Generative AI enables value creation through multi-stage collaboration among heterogeneous contributors, including training data, base models, fine-tuning behaviors, and prompts. However, how to fairly allocate the data value remains largely unexplored. This paper formulates multi-stage generative AI value allocation as a new research problem and identifies three core challenges: heterogeneous data contribution valuation, data rights mapping, and trustworthy execution. We propose AME (Attribution-Mapping-Execution) framework, a unified framework that integrates data contribution valuation, data rights mapping, and trustworthy execution into a single workflow. Experimental results demonstrate that AME framework achieves data value allocation outcomes more consistent with human reference judgments while maintaining low-cost trustworthy execution. Our work provides an initial foundation for value assessment and revenue allocation in generative AI data markets.

翻译：生成式AI通过异构贡献者之间的多阶段协作实现价值创造，这些贡献者包括训练数据、基础模型、微调行为以及提示词。然而，如何公平分配数据价值仍是一个鲜有探索的问题。本文将多阶段生成式AI价值分配定义为一个新的研究问题，并识别出三大核心挑战：异构数据贡献估值、数据权利映射与可信执行。我们提出AME（归因-映射-执行）框架，这是一个将数据贡献估值、数据权利映射与可信执行整合为统一工作流的整合框架。实验结果表明，AME框架在实现数据价值分配结果更符合人类参考判断的同时，保持了低成本的可信执行。本研究为生成式AI数据市场中的价值评估与收益分配奠定了初步基础。