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.

翻译：生成式人工智能的进步引发了紧迫的版权挑战，尤其在音乐产业中尤为突出。本文聚焦于这些挑战的经济层面，强调经济影响构成版权领域的核心问题。黑盒生成式人工智能技术的复杂性不仅暗示了算法解决方案的必要性，更使其成为必然选择。然而，此类解决方案长期缺失，导致该领域面临监管困境。我们旨在通过提出适用于AI音乐生成平台收入分成的潜在版税模型，弥合现有方法的不足。研究方法涉及对Spotify和YouTube等平台现有版税模型的详细分析，并将其适配至AI生成音乐的特殊语境。我们解决的关键挑战在于将AI生成音乐归因于训练数据中具有影响力的受版权保护内容。为此，我们提出了采用数据归因技术的算法解决方案。实验结果验证了这些方法的有效性。本研究作为整合技术进步与经济法律维度的开创性尝试，为生成式AI领域因技术黑箱性质引发的难题提供了计算版权解决方案。