What's a Credit Worth? A Market Framework for Attribution-Aware Compensation in Generative Music
arXiv:2607.00641v1 Announce Type: cross Abstract: Advances in generative AI are rapidly increasing the quality and commercial value of generated music, and this progress depends on large catalogs of creators' recordings. This raises a central question for platform design: how should creators be compensated when their work is used to train generative AI models that in turn produce commercial outputs? We develop a framework for fairly compensating creators in generative-music markets, where each creator's payment depends on a data-attribution score estimating their contribution to model outputs.
The rapid advancement in generative AI quality for music production, coupled with increasing commercialization, necessitates a framework for creator compensation based on data attribution.
A strategic reader should care because this establishes a foundational economic and legal precedent for intellectual property in generative AI, potentially impacting all creative industries and IP-heavy sectors.
The proposed market framework shifts the compensation model from traditional licensing or lack thereof, to one directly tied to a creator's estimated contribution to AI model outputs.
- · Original content creators
- · Attribution scoring platforms
- · Transparency-focused AI music platforms
- · Legal and IP services
- · AI models trained on uncompensated data
- · Platforms avoiding creator compensation
- · Piracy-adjacent AI applications
- · Generic music libraries
Creators receive direct financial incentives for their contributions to generative AI training datasets.
New business models emerge around data attribution and compensation, transforming the economics of creative industries.
The development of robust attribution mechanisms could set a precedent for ethical AI development and data usage across various domains, not just music.
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Read at arXiv cs.LG