  The advancement of generative AI has given rise to pressing copyright
challenges, especially within the music industry. This paper focuses on the
economic aspects of these challenges, emphasizing that the economic impact
constitutes a central issue in the copyright arena. Furthermore, the complexity
of the black-box generative AI technologies not only suggests but necessitates
algorithmic solutions. Yet, such solutions have been largely missing,
exacerbating regulatory hurdles in this landscape. We seek to address this gap
by proposing viable royalty models for revenue sharing on AI music generation
platforms. We start by examining existing royalty models utilized by platforms
like Spotify and YouTube, and then discuss how to adapt them to the unique
context of AI-generated music. A significant challenge emerging from this
adaptation is the attribution of AI-generated music to influential copyrighted
content in the training data. To this end, we present algorithmic solutions
employing data attribution techniques. We also conduct a range of experiments
to verify the effectiveness and robustness of these solutions. This research is
one of the early attempts to integrate technical advancements with economic and
legal considerations in the field of music generative AI, offering a
computational copyright solution for the challenges posed by the opaque nature
of AI technologies.
