AI-Generated Content Attribution for Proportional Creator Compensation
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Solution Overview
Problem
Current systems lack the ability to determine the proportionality of content used by generative AI to generate derivative content, preventing accurate attribution and compensation to content creators.
Innovation Solution
A model-based attribution system that determines an attribution vector during the training of generative AI, identifying the influence of individual content creators based on their contributions, and provides compensation accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If generative AI is trained using content items from multiple content creators, then the AI can generate diverse derivative content, but there is no mechanism to determine the proportionality of content used and provide accurate attribution to creators
Solution Approach 1:
The system performs preliminary actions by determining the attribution vector during the training phase, before the AI generates derivative content. This allows the system to capture and store the proportionality information about how each content creator's training data influences the AI, preserving attribution information that would otherwise be lost during the generative process
Solution Approach 2:
The system implements feedback by using the determined attribution vector to provide attribution information back to content creators whose training data was used to generate derivative content. This creates a closed loop where the AI's generation process feeds back attribution information to the original creators, enabling proper recognition and compensation
2Measurement precision
If the system tracks the influence of each content creator during AI training, then accurate attribution can be provided, but the complexity of the system increases
Solution Approach 1:
The system changes parameters by representing the complex influence of multiple content creators as an attribution vector with specific parameters (weights indicating proportionality). This mathematical representation simplifies the tracking and measurement of creator influence, enabling precise attribution without requiring overly complex tracking mechanisms for each individual training data element
Data Source
AI summary
In some aspects, a server, after determining that a training of an artificial intelligence has been completed to create a trained artificial intelligence, determines an attribution vector created during the training of the artificial intelligence, determines that the trained artificial intelligence has received an input, and generates, using the trained artificial intelligence, an output based on the input. The server determines an attribution determination for individual content creators of multiple content creators based on the attribution vector and based on identifying one or more of the creators that contributed at least a threshold amount during the training. A particular creator having a contribution less than the threshold amount does not receive a creator attribution. The server initiates providing compensation to one or more of the multiple content creators based at least in part on the attribution determination.