In the world of Artificial Intelligence (AI), ensuring trust in the information supply chain is paramount. Verifiable credentials offer a solution by bolstering transparency, accountability, and security across various stages of the AI lifecycle.

Firstly, let us briefly discuss the topic; Understanding the Trust Triangle in Verifiable Credentials

Verifiable Credentials work based on the Trust Triangle, simplifying digital interactions with Trusted Data. This concept decentralizes trust, meaning the verifier doesn't need direct contact with the issuer to confirm attestations. Let's break down the Trust Triangle:

1. The Holder: This actor possesses the credentials and might need to prove something. They store their credentials in a wallet.

2. The Issuer: This actor issues the credential initially. They register a Decentralized Identifier (DID) on the network, acting as a signature to validate the credential. The issuer then signs the credential with their DID and sends it to the holder's wallet.

3. The Verifier: This actor checks the credibility of the credential. They verify if the issuer is trustworthy and valid by cross-referencing the DID attached to the credential with the network.

Verifiable Credentials essentially form packets of Trusted Data, controlled entirely by the holder. When asked to prove an attestation, the holder sends the Verifiable Credential to the verifier. The verifier then confirms the issuer's trustworthiness through cross-referencing. This process ensures secure and efficient digital interactions.

Understanding How Verifiable Credentials Enhance Trust in the AI Information Supply Chain

Model Training: Different AI models are trained on diverse datasets, each with its acteristics. Verifiable credentials allow for labeling and documentation of bias detection and mitigation efforts during training. This empowers users to make informed choices about the suitability of AI models for their specific needs.

Model Deployment: Verifiable credentials extend to the deployment phase, confirming the legitimacy and fairness of AI models. Credentials associated with model architecture, hyperparameters, and training processes enable users to verify compliance with ethical standards and regulations.

Data Quality and Provenance: High-quality data is essential for training robust AI models. Verifiable credentials can be attached to data at its source, creating a secure and tamper-proof record of its origin and characteristics. This ensures stakeholders can verify the authenticity and reliability of data, crucial for training AI models ethically and legally.

Hardware Integrity: Verifiable credentials play a pivotal role in ensuring the integrity of hardware components used in AI infrastructure. By attaching credentials to hardware such as GPUs and TPUs, stakeholders can verify their authenticity and compliance with specifications, mitigating risks associated with compromised or faulty hardware.

Verifiable credentials play a vital role in enhancing trust and accountability throughout the AI information supply chain, ensuring ethical, transparent, and secure AI-driven interactions....