How Do Training Data Copyright Risks Affect the Attribution of Copyright in AI-Generated Music? - A Theoretical Explanation Based on the CRR Model

This paper explores the derivative implications of the absence of responsibility in AI music training datasets for the copyright ownership of AI-generated music.The research context stems from the disruptive impact of generative AI technology on the music industry in 2025, where the massive training data it relies on carries significant copyright risks.During the construction of AI music generation model training datasets, the unauthorized use of copyrighted content means that accompanying licensing agreements cannot fully mitigate infringement risks.The study finds that this lack of accountability at the upstream of the "data supply chain" is amplified by technological "black boxes" and complex data processing workflows, posing a fundamental challenge to determining copyright ownership of downstream AI-generated musical works.To systematically analyze this issue, this study employs grounded theory methodology and conducts a three-tier coding analysis of multisource policy and industry texts.This ultimately constructs the "Conflict, Response, and Reconciliation" (CRR) theoretical model for AI data governance.The model reveals a dynamic evolutionary path: from "technological and legal conflicts" to "systemic risks," then through "multilevel governance responses" toward "economic reconciliation".The study identifies "training data traceability" and "training data lifecycle management" as key mediating variables linking governance and reconciliation.Building upon this foundation, a systematic governance strategy centered on a trusted data foundation has been proposed.This strategy encompasses four dimensions: a technical compliance system (resolving conflicts), risk buffer mechanisms (managing risks), a rule-of-law regulatory environment (implementing governance), and market allocation models (achieving reconciliation).It provides both theoretical underpinnings and a practical roadmap for resolving AI music copyright challenges and fostering the industry's healthy, sustainable development.