Abstract Adopting shared data resources requires scientists to place trust in the originators of the data. When shared data is later used in the development of artificial intelligence (AI) systems or machine learning (ML) models, the trust lineage extends to the users of the system, typically practitioners in fields such as healthcare and finance. Practitioners rely on AI developers to have used relevant, trustworthy data, but may have limited insight and recourse. This article introduces a software architecture and implementation of a system based on design patterns from the field of self‐sovereign identity. Scientists can issue signed credentials attesting to qualities of their data resources. Data contributions to ML models are recorded in a bill of materials (BOM), which is stored with the model as a verifiable credential. The BOM provides a traceable record of the supply chain for an AI system, which facilitates on‐going scrutiny of the qualities of the contributing components. The verified BOM, and its linkage to certified data qualities, is used in the AI scrutineer, a web‐based tool designed to offer practitioners insight into ML model constituents and highlight any problems with adopted datasets, should they be found to have biased data or be otherwise discredited.

The quantity and capabilities of smart devices and sensors deployed as part of the Internet of Things (IoT) and accessible via remote microservices is set to rise dramatically as the provision of interactive data streaming increases. This introduces opportunities to rapidly construct new applications by interconnecting these microservices in different workflow configurations. The challenge is to discover the required microservices, including those from trusted partners and the wider community, whilst being able to operate robustly under diverse networking conditions. This paper outlines a workflow approach that provides decentralized discovery and orchestration of verifiably trustable services in support of multi-party operations. The approach is based on adoption of patterns from self-sovereign identity research, notably Verifiable Credentials, to share information amongst peers based on attestations of service descriptions and prior service usage in a privacy preserving and secure manner. This provides a dynamic, trust-based framework for ratifying and evaluating the qualities of different services. Collating these new service descriptions and integrating with existing decentralized workflow research based on vector symbolic architecture (VSA) provides an enhanced semantic search space for efficient and trusted service discovery that is necessary to support a diverse range of emerging edge-computing environments. An architecture for a dynamic decentralized service discovery system, is designed, and described through application to a scenario which uses trusted peers’ reported experiences of an anomaly detection service to determine service selection.

Reproducibility can be considered as one of the basic requirements to ensure that a given research finding is accurate and acceptable. This paper presents a new layered approach that allows scientific researchers to provide a) data to fellow researchers to validate research and b) proofs of research quality to funding agencies, without revealing sensitive details associated with the same. We conclude that by integrating smart contracts, blockchain technology, and self-sovereign identity into an automated system, it is possible to assert the quality of scientific materials and validate the peer review process without the need of a central authority.

Abstract Increased adoption of artificial intelligence (AI) systems into scientific workflows will result in an increasing technical debt as the distance between the data scientists and engineers who develop AI system components and scientists, researchers and other users grows. This could quickly become problematic, particularly where guidance or regulations change and once‐acceptable best practice becomes outdated, or where data sources are later discredited as biased or inaccurate. This paper presents a novel method for deriving a quantifiable metric capable of ranking the overall transparency of the process pipelines used to generate AI systems, such that users, auditors and other stakeholders can gain confidence that they will be able to validate and trust the data sources and contributors in the AI systems that they rely on. The methodology for calculating the metric, and the type of criteria that could be used to make judgements on the visibility of contributions to systems are evaluated through models published at ModelHub and PyTorch Hub, popular archives for sharing science resources, and is found to be helpful in driving consideration of the contributions made to generating AI systems and approaches toward effective documentation and improving transparency in machine learning assets shared within scientific communities.