arXiv:2606.16075v1 Announce Type: new 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
The rapid development and commercialization of generative AI necessitate immediate solutions for fair value allocation among diverse contributors, as legal and ethical frameworks lag behind technological advancements.
This framework addresses a fundamental challenge in the burgeoning generative AI market: how to equitably distribute value, which is critical for fostering innovation and preventing disputes that could hinder adoption.
The explicit formulation of multi-stage generative AI value allocation as a research problem, combined with a proposed framework, moves towards standardizing how contributions are recognized and compensated.
- · AI data contributors
- · Base model developers
- · Fine-tuning specialists
- · AI platform providers with attribution systems
- · Companies exploiting uncompensated AI contributions
- · Opaque AI development models
- · Litigators addressing complex IP ownership without clear frameworks
Clearer attribution will incentivize more diverse and higher-quality contributions to generative AI projects.
Standardized attribution frameworks could lead to new business models around data licensing and fractional ownership in AI assets.
The concept of 'data rights mapping' could evolve into broader digital rights management for AI-generated content and underlying intellectual property.
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Read at arXiv cs.LG