FAIR for AI: An interdisciplinary and international community building perspective

Abstract

A foundational set of findable, accessible, interoperable, and reusable (FAIR) principles were proposed in 2016 as prerequisites for proper data management and stewardship, with the goal of enabling the reusability of scholarly data. The principles were also meant to apply to other digital assets, at a high level, and over time, the FAIR guiding principles have been re-interpreted or extended to include the software, tools, algorithms, and workflows that produce data. FAIR principles are now being adapted in the context of AI models and datasets. Here, we present the perspectives, vision, and experiences of researchers from different countries, disciplines, and backgrounds who are leading the definition and adoption of FAIR principles in their communities of practice, and discuss outcomes that may result from pursuing and incentivizing FAIR AI research. The material for this report builds on the FAIR for AI Workshop held at Argonne National Laboratory on June 7, 2022.

arXiv comment10 pages, comments welcome!; v2: 12 pages, accepted to Scientific Data

References in corpus (19)

• On the Opportunities and Risks of Foundation Models
• PaLM: Scaling Language Modeling with Pathways
• The Machine Learning Landscape of Top Taggers
• funcX: A Federated Function Serving Fabric for Science
• A Data Ecosystem to Support Machine Learning in Materials Science
• OPTIMADE, an API for exchanging materials data
• Accelerated, Scalable and Reproducible AI-driven Gravitational Wave Detection
• Convergence of Artificial Intelligence and High Performance Computing on NSF-supported Cyberinfrastructure
• Deep Transfer Learning for Star Cluster Classification: I. Application to the PHANGS-HST Survey
• FAIR principles for AI models with a practical application for accelerated high energy diffraction microscopy
• Star Cluster Classification in the PHANGS-HST Survey: Comparison between Human and Machine Learning Approaches
• Applications of physics informed neural operators
• Deep Learning at Scale for the Construction of Galaxy Catalogs in the Dark Energy Survey
• Sharing Begins at Home
• Inference-optimized AI and high performance computing for gravitational wave detection at scale
• Physics-inspired deep learning to characterize the signal manifold of quasi-circular, spinning, non-precessing binary black hole mergers
• Deep Learning for Cardiologist-level Myocardial Infarction Detection in Electrocardiograms
• Playing catch-up in building an open research commons
• Explainable AI for High Energy Physics

Cited by in corpus (6)

• WorkflowHub: a registry for computational workflows
• FAIR AI Models in High Energy Physics
• Open and Sustainable AI: challenges, opportunities and the road ahead in the life sciences (October 2025 -- Version 2)
• FAIR Digital Objects for the Realization of Globally Aligned Data Spaces
• Stable and Interpretable Jet Physics with IRC-Safe Equivariant Feature Extraction
• FAIR Ecosystems for Science at Scale