解决生成式AI多阶段贡献者的价值分配难题
AME: A Multi-Type Contributor Attribution Framework in Generative AI Markets
- 提出三位一体的贡献归属框架，统一估值、确权与可信执行
- 实验显示分配结果更贴近人工判断，且执行成本低
- 适合关注AI数据市场公平分配的研究者与从业者
生成式AI通过训练数据、基础模型、微调行为和提示词等异构贡献者的多阶段协作创造价值，但数据价值的公平分配仍缺乏研究。本文将多阶段生成式AI价值分配定义为新问题，识别出三大核心挑战：异构数据贡献估值、数据权利映射和可信执行。提出AME（Attribution-Mapping-Execution）框架，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。实验表明，该框架在保持低成本可信执行的同时，其数据价值分配结果更符合人类参考判断。本工作为生成式AI数据市场的价值评估与收益分配提供了初步基础。
原文摘要 · Abstract (English)
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.
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