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Byte the Beat: Copying or Creating?

Rydberg, Amanda LU (2024) JURM02 20241
Department of Law
Faculty of Law
Abstract
Artificial intelligence (AI) and text and content-generating tools have been around for a while, bringing significant changes, challenges, and opportuni-ties, especially regarding copyrighted materials such as music. As AI sys-tems have become more sophisticated in generating new creative works, questions about these systems' potential copyright infringement have gained prominence.
This thesis delves into the legal landscape where creativity and technology intersect and explores the complexities surrounding copyrighted music's use in training AI models and the subsequent creation of new musical out-puts. The central focus is whether it constitutes a copyright infringement under EU law when generative AI mimics an artist's sound and... (More)
Artificial intelligence (AI) and text and content-generating tools have been around for a while, bringing significant changes, challenges, and opportuni-ties, especially regarding copyrighted materials such as music. As AI sys-tems have become more sophisticated in generating new creative works, questions about these systems' potential copyright infringement have gained prominence.
This thesis delves into the legal landscape where creativity and technology intersect and explores the complexities surrounding copyrighted music's use in training AI models and the subsequent creation of new musical out-puts. The central focus is whether it constitutes a copyright infringement under EU law when generative AI mimics an artist's sound and style.
To address this, the thesis will examine how EU law handles the use of copyrighted musical works in training AI models and how EU law address-es the copyright status of AI-generated musical outputs that imitate existing artists.
These questions are explored through a legal dogmatic method. A compara-tive perspective is also incorporated to a lesser extent to examine how dif-ferent legal frameworks address these issues.
Musical works and their related right, such as phonograms, are generally protected by law, granting rightsholders certain exclusive rights, such as the right to reproduce the work. However, when AI generates music, the musi-cal pieces are often reproduced multiple times in the training phase of the AI system. The thesis finds that these reproductions are not covered by the exemption for temporary acts of reproduction in Article 5(1) InfoSoc Di-rective. The exemption for text and data mining in Article 3 and Article 4 of the Directive (2019/790) on copyright and related rights in the Digital Single Market (DSM Directive) can potentially cover some reproductions of copyrighted musical works. Still, these exemptions are narrow in scope. Article 3 applies research organisations and cultural heritage institutions that conduct text and data mining activities on a non-commercial basis. Moreover, the general exemption in Article 4 faces practical hindrances, as rightsholders can reserve their right to prevent their works from being mined. Therefore, the mentioned provisions are not sufficient for AI music generators to train their generative models on copyright-protected music in most cases.
AI-generated output may constitute a derivative work. However, each case must be assessed individually, as some AI-generated musical outputs might include samples of existing works, whilst others may only be similar in style, which generally is not protected by copyright. The AI-generated voice is also typically not protected by copyright, as the voice is not con-sidered fixed in a tangible medium or reflective of the author's own intel-lectual creation in most circumstances. Thus, while the output may stay within the legal framework, the input often does not comply with EU copy-right laws, rendering the overall process of generative AI mimicking an artist's sound and style unlawful. (Less)
Abstract (Swedish)
Artificiell intelligens (AI) och dess genererande verktyg har funnits ett tag
och medför med detta betydande förändringar, utmaningar och möjligheter,
särskilt för upphovsrättsskyddat material såsom musik. I takt med att AI-system har blivit mer sofistikerat i att generera nya kreativa verk, har frågor om
dessa system och deras potentiella upphovsrättsintrång fått ökad uppmärksamhet.

Den här uppsatsen undersöker därav det juridiska landskapet där kreativitet
och teknik möts för att utforska hur användningen av upphovsrättsskyddad
musik vid träning av AI-modeller samt det efterföljande alstret av nya
musikaliska verk förhåller sig till upphovsrättslagstiftning. Fokus ligger på
huruvida det utgör ett upphovsrättsintrång enligt... (More)
Artificiell intelligens (AI) och dess genererande verktyg har funnits ett tag
och medför med detta betydande förändringar, utmaningar och möjligheter,
särskilt för upphovsrättsskyddat material såsom musik. I takt med att AI-system har blivit mer sofistikerat i att generera nya kreativa verk, har frågor om
dessa system och deras potentiella upphovsrättsintrång fått ökad uppmärksamhet.

Den här uppsatsen undersöker därav det juridiska landskapet där kreativitet
och teknik möts för att utforska hur användningen av upphovsrättsskyddad
musik vid träning av AI-modeller samt det efterföljande alstret av nya
musikaliska verk förhåller sig till upphovsrättslagstiftning. Fokus ligger på
huruvida det utgör ett upphovsrättsintrång enligt EU-lagstiftningen när generativ AI imiterar en artists stil?

För att besvara denna fråga kommer uppsatsen att undersöka hur EU-lagstiftningen hanterar användningen av upphovsrättsskyddade musikaliska verk vid
träning av AI-modeller. Samt hur EU-lagstiftningen hanterar AI-genererade
musikaliska alster som imiterar befintliga artister.

Dessa frågor utforskas genom en rättsdogmatisk metod samt ett komparativt
perspektiv i mindre utsträckning för att undersöka hur olika rättssystem hanterar dessa frågor.

