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Copyright Protection Frameworks For Outputs Created Entirely Or Partially By Artificial Intelligence, Especially Generative Models




Abhay Sinha, LLM, Alliance School of Law, Alliance University, Bangalore


ABSTRACT


Artificial intelligence, particularly generative models, is reshaping the creation of artistic, literary, and technological works, raising complex questions within copyright law. Traditional copyright frameworks are premised on human authorship, intentional creativity, and originality, which creates tension when applied to works generated autonomously or semi- autonomously by machines. This paper examines the evolving global debate regarding whether AI-generated outputs merit protection, and if so, who should be recognized as the rights holder the AI’s developer, the user providing inputs, or neither. By analyzing legal doctrines across different jurisdictions, including the Japan, the Indonesia, and emerging perspectives in Asia, this study highlights the uncertainty surrounding authorship standards, originality assessments, and ownership allocation in the era of intelligent systems. The paper further considers hybrid works co-created by humans and AI, where the threshold of human contribution becomes a critical determinant of eligibility for protection. Ultimately, the research argues for a nuanced rethinking of copyright frameworks that balances the incentive to innovate with the need to safeguard human creativity, ensuring that the law remains responsive to technological disruption without undermining its foundational principles.



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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