Common Metadata Framework: Integrated Framework for Trustworthy Artificial Intelligence Pipelines

In this article, we present the capabilities of the Common Metadata Framework (CMF) to enable trustworthy artificial intelligence (AI). CMF is a decentralized framework for tracking metadata and lineages of datasets and machine learning (ML) models in AI pipelines. The framework provides a few unique features for ML practitioners, such as effortless management of distributed AI pipelines that span across the edge, high-performance systems, and public and private clouds. It provides an unbreakable audit trail and model provenance, resulting in trustworthy models. It ensures reproducibility by versioning artifacts and source code. CMF bridges the gap between the pipeline- and model-centric views of the AI metadata. This end-to-end approach to metadata logging unlocks a comprehensive understanding of ML workflows, enabling more efficient management and optimization of AI pipelines.

The rapid development of large machine learning (ML) models has emphasized the need for trustworthiness in the field of artificial intelligence (AI). The societal impact of these models is enormous, and their implications require careful ethical assessment. The European Commission’s High-Level Expert Group1 stresses the need to build systems that are transparent, auditable, and ensure data privacy. Building trustworthy models necessitates the use of ML tracking frameworks that record every stage in their development (e.g., data preprocessing, model training, and others) for subsequent analysis and audit.