AI-GENERATED MUSIC & COPYRIGHT OWNERSHIP: HUMAN INTERVENTION, AUTHORSHIP, AND THE LIMITS OF PROTECTION
AUTHOR – SIDDHARTH JADHAV, STUDENT AT ILS LAW COLLEGE PUNE.
BEST CITATION – SIDDHARTH JADHAV, AI-GENERATED MUSIC & COPYRIGHT OWNERSHIP: HUMAN INTERVENTION, AUTHORSHIP, AND THE LIMITS OF PROTECTION, INDIAN JOURNAL OF LEGAL REVIEW (IJLR), 6 (10) OF 2026, PG. 193-202, APIS – 3920 – 0001 & ISSN – 2583-2344.
Abstract
The creation of music has been revolutionized by generative artificial intelligence (AI), raising pressing issues for copyright law related to authorship, ownership, and the extent of protection for sound recordings and musical compositions. The doctrinal conflicts and policy decisions that result from the intersection of machine-generated outputs with legal frameworks based on human creativity are critically examined in this study. In order to explain how levels of human participation impact authorship attribution under standard tests of originality and fixation, it first maps technical modalities: entirely autonomous generation, algorithmic recomposition, and human-assisted co-creation. Building on this theoretical underpinning, the paper examines real-world issues such as training data from copyrighted works, the possibility of model memorization and output replication, the implications of attribution and moral rights, and distributive issues pertaining to royalties and compensation for displaced creators. In order to protect expressive labor, the main argument supports a balanced, hybrid regulatory framework that maintains a human-centric authorship threshold while enacting specific legal and contractual mechanisms to regulate machine-human collaboration. These mechanisms include mandatory training-data licensing, transparency and provenance obligations, sui generis rights for AI service providers, and revenue-sharing schemes. In order to account for algorithmic opacity and cross-border data flows, the study also suggests procedural improvements for enforcement and dispute resolution. The study intends to provide policymakers and courts with practical avenues that balance incentives for AI innovation with sufficient protection for human creators and cultural heritage by fusing technical reality with doctrinal analysis. The paper’s methodology grounds legal theories in empirical reality by combining doctrinal analysis, a review of case law, and a technical evaluation of generative models. By outlining contractual provisions and regulatory adjustments that governments may implement to lessen harms while maintaining cultural vibrancy, it advances scholarship.
Keywords: AI-generated music; authorship; copyright law; training-data licensing; remuneration mechanisms.