AME: A Multi-Type Contributor Attribution Framework in Generative AI Markets

Abstract: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.

Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV)

arXiv:2606.16075 [cs.LG]

(or arXiv:2606.16075v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2606.16075 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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