Artificiell intelligens inom upphovsrätt och behovet av internationell harmonisering
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,002 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».