Is FAIR FAIR? A discussion of the overlaps in the FAIR principles of data management and AI-Readiness.

This BoF session will explore the FAIR principles and compare them with the necessary metadata for Artificial Intelligence (AI) readiness. AI-readiness can be defined as, “...taking steps to collect data around relevant systems, equipment, and procedures; and storing and curating that data in a way that makes it easily accessible to others for use in future AI applications.” (US DoD - AI in Defense) We will bring together community experts in both the FAIR principles and AI readiness with the purpose of determining the extent of overlap between the FAIR principles and the metadata needed to describe datasets as AI-ready. We will explore from the point of view of sub-domains of AI rather than from a disciplinary perspective. These subdomains will include image analysis, audio analysis, and natural language processing. The expected outcomes are twofold, to explore the questions of how fully aligned the FAIR principles are with the concept of AI-readiness (Is FAIR FAIR?) and to highlight potential extensions necessary in the AI sub-domains identified to achieve both FAIRness and AI-readiness. Upon completion of the BoF the organizers will determine if merging with the current FAIR for ML effort in RDA to expand the discussion to include AI-Readiness or if an RDA Working Group based on recommending a FAIR metadata profile as an extension of the PID Kernel Information recommendations is the proper continuation of this effort.