field of artificial intelligence is crucial for Ukraine to ensure effective protection of individual and legal entities' rights. Particular attention should be paid to issues of copyright protection and regulation of property relations arising from AI-generated content. It is also important to integrate international experience and develop national approaches to regulation, considering national specificities, to create a legal system that harmonizes the balance between technological development, ethical standards, and citizens' rights.
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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 | 0.016 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.041 |
| Scholarly communication | 0.018 | 0.011 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".