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An Analysis Of Intellectual Property Challenges For AI-Generated Works In The Entertainment Industry




Shaurya Kapoor, The Shri Ram School, Aravali, Haryana, India


ABSTRACT


The rapid advancement of generative artificial intelligence (AI) has transformed creative processes within the entertainment industry, enabling new forms of content production. However, existing intellectual property (IP) frameworks largely fail to address the complexities arising from AI- generated works, particularly regarding authorship and ownership. Most jurisdictions require a natural person to be named as an author or inventor, leaving fully autonomous AI creations unprotected. This paper examines the legal ambiguities surrounding both the input data used to train AI models and the resulting AI-generated outputs, highlighting divergent international approaches. Through an analysis of landmark cases and evolving legislation, it explores how current IP laws struggle to balance innovation, protection of human creators, and ethical AI usage, further examining the implications for the entertainment industry, specifically within the music and film sectors. The study argues for urgent reform of international IP frameworks to establish clear guidelines for AI-generated content to ensure fairness, foster innovation, and address emerging risks in the global entertainment landscape.


Keywords: AI-generated content, intellectual property law, entertainment industry, authorship, legal reform



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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Licensing: 

 

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.

 

Disclaimer:

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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