The Responsible AI Ledger Alliance utilizes the following open standard for providing trustworthy provenance for digital content. This standard, originally authored as Arweave Network Standard 112, is a framework and definition for the “Data Format” of provenance proofs.  Each proof of provenance lists a unique hash of the content's data, as well as the prompt that was used to generate the AI image. Other fields such as “Uploaded-For” and “Model” help to identify the content and make it searchable.

The RAIL protocol is built on top of Arweave, a decentralized data storage network that enables reliable, permanent storage of data on the internet. Unlike traditional blockchains, Arweave utilizes a structure known as the blockweave, which leverages a proof-of-access consensus mechanism to maintain the network's data integrity. Arweave‘s design features a 'storage endowment' and replaces the energy-intensive 'work' of blockchain networks with the useful validation of the network's dataset, ensuring that once data is stored on the network, it remains accessible indefinitely.

By recording RAIL's provenance records on the Arweave network, users can create an immutable and verifiable chain of custody for their data, supporting its credibility and trustworthiness in a wide variety of applications. Our specification focuses on the design and implementation of such a data protocol for AI-generated data, taking advantage of the characteristics of the Arweave network to provide a permissionless and immutable ledger of content provenance, without centralized controllers.

In addition, data storage on Arweave is scalable, meaning that RAIL provenance records can be adopted by large scale creators of AI content well into the future. Through the use of transaction bundles facilitated by RAIL members like Bundlr, content generators can create dozens of thousands of new provenance records every second. You can learn more about scalable bundling here.