This thesis examines the intricate interplay between technological advancement, copyright law, and Artificial Intelligence creations. Technological progress had a profound impact on copyright law, culminating in the WIPO Copyright Treaty's implementation. Firstly, the new mechanisms granted to creators to safeguard their works against unauthorized access and use, namely technological protection measures and rights management information are examined. Then, the first chapter scrutinizes the uncertainties arising from this provision, focusing on the concept of the access right. The focus then shifts to the definition of AI, a concept challenged by its multifaceted nature. The thesis offers a short overview of the debate on AI as a creative author; afterwards, it delves into Generative AI's initial stage, dissecting models' role in output generation. Notably, the Diffusion Model and Large Language Models like ChatGPT are discussed: the chapter dissects the Machine Learning process, highlighting its copyright implications, focusing on concerns over training data use. Addressing responsibility for AI-generated copyright infringement, the chapter delves into the pathways through which AI-generated works might violate copyright regulations. The dissertation delves into the legal intricacies of AI-generated works in the context of copyright law. It examines the recourse to the fair use doctrine in the US. Through the analysis of the historical developments of such doctrine, the challenges of adapting fair use to creative AI are explored. The final chapter shifts its focus to the European Union's copyright framework, centering on the Digital Single Market Directive and the recent AI Act. The chapter probes whether dataset reproduction could qualify as temporary reproduction under EU law, drawing parallels with precedents like the Infopaq case. The Text and Data Mining Exception's role in AI is also explored, examining its potential for limiting liability in the realm of Machine Learning.
This thesis examines the intricate interplay between technological advancement, copyright law, and Artificial Intelligence creations. Technological progress had a profound impact on copyright law, culminating in the WIPO Copyright Treaty's implementation. Firstly, the new mechanisms granted to creators to safeguard their works against unauthorized access and use, namely technological protection measures and rights management information are examined. Then, the first chapter scrutinizes the uncertainties arising from this provision, focusing on the concept of the access right. The focus then shifts to the definition of AI, a concept challenged by its multifaceted nature. The thesis offers a short overview of the debate on AI as a creative author; afterwards, it delves into Generative AI's initial stage, dissecting models' role in output generation. Notably, the Diffusion Model and Large Language Models like ChatGPT are discussed: the chapter dissects the Machine Learning process, highlighting its copyright implications, focusing on concerns over training data use. Addressing responsibility for AI-generated copyright infringement, the chapter delves into the pathways through which AI-generated works might violate copyright regulations. The dissertation delves into the legal intricacies of AI-generated works in the context of copyright law. It examines the recourse to the fair use doctrine in the US. Through the analysis of the historical developments of such doctrine, the challenges of adapting fair use to creative AI are explored. The final chapter shifts its focus to the European Union's copyright framework, centering on the Digital Single Market Directive and the recent AI Act. The chapter probes whether dataset reproduction could qualify as temporary reproduction under EU law, drawing parallels with precedents like the Infopaq case. The Text and Data Mining Exception's role in AI is also explored, examining its potential for limiting liability in the realm of Machine Learning.
Copyright issues of works generated by Artificial Intelligence
ANSELMETTI, ALICE
2022/2023
Abstract
This thesis examines the intricate interplay between technological advancement, copyright law, and Artificial Intelligence creations. Technological progress had a profound impact on copyright law, culminating in the WIPO Copyright Treaty's implementation. Firstly, the new mechanisms granted to creators to safeguard their works against unauthorized access and use, namely technological protection measures and rights management information are examined. Then, the first chapter scrutinizes the uncertainties arising from this provision, focusing on the concept of the access right. The focus then shifts to the definition of AI, a concept challenged by its multifaceted nature. The thesis offers a short overview of the debate on AI as a creative author; afterwards, it delves into Generative AI's initial stage, dissecting models' role in output generation. Notably, the Diffusion Model and Large Language Models like ChatGPT are discussed: the chapter dissects the Machine Learning process, highlighting its copyright implications, focusing on concerns over training data use. Addressing responsibility for AI-generated copyright infringement, the chapter delves into the pathways through which AI-generated works might violate copyright regulations. The dissertation delves into the legal intricacies of AI-generated works in the context of copyright law. It examines the recourse to the fair use doctrine in the US. Through the analysis of the historical developments of such doctrine, the challenges of adapting fair use to creative AI are explored. The final chapter shifts its focus to the European Union's copyright framework, centering on the Digital Single Market Directive and the recent AI Act. The chapter probes whether dataset reproduction could qualify as temporary reproduction under EU law, drawing parallels with precedents like the Infopaq case. The Text and Data Mining Exception's role in AI is also explored, examining its potential for limiting liability in the realm of Machine Learning.File | Dimensione | Formato | |
---|---|---|---|
945034_dissertationanselmettialice.pdf
non disponibili
Tipologia:
Altro materiale allegato
Dimensione
887.4 kB
Formato
Adobe PDF
|
887.4 kB | Adobe PDF |
I documenti in UNITESI sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
https://hdl.handle.net/20.500.14240/150812