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

2026-06-14

AI Summary

This study addresses the challenge of fairly allocating value among heterogeneous contributors—such as training data, base models, fine-tuning procedures, and prompts—in multi-stage generative AI collaboration. It presents AME, the first unified framework that formalizes this problem and integrates contribution attribution modeling,权益 mapping mechanisms, and lightweight trusted execution to enable end-to-end value assessment and distribution. Experimental results demonstrate that AME achieves allocation outcomes more aligned with human judgment while maintaining low-overhead trusted execution, thereby offering both theoretical grounding and practical support for emerging generative AI data 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.

Problem

• value allocation
• generative AI
• data contribution
• heterogeneous contributors
• data rights

Innovation

• multi-stage value allocation
• heterogeneous contribution
• data rights mapping
• trustworthy execution
• generative AI markets