AI-Generated Works And Copyright Ownership: The Indian Perspective
- IJLLR Journal
- 3 days ago
- 2 min read
Pranay Sundriyal, BBA LLB, Gitarattan International Business School, Guru Gobind Singh Indraprastha University (GGSIPU)
ABSTRACT
In an era of rapidly advancing generative artificial intelligence (AI) technologies, the question of copyright ownership for AI-generated creative works has come to the fore of legal discourse worldwide. Indian copyright law – rooted in a statute drafted in 1957 – makes no explicit provision for content autonomously produced by machines, leaving uncertainty as to who (if anyone) can claim authorship or ownership of such works. Recent government statements in India have suggested that existing intellectual property laws are sufficient: for example, the Commerce Ministry has declared the current regime “well-equipped to protect AI-generated works” and indicated no plan to create a separate category of rights. This study provides a comprehensive examination of AI-generated works and copyright ownership from the Indian perspective. It begins with a review of relevant scholarship and policy commentary and sets out the doctrinal foundations of authorship and originality – noting that copyright traditionally hinges on a human author’s “intellectual creation” or individual imprint. The core analysis then surveys India’s legal framework, including the key statutory definitions of “author” and “computer-generated work” and the judicial treatment of originality and authorship. It highlights that although Section 2(d)(vi) defines the author of a computer-generated work as “the person who causes the work to be created”, Indian courts have interpreted “person” to mean a natural human being, generating tension with AI outputs. A comparative section examines approaches in the UK, US, and EU – for example, the UK’s CDPA s.9(3) rule for computer-generated works, and the US Copyright Office’s strict human-authorship requirement – to show that jurisdictions vary from denying copyright to purely AI works to allowing conditional human-centric attribution. Finally, the paper discusses policy considerations (balancing innovation incentives, fairness to human creators, and data access for AI training) and offers reform proposals. For instance, legislative amendments could clarify that only works with meaningful human creative input qualify for protection or expressly attribute authorship to the human instigator of the AI process, while ensuring appropriate exceptions for AI training. These proposals aim to reconcile encouragement of innovation with safeguarding the rights of human authors.
