Generative AI, Training Data And Indian Copyright Law: Between Significant Human Input And Infringement
Pratiksha Gehlot, B.A. LL.B., University of Petroleum and Energy Studies (UPES), Dehradun, Uttarakhand, India
ABSTRACT
This review examines the intersection of generative artificial intelligence (AI), training data practices, and Indian copyright law, with particular focus on the doctrine of significant human input and its implications for copyright infringement liability. The paper synthesises contemporary scholarship, Indian judicial precedent, statutory provisions, and international instruments to analyse unresolved tensions within India's copyright framework as applied to AI-generated content. The objective is to identify how existing legal structures particularly fair dealing provisions under Section 52 of the Copyright Act, 1957; originality and authorship doctrine; and the emerging concept of significant human input interface with technological affordances of generative AI systems trained on copyrighted materials. Through critical examination of the Copyright (Amendment) Bill, 2023, domestic case law, and comparative analysis with jurisdictions including the United States and the European Union, this review identifies doctrinal lacunae, unresolved definitional questions, and areas requiring legislative clarification. The paper further integrates India's distinctive constitutional framework, specifically examining how fundamental rights under Articles 19(1)(a), 19(1)(g), 21, and 14 of the Constitution constrain and inform copyright policy regarding AI- generated content and training data governance. This constitutional analysis represents a unique contribution, distinguishing this review from Anglo- American copyright scholarship that predominates in existing literature. Key findings reveal that Indian copyright law operates without explicit statutory recognition of generative AI; significant human input remains judicially undefined; the fair dealing exception provides limited guidance for AI training scenarios; authorship frameworks fail to accommodate non-human creative processes; and constitutional dimensions require urgent judicial and legislative consideration. The review concludes that India requires urgently coordinated legal reform addressing AI training data governance, redefinition of fair dealing to accommodate technological innovation, clarification of originality standards, explicit authorship provisions for AI- generated works, constitutional alignment of copyright restrictions with fundamental rights, and potentially new statutory rights for training data proprietors. This paper contributes to emerging jurisprudence on artificial intelligence and intellectual property by providing a comprehensive Indian legal analysis grounded in statutory text, constitutional principles, and comparative frameworks, while offering evidence-based policy recommendations for India's legal development in this critical domain
Keywords: Generative AI; Training Data; Copyright Infringement; Fair Dealing; Significant Human Input; Authorship; Originality; Indian Copyright Law; Digital Technology; Machine Learning.
