What is it about?
DECORAIT is a decentralized registry through which content creators may assert their right to opt in or out of AI training and receive rewards for their contributions. Generative AI (GenAI) enables images to be synthesized using AI models trained on vast amounts of data scraped from public sources. Model and content creators who may wish to share their work openly without sanctioning its use for training are thus presented with a data governance challenge. Further, establishing the provenance of GenAI training data is important to creatives to ensure fair recognition and reward for their such use. We report a prototype of DECORAIT, which explores hierarchical clustering and a combination of on/off-chain storage to create a scalable decentralized registry to trace the provenance of GenAI training data to determine training consent and reward creatives who contribute that data. DECORAIT combines distributed ledger technology (DLT) with visual fingerprinting, leveraging the emerging C2PA standard to create a secure, open registry through which creatives may express consent and data ownership for GenAI.
Featured Image
Photo by John Schnobrich on Unsplash
Why is it important?
DECORAIT enables creators to express consent, or otherwise, for their images to be used in AI training, as well as enabling them to receive reward when such use occurs. We envision a future creative economy for content delivery services, such as stock photography platforms, which enables the commercialization of creative content and the contribution of it to ethically and consensually built datasets for GenAI training. These platforms are responsible for enabling their users, contributors, and collaborators to express consent over their data being used in GenAI training in a secure, persistent, and interoperable way. Such capability is grounded in strong provenance signals for GenAI training data, that enable creatives to register ownership and means for payment for GenAI use as well as their consent for that use.
Perspectives
The questions posed by this work are likely to remain open for some time, however, ensuring the consensual use of digital assets and fair reward to contributors within the GenAI training pipeline is both a timely and urgent matter. We believe the proposed DECORAIT system is a promising first step towards a decentralized, end-to-end solution to the problem. It is a pleasure to continue working on this highly relevant challenge at this time and an inspiration to see others join in helping change policy for the better even outside our field.
Kar Balan
University of Surrey
Read the Original
This page is a summary of: DECORAIT - DECentralized Opt-in/out Registry for AI Training, November 2023, ACM (Association for Computing Machinery),
DOI: 10.1145/3626495.3626506.
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