Generative AI Attribution Using Meta-Feature Contribution Scoring
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Solution Overview
Problem
Existing generative AI models lack a systematic method to attribute copyright-protected assets owners for their contributions to the training process and generated products, leading to potential misuse and unfair compensation.
Innovation Solution
A method and system for determining contribution distribution by analyzing meta features of assets used in training, calculating contribution scores based on quality, similarity, and correlation with generated products, enabling fair attribution and compensation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If generative AI models are trained using large datasets of copyright-protected assets, then the quality and capability of the model improves, but the ability to attribute and compensate asset owners fairly deteriorates
Solution Approach 1:
The system performs preliminary actions by embedding attribution metadata and unique identifiers into assets before they are used for training the generative AI model. This ensures that ownership information is preserved throughout the training process and can be traced back to original creators when generating compensation distributions.
Solution Approach 2:
The system implements feedback mechanisms by continuously tracking and analyzing the contribution of each asset to the generated outputs. The attribution system uses this feedback to calculate fair compensation distributions, ensuring that asset owners receive appropriate recognition and payment based on their actual contribution to the model's outputs.
2Reliability
If the system tracks and attributes every asset contribution to the model, then fair compensation can be achieved, but the system complexity increases
Solution Approach 1:
The system creates simplified copies or representations of asset attribution data in the form of metadata tags and contribution scores. These copied representations allow the system to track and attribute asset contributions without requiring complex storage and processing of complete asset datasets, thereby reducing system complexity while maintaining attribution reliability.
Solution Approach 2:
The attribution system is designed with universal components that can handle multiple types of assets (images, text, audio, video) and multiple attribution scenarios through a single unified framework. This multi-functionality reduces overall system complexity by avoiding the need for separate tracking systems for different asset types.
3Measurement precision
If detailed metadata is extracted from each asset, then accurate contribution measurement is possible, but the processing time and computational resources increase
Solution Approach 1:
The system extracts only the essential and most relevant metadata features from assets that are necessary for contribution measurement, such as unique identifiers, basic descriptive tags, and key attribute information. By selectively extracting only the necessary metadata rather than processing complete asset data, the system achieves accurate contribution measurement while significantly reducing processing time and computational resource requirements.
Data Source
AI summary
The present disclosure provides a solution for determining the extent of contribution of each copyright-protected assets owner to the training of a generative AI model. Furthermore, the present disclosure provides a solution for identifying the specific assets that contributed the most for the generation of a specific generated product being generated by the generative AI model. This is performed based on correlation of metrics related to meta features composing the assets and the generated product. By determining the contribution distribution of the owners to the generative AI model and a specific generated product, the owners can be attributed with recognition for their contribution. The recognition can be manifested in many ways, for example in attribution of copyright or an allocation of an income that is received for the use of the generative AI model according to a certain financial model. Therefore, by the solution of the present disclosure, the use of assets protected by copyrights in the training of generative AI models can be standardized.