AI Training Data Provenance Tracking for Copyright Compliance
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Solution Overview
Problem
The issue of copyright infringement in large language models (LLMs) due to the use of large volumes of unattributed or unlicensed data for training, which can lead to legal disputes and lack of compensation for content creators.
Innovation Solution
Implementing blockchain technology to track and record the provenance of copyrighted data used in AI training, ensuring proper compensation and compliance with copyright laws by maintaining an immutable record of data usage and transactions on a decentralized ledger.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If large volumes of data are used to train AI models, then the model's predictive capability and pattern recognition improve, but the risk of copyright infringement increases
Solution Approach 1:
The system performs preliminary actions by obtaining permissions and paying royalties for training data before the AI model is trained. The blockchain records these preliminary permission acquisitions, ensuring that copyright holders are compensated before their work is used in model training, thus preventing copyright infringement while enabling the use of large datasets for improving model capability.
Solution Approach 2:
The blockchain acts as an intermediary between AI model developers and copyright holders. It provides a transparent, immutable record of data usage permissions and royalty payments, mediating the relationship between parties and enabling trustworthy data sharing while protecting copyright interests.
2Reliability
If blockchain technology is implemented to track copyrighted data, then copyright risk management and creator compensation improve, but system complexity increases
Solution Approach 1:
The blockchain system performs multiple functions simultaneously: it tracks copyrighted data usage, records permission acquisitions, manages royalty payments, and provides audit trails. This multi-functionality consolidates what would otherwise require separate systems into a single unified platform, making the complexity manageable while achieving comprehensive copyright risk management.
Solution Approach 2:
The system enables self-service through automated smart contracts that handle royalty calculations and payments without requiring manual intervention. Once the blockchain infrastructure is established, it autonomously manages copyright tracking and compensation, reducing the operational complexity despite the initial system design complexity.
Data Source
AI summary
Various aspects of the subject technology relate to systems, methods, and machine-readable media for managing training data in artificial intelligence (AI) models. Various aspects may include receiving, at a blockchain, training data from data sources. Aspects may also include validating authenticity of the data sources. Aspects may also include receiving a request for training AI models using the training data and training the models based on an approval of the request. Aspects may also include submitting a first transaction to the blockchain for the training of the models using the training data. Aspects may also include generating a response to a query from a user using the models based on at least a subset of the training data. Aspects may also include submitting a second transaction to the blockchain for leveraging the subset of the training data to generate the response to the query.