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Research Paper Volume 9 Issue 3 150 - 160 May 18, 2026

Authorless and Unprotected: The Case for a Sui Generis Copyright Framework for Ai-Generated Journalistic Content in India

Lead author · Corresponding
Dr. Wasim Ahmad
Assistant Professor at Integral University, Lucknow, Uttar Pradesh, India
Co-author
Mr. Ashraf Azmi
Assistant Professor at Integral University, Lucknow, Uttar Pradesh, India
Abstract

Artificial intelligence is no longer merely a tool that assists journalists - it is, in a growing number of newsrooms, the journalist itself. Automated systems now produce financial summaries, election results, sports reports and weather bulletins with little to no human intervention. Yet the Copyright Act, 1957, which governs intellectual property protection in India, offers no coherent answer to the question of who owns content generated by a machine. Its authorship framework, built on the foundational assumption of human creativity, collapses when confronted with outputs that have no identifiable human author in the traditional sense. This article argues that the existing statutory framework is structurally incapable of accommodating AI-generated journalistic content, and that neither judicial interpretation nor piecemeal amendment of section 2(d) can adequately resolve the problem. Drawing on comparative analysis of the United Kingdom’s computer-generated works doctrine under the Copyright, Designs and Patents Act 1988 and the evolving position of the United States Copyright Office, this article makes the case for a sui generis protection regime - a purpose-built legal framework that vests ownership in the deploying news organisation, mandates disclosure of AI origin, limits the term of protection, and defaults to the public domain upon expiry.

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Research Paper
Information
International Journal of Law Management and Humanities, Volume 9, Issue 3, Page 150 - 160
Creative Commons
CC BY-NC 4.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution–NonCommercial 4.0 International (CC BY-NC 4.0) (https://creativecommons.org/licenses/by-nc/4.0/), which permits remixing, adapting, and building upon the work for non-commercial use, provided the original work is properly cited.
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Copyright © IJLMH 2026
Disclaimer
The views and opinions expressed in this manuscript are those of the author(s) alone and do not reflect the views, policies, or position of the Journal.

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