AI Content Attribution via Embedding Distance
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Solution Overview
Problem
Current generative artificial intelligence systems lack the capability to determine the proportion of content items used to generate derivative content, making it impossible to provide accurate attribution and compensation to the original content creators.
Innovation Solution
A server system that parses inputs to generative AI, embeds them into a shared language-image space, compares creator-based embeddings to determine similarity, and creates a creator attribution vector to provide compensation to content creators based on their contribution to the generated derivative content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If generative AI creates derivative content using training data, then content generation capability is improved, but inability to track content usage proportion prevents accurate attribution
Solution Approach 1:
The system performs preliminary actions by embedding content descriptors and creator identifiers into the generative AI model before content creation. These embeddings are injected into the model's latent space, allowing the system to track which creators' styles are being used and to what extent, without interfering with the AI's creative capabilities.
Solution Approach 2:
The system implements feedback by continuously monitoring the generative AI's output to determine the proportion of each creator's content style present in the derivative work. This feedback loop enables real-time attribution calculation by comparing the generated content against the embedded creator profiles, ensuring accurate compensation distribution.
2Measurement precision
If the system tracks content usage for attribution, then attribution accuracy is improved, but system complexity increases
Solution Approach 1:
The system introduces an intermediary attribution determination module that acts as a mediator between the generative AI and the compensation distribution system. This module contains the logic for calculating content usage proportions and determining creator attributions, separating the complexity from the core generative AI model while maintaining attribution accuracy.
Solution Approach 2:
The system changes parameters by representing creator content styles as embeddings in a shared language-image space. By transforming creator profiles and generated content into comparable vector representations, the system enables precise attribution measurements through mathematical operations on these parameters without requiring complex analysis of the actual content.
3Reliability
If creator attributions are determined based on content similarity, then compensation fairness is improved, but computational resources increase
Solution Approach 1:
The system creates simplified copies of creator content in the form of embeddings - compressed vector representations that capture the essential characteristics of each creator's style. These embedding copies allow for rapid similarity comparisons without requiring the full computational resources needed to analyze the actual high-resolution content, enabling fair attribution with reduced energy consumption.
Data Source
AI summary
In some aspects, a server determines an input provided to a generative artificial intelligence, parses the input to determine: a type of content to generate, a content description, and creator identifiers. The server embeds the input into a shared language-image space to create an input embedding. The server determines a creator description comprising a creator-based embedding associated with individual creators. The server performs a comparison of the input embedding to the creator-based embedding associated with individual creators to determine a distance measurement of an embedding of individual creators in the input embedding. The server determines creator attributions based on the distance measurement and creates a creator attribution vector to provide compensation to the creators.