Supply chain patterns

Bill of Materials Registry

. The bill of materials registry [12, 88] can be designed to keep a formal machine-readable record of the supply chain details of the components used in building an AI system, including component name, version, supplier, dependency relationship, author, and timestamp. In addition to supply chain details of the components, context documents (like model cards [81] for reporting AI models, and datasheets for the datasets [41] used to train AI models) can be integrated to the bill of materials registry.

Verifiable ethical credential

Verifiable ethical credentials can be used as evidence of ethical compliance for AI systems, components, models, developers, operators,96 users, organizations, and development processes [21, 72, 92]. Verifiable credentials are data that could be cryptographically verified and be presented with strong proofs [23]. Publicly accessible data infrastructure needs to be built to support the generation and verification of the ethical credentials on a neutral platform. Before using AI systems, users may verify the systems’ ethical credential to check if the systems are compliant with AI ethics principles or regulations [21]. However, the users may be required to provide the ethical credentials to use and operate the AI systems (e.g., to ensure the flight safety of drones).

Co-versioning registry

Co-versioning of the components or AI artifacts of AI systems provides end-to-end provenance guarantees across the entire lifecycle of AI systems. The co-versioning registry can track the co-evolution of components or AI artifacts [65, 70]. There are different levels of co-versioning: co-versioning of AI components and non-AI components, co-versioning of the artifacts within the AI components (i.e., co-versioning of data, model, code, and configurations, and co-versioning of local models and global models in federated learning). Co-versioning enables effective maintenance and evolution of AI components because the deployed model or code can be traced to the exact set of artifacts, parameters, and metadata that were used to develop the model and code

Federated learner

The federated learner trains an AI model across multiple edge devices or servers with local data samples. The federated learner [15, 18, 68–70, 112, 113, 117] preserves the data privacy by training models locally on the client devices and formulating a global model on a central server based on the local model updates (e.g., train the visual perception model locally in each vehicle). Decentralized learning is a variant of federated learning, which could use blockchain to remove the single point of failure and coordinate the learning process in a fully decentralized way [120].