Beyond Human Authorship: Generative AI, Intellectual Property Rights, And The Future Of Digital Content Ownership
- IJLLR Journal
- 3 days ago
- 2 min read
Asst. Prof. K. Panneerselvam, Government Law College, Vellore
ABSTRACT
The emergence of generative artificial intelligence (AI) systems capable of producing text, images, music, and code has destabilized the foundational assumptions of copyright law, which has historically presumed a human author as the source of original expression. This paper critically examines the authorship and ownership problem posed by generative AI, tracing how legal doctrines built around human creativity, originality, and intent struggle to accommodate content produced substantially or entirely by machine learning systems. Drawing on comparative analysis of United States, United Kingdom, European Union, and Indian copyright frameworks, the paper reviews landmark disputes, including Thaler v. Perlmutter, the Zarya of the Dawn registration decision, and pending infringement litigation against AI developers such as Getty Images v. Stability AI and the Authors Guild and New York Times actions against OpenAI, to illustrate the doctrinal uncertainty surrounding both the outputs of generative systems and the legality of the training data used to build them. The paper further explores the mechanics of generative models to clarify where human creative agency plausibly persists in prompt engineering, curation, and post-processing, and where it does not. Building on this analysis, the paper proposes a framework of ownership models for the generative AI era, encompassing human-in-the- loop authorship standards, a possible sui generis right for AI-assisted works, text-and-data-mining licensing and compensation mechanisms for training data, and contractual allocation of rights at the platform level. The paper argues that copyright law's traditional binary of human author versus unprotected machine output is no longer adequate, and that a graduated, contribution-sensitive approach better serves the interests of creators, AI developers, and the public domain alike. The paper concludes with policy recommendations and directions for future research.
Keywords: generative artificial intelligence, copyright law, authorship, intellectual property, digital content ownership, training data, fair use.
