AME: A Multi-Type Contributor Attribution Framework in Generative AI Markets
It addresses the underexplored problem of fair data value allocation in multi-stage generative AI collaborations, providing an initial foundation for data markets.
The paper introduces the AME framework for fair value allocation among heterogeneous contributors in generative AI markets, achieving allocation outcomes more consistent with human judgments while maintaining low-cost trustworthy execution.
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.