Artificiell intelligens inom upphovsrätt och behovet av internationell harmonisering
Bibliographic record
Abstract
The development of artificial intelligence (AI) has accelerated rapidly in recent years, and AI tools that can be used for advanced cognitive and perceptual tasks are appearing in all areas of life. It did not take long before it was also realized that AI could be used for literature and art. In a matter of seconds, advanced works of art and literature can be produced at the touch of a button using so-called generative AI. This has led many to question whether, to what extent and on what grounds protection can be obtained for this category of works. Compared to other areas of law, intellectual property law, and in particular copyright law, has been subject to extensive international harmonisation efforts. This is because intellectual property rights, which, unlike tangible goods, cannot be physically confined within the borders of a particular country, are by their nature highly international. The desire to ensure that the rights of national authors are also protected abroad, together with the value of intellectual property rights as international commodities, has therefore led to a number of international conventions, such as the Berne Convention for the Protection of Literary and Artistic Works, which together establish a harmonised global minimum level of protection for copyright works. The advent of AI in the field of copyright raises the question of whether this relative global consensus has been disrupted. Unlike previous technologies that have impacted copyright, AI is fundamentally new in that it reduces the need for human creativity, or perhaps replaces it altogether. Even at this early stage, it is clear that different countries have taken different approaches to how AI works should be protected under their respective copyright regimes. In the United States, a series of decisions by the United States Copyright Office suggest that the use of so-called prompts to create works is not considered sufficient for copyright protection, regardless of how many such prompts are used. In contrast, countries such as the United Kingdom, Ireland, New Zealand, Hong Kong, India and South Africa offer specific protection for works created by computers without human intervention, and countries such as Canada and India have allowed the AI tool itself to be registered as a co-author of the work. This raises the question of whether the international copyright framework is sufficient to deal with developments in AI, or whether there is a need for further harmonisation. The paper examines this question from a number of perspectives, including economic, ethical and legal. The conclusion is that several circumstances indicate that further harmonisation is desirable, at least from a Swedish perspective.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.027 | 0.009 |
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".