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

arXiv: https://arxiv.org/abs/2606.16075 · HTML: https://mlsanity.com/paper/2606.16075/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 42 references that are in the map.
- First posted on arXiv 15 Jun 2026.

## Builds on (most cited first, 16 of 16)

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- [Data Shapley: Equitable Valuation of Data for Machine Learning](https://mlsanity.com/paper/1904.02868/) (2019)
- [TRAK: Attributing Model Behavior at Scale](https://mlsanity.com/paper/2303.14186/) (2023)
- [Diffusion Model-Based Image Editing: A Survey](https://mlsanity.com/paper/2402.17525/) (2024)
- [Generative AI](https://mlsanity.com/paper/2309.07930/) (2023)
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- [Generative Artificial Intelligence: A Systematic Review and Applications](https://mlsanity.com/paper/2405.11029/) (2024)
- [Zero-Knowledge Proof-based Verifiable Decentralized Machine Learning in Communication Network: A Comprehensive Survey](https://mlsanity.com/paper/2310.14848/) (2023)
- [A Survey on Quality Metrics for Text-to-Image Generation](https://mlsanity.com/paper/2403.11821/) (2024)
- [An Economic Solution to Copyright Challenges of Generative AI](https://mlsanity.com/paper/2404.13964/) (2024)
- [A survey of Monte Carlo methods for noisy and costly densities with application to reinforcement learning and ABC](https://mlsanity.com/paper/2108.00490/) (2021)
- [Between- and Within-Cluster Spearman Rank Correlations](https://mlsanity.com/paper/2402.11341/) (2024)
- [Shapley Values-Powered Framework for Fair Reward Split in Content Produced by GenAI](https://mlsanity.com/paper/2403.09700/) (2024)
- [ZK-WAGON: Imperceptible Watermark for Image Generation Models using ZK-SNARKs](https://mlsanity.com/paper/2510.01967/) (2025)
- [A Comparative Analysis of zk-SNARKs and zk-STARKs: Theory and Practice](https://mlsanity.com/paper/2512.10020/) (2025)

## Abstract (arXiv)

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.