Uncertainty of Copyright Ownership in AI-Generated Content: Doctrinal and Legal Challenges

Author : Satya prakash

Abstract : has exposed profound uncertainties in copyright ownership under existing doctrinal frameworks. Traditional copyright law predicates ownership on human authorship; however, AI-generated outputs challenge this cornerstone by blurring the lines between human input, machine processing, and resultant works. This paper examines key doctrinal hurdles, including the authorship requirement in statutes such as the U.S. Copyright Act (17 U.S.C. § 102) and India’s Copyright Act, 1957 (Section 2(d)), alongside legal challenges arising from the non-human nature of AI agents, the attribution of substantial similarity, and the enforceability of derivative claims. Through a comparative analysis of landmark cases such as Thaler v. Perlmutter (2023) and emerging EU regulatory directives, the study argues that rigid human-centric doctrines risk both stifling innovation and exposing creators to heightened infringement liabilities. It ultimately proposes hybrid ownership models integrating contributory authorship thresholds and mandatory metadata disclosure to reconcile AI’s transformative potential with robust intellectual property protection.

Keywords : Copyright Ownership Challenges in AI-Generated Works: A Comparative Analysis

Conference Name : International Conference on Copyright Law and Creative Industry Management (ICCL-CIM-26)

Conference Place : Goa, India

Conference Date : 4th Jan 2026