Attribution-by-design: Ensuring Inference-Time Provenance in Generative Music Systems

2025-10-09

AI Summary

AI-generated music dilutes royalty pools and obscures attribution, while existing licensing mechanisms lack scalability, and data provenance remains coarse-grained and impractical. This paper proposes a generative-music-oriented attribution assurance framework that innovatively decouples attribution into training-time and inference-time responsibilities: at training, it enables dataset-level copyright provenance; at inference, it supports fine-grained, verifiable attribution and royalty allocation conditioned on specific musical outputs. The framework integrates generative models, data provenance techniques, conditional generation control, and decentralized rights registration—embedding fairness directly into system architecture. Experiments demonstrate real-time transparent attribution, dynamic royalty settlement, and granular artist control over AI-mediated usage. The approach establishes a technically rigorous and ethically viable compensation paradigm for the AI music ecosystem.

Technology Category

• Humans and AI: Game Design — Procedural Content Generation & Storytelling
• Natural Language Processing: Generation
• Philosophy and Ethics of AI: Accountability, Interpretability & Explainability

Application Category

• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Security and Privacy: Data transparency and provenance
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

The rise of AI-generated music is diluting royalty pools and revealing structural flaws in existing remuneration frameworks, challenging the well-established artist compensation systems in the music industry. Existing compensation solutions, such as piecemeal licensing agreements, lack scalability and technical rigour, while current data attribution mechanisms provide only uncertain estimates and are rarely implemented in practice. This paper introduces a framework for a generative music infrastructure centred on direct attribution, transparent royalty distribution, and granular control for artists and rights' holders. We distinguish ontologically between the training set and the inference set, which allows us to propose two complementary forms of attribution: training-time attribution and inference-time attribution. We here favour inference-time attribution, as it enables direct, verifiable compensation whenever an artist's catalogue is used to condition a generated output. Besides, users benefit from the ability to condition generations on specific songs and receive transparent information about attribution and permitted usage. Our approach offers an ethical and practical solution to the pressing need for robust compensation mechanisms in the era of AI-generated music, ensuring that provenance and fairness are embedded at the core of generative systems.

Problem

• Addressing structural flaws in artist compensation for AI-generated music
• Providing verifiable attribution mechanisms for generative music systems
• Ensuring transparent royalty distribution and provenance in AI music

Innovation

• Framework enabling direct verifiable inference-time attribution
• Distinguishing training and inference sets for dual attribution
• Providing transparent royalty distribution and granular artist control