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Intellectual Property Implications Of Training Generative AI Models On Copyrighted Works: A Comprehensive Legal Analysis




Prachi Kumari, Maharashtra National Law University Chhatrapati Sambhajinagar


ABSTRACT


The advent of Generative Artificial Intelligence (AI) has transformed creative industries and tested conventional perceptions about copyright law. Generative AI models like ChatGPT and Stable Diffusion are built on enormous datasets comprising copyrighted work, thereby creating problematic concerns regarding authorship, ownership, and infringement. This research embarks on a comparative and doctrinal analysis of the law implications of AI training on copyright materials between jurisdictions such as the United States, the European Union, and India. It considers whether doctrines like fair use, fair dealing, transformative use, and text-and-data mining (TDM) exceptions apply to AI systems. The study discovers that current copyright regimes fall short of tackling algorithmic creativity and suggests a balanced legal framework through compulsory licensing, transparency in datasets, and sui generis rights. The paper concludes that harmonized international norms are needed to protect creators' rights while promoting responsible AI innovation.


Keywords: Generative Artificial Intelligence; Copyright Law; AI Training Data; Fair Use and Fair Dealing; Text and Data Mining (TDM); Transformative Use; Algorithmic Creativity; Comparative Copyright Law; Authorship and Ownership; International Harmonization



Indian Journal of Law and Legal Research

Abbreviation: IJLLR

ISSN: 2582-8878

Website: www.ijllr.com

Accessibility: Open Access

License: Creative Commons 4.0

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All research articles published in The Indian Journal of Law and Legal Research are fully open access. i.e. immediately freely available to read, download and share. Articles are published under the terms of a Creative Commons license which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

 

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The opinions expressed in this publication are those of the authors. They do not purport to reflect the opinions or views of the IJLLR or its members. The designations employed in this publication and the presentation of material therein do not imply the expression of any opinion whatsoever on the part of the IJLLR.

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