Algorithmic Authorship In India: A Doctrinal Analysis Of Challenges In IPR Model In The Age Of Generative Ai- Analysing The Need For Hybrid Authorship Models And Legal Reforms

Artificial intelligence (AI) is fundamentally reshaping creative production by leveraging advanced machine learning paradigms and deep neural networks. This transformation raises intricate legal and ethical questions about intellectual property rights (IPR), particularly regarding the ownership of AI-generated content. In India, where copyright law is deeply rooted in the notion of human authorship, emerging AI technologies—powered by convolutional neural networks, algorithmic transparency, and explainable AI models—challenge traditional legal frameworks. This paper employs a qualitative research methodology and doctrinal legal analysis to scrutinize Indian copyright law alongside global precedents. By examining case studies such as Tech Mahindra v. AI Ownership Dispute and Thaler v. Perlmutter, and by applying technical terms like algorithmic bias and morphological analysis of creative output, we identify key policy gaps related to originality, liability, and authorship. Our findings reveal that current statutes, notably the Copyright Act, 1957, are inadequate in addressing the complexities introduced by machine-generated works. We propose a reformed legal framework that integrates hybrid authorship models and explicit AI copyright regulations, balancing technological innovation with traditional legal principles.