Musikverk och deras närstående rättigheter, såsom fonogram, är i allmänhet
skyddade av lag som ger rättighetsinnehavare vissa exklusiva rättigheter
såsom rätten att mångfaldiga verket. När AI genererar musik, reproduceras
musikstyckena oftast i flera steg under träningsfasen av AI-systemet.
Uppsatsen konstaterar att dessa reproduktioner inte täcks av undantaget för
tillfälliga exemplarframställningar i Artikel 5(1) InfoSoc-direktivet. Undantaget för text- och datautvinning i Artikel 3 och Artikel 4 i direktivet (2019/790)
om upphovsrätt och närstående rättigheter på den digitala inre marknaden
(DSM-direktivet) kan potentiellt omfatta vissa reproduktioner av upphovsrättsskyddade musikverk, men dessa undantag är snäva i sin omfattning. Artikel 3 omfattar forskningsorganisationer och kulturarvsinstitutioner som bedriver text- och datamining på icke-kommersiell basis. Det
allmänna undantagen i artikel 4 möter förövrigt praktiska hinder då rättighetsinnehavare kan förbehålla sig rätten att deras verk utvinns. Dessa
bestämmelser är därav inte tillräckliga i de flesta fall för att AI-musikgeneratorer ska kunna träna sina modeller på upphovsrättsskyddad musik.

AI-genererad alster kan även utgöra en bearbetning. Varje fall måste dock
bedömas individuellt, eftersom vissa AI-genererade musikaliska alster kan
innehålla sampling av befintliga verk, medan andra endast liknande tidigare
verk i dess stil, vilket generellt sett inte är skyddat av upphovsrätt. Den AI-genererade rösten är vanligtvis inte heller skyddad av upphovsrätt, eftersom
rösten inte anses vara fixerad eller reflekterande av författarnas egna intellektuella skapelser. Således, även om det genererade alstret kan hålla sig inom
det rättsliga ramverket så överensstämmer träningsprocessen oftast inte med
EU:s upphovsrättslagar, vilket gör den övergripande processen där generativ
AI imiterar en artists ljud och stil olaglig. (Less)
Please use this url to cite or link to this publication:
author
Rydberg, Amanda LU
supervisor
organization
alternative title
Byte the Beat: Copying or Creating?
course
JURM02 20241
year
type
H3 - Professional qualifications (4 Years - )
subject
keywords
copyright law, copyright, AI
language
English
id
9153191
date added to LUP
2024-06-18 10:10:14
date last changed
2024-06-18 10:10:14
@misc{9153191,
  abstract     = {{Artificial intelligence (AI) and text and content-generating tools have been around for a while, bringing significant changes, challenges, and opportuni-ties, especially regarding copyrighted materials such as music. As AI sys-tems have become more sophisticated in generating new creative works, questions about these systems' potential copyright infringement have gained prominence. 
This thesis delves into the legal landscape where creativity and technology intersect and explores the complexities surrounding copyrighted music's use in training AI models and the subsequent creation of new musical out-puts. The central focus is whether it constitutes a copyright infringement under EU law when generative AI mimics an artist's sound and style. 
To address this, the thesis will examine how EU law handles the use of copyrighted musical works in training AI models and how EU law address-es the copyright status of AI-generated musical outputs that imitate existing artists.
These questions are explored through a legal dogmatic method. A compara-tive perspective is also incorporated to a lesser extent to examine how dif-ferent legal frameworks address these issues. 
Musical works and their related right, such as phonograms, are generally protected by law, granting rightsholders certain exclusive rights, such as the right to reproduce the work. However, when AI generates music, the musi-cal pieces are often reproduced multiple times in the training phase of the AI system. The thesis finds that these reproductions are not covered by the exemption for temporary acts of reproduction in Article 5(1) InfoSoc Di-rective. The exemption for text and data mining in Article 3 and Article 4 of the Directive (2019/790) on copyright and related rights in the Digital Single Market (DSM Directive) can potentially cover some reproductions of copyrighted musical works. Still, these exemptions are narrow in scope. Article 3 applies research organisations and cultural heritage institutions that conduct text and data mining activities on a non-commercial basis. Moreover, the general exemption in Article 4 faces practical hindrances, as rightsholders can reserve their right to prevent their works from being mined. Therefore, the mentioned provisions are not sufficient for AI music generators to train their generative models on copyright-protected music in most cases. 
AI-generated output may constitute a derivative work. However, each case must be assessed individually, as some AI-generated musical outputs might include samples of existing works, whilst others may only be similar in style, which generally is not protected by copyright. The AI-generated voice is also typically not protected by copyright, as the voice is not con-sidered fixed in a tangible medium or reflective of the author's own intel-lectual creation in most circumstances. Thus, while the output may stay within the legal framework, the input often does not comply with EU copy-right laws, rendering the overall process of generative AI mimicking an artist's sound and style unlawful.}},
  author       = {{Rydberg, Amanda}},
  language     = {{eng}},
  note         = {{Student Paper}},
  title        = {{Byte the Beat: Copying or Creating?}},
  year         = {{2024}},
}