The advancement of generative AI has given rise to pressing copyright
challenges, particularly in 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. The complexity of the black-box
generative AI technologies not only suggests but necessitates algorithmic
solutions. However, such solutions have been largely missing, leading to
regulatory challenges in this landscape. We aim to bridge the gap in current
approaches by proposing potential royalty models for revenue sharing on AI
music generation platforms. Our methodology involves a detailed analysis of
existing royalty models in platforms like Spotify and YouTube, and adapting
these to the unique context of AI-generated music. A significant challenge we
address 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. Our experimental results verify the
effectiveness of these solutions. This research represents a pioneering effort
in integrating technical advancements with economic and legal considerations in
the field of generative AI, offering a computational copyright solution for the
challenges posed by the opaque nature of AI technologies.

该研究旨在针对生成式人工智能在音乐产业中所引发的版权挑战，重点关注经济方面的问题，并提出了用于 AI 音乐生成平台的潜在版税模式。研究方法包括对 Spotify 和 YouTube 等现有版税模式进行详细分析，并将其适应于 AI 生成音乐的特殊背景。研究通过数据归属技术提出了解决 AI 生成音乐与训练数据中有影响力的受版权保护内容之间归属问题的算法解决方案，并通过实验结果验证了这些解决方案的有效性。该研究在生成式人工智能领域中首次整合了技术进步与经济和法律考虑，为 AI 技术的不透明性带来的挑战提供了计算版权解决方案。