AI-träning och upphovsrätt - En analys av exemplarframställning, text- och datautvinning och transparenskrav - Pregén,Linn (2026)
The growing use of generative Artificial intelligence trained on large datasets has increased copyright questions when the training data consists of protected material. The thesis aims to analyze how the use of copyright-protected material as training data can be assessed under both Swedish and EU law, and how the AI Regulations transparency requirements come into play. The study is based on a legal dogmatic method and therefore addresses statutory law, EU directives, case law from the Court of Justice of the European Union, preparatory works, and relevant legal doctrine. AI training usually presupposes copying, which brings into consideration the reproduction right under section 2 of the Swedish Copyright Act, as well as the corresponding provisions in the Infosoc-directive. The central legal question therefore is not necessarily whether the exclusive right as such is affected, but under what conditions copying may be considered permissible. The thesis aslo presents how the provisions on text and data mining under sections 15 a-c of the Swedish Copyright Act may in certain cases be applicable to AI training, but only subject to conditions such as the requirement of lawful access and the possibility for right holders to reserve their rights through opt-out. Copies may, however, be permitted under the exceptions to a certain extent, but they must then be necessary or analyze-oriented, and may in some cases fall outside the scope of application when the work, through memorization, becomes reproducible in the AI model. The analyze shows that the substantive copyright rules have had a limited effect in practice, since right holders in many cases have lacked the possibility to detect and control the use of their works for the purpose of training an AI.
Keywords: Upphovsrätt; Artificiell intelligens; Träningsdata; Exemplarframställning; text- och datautvinning; transparenskrav; AI-förordningen. Intellectual Property; Artificial Intelligence; Training Data; Reproduction; text and data mining; transparency requirements; AI-act.