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。黑箱式生成式人工智能技术的复杂性不仅提示、更要求采用算法解决方案。然而，此类方案长期缺失，导致相关监管面临困境。我们旨在通过提出面向AI音乐生成平台的潜在版税模型来弥补当前方法的不足。研究方法包括深入分析Spotify和YouTube等平台现有的版税模型，并使其适应AI生成音乐的特殊情境。我们面临的一项重大挑战是如何将AI生成音乐归因于训练数据中具有影响力的版权内容。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案的有效性。本研究是生成式人工智能领域将技术进步与经济及法律考量相结合的开创性尝试，为应对AI技术不透明性带来的挑战提供了计算版权解决方案。