Artificial Intelligence (AI), to include Machine Learning (ML), depends on data quality. This project considers how to capture, render and share the attributes of provenance, accountability [e.g. audit trails], trustworthiness, context, structure, patterns, annotation and annotation history at each step in the life cycle of the data. The goals are to provide implementation guidance for AI/ML projects to leverage discoverable patterns and annotations provided by standards-based interoperable datasets, provide a roadmap for AI/ML experts to take advantage of interoperability standards to combine data from multiple, disparate data sources; and, in turn, articulate the return on investment of using interoperable, HL7-conformant data sets to create AI/ML solutions that are trusted by clinicians.