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.
@article{arxiv.2210.08973,
title = {FAIR for AI: An interdisciplinary and international community building perspective},
author = {E. A. Huerta and Ben Blaiszik and L. Catherine Brinson and Kristofer E. Bouchard and Daniel Diaz and Caterina Doglioni and Javier M. Duarte and Murali Emani and Ian Foster and Geoffrey Fox and Philip Harris and Lukas Heinrich and Shantenu Jha and Daniel S. Katz and Volodymyr Kindratenko and Christine R. Kirkpatrick and Kati Lassila-Perini and Ravi K. Madduri and Mark S. Neubauer and Fotis E. Psomopoulos and Avik Roy and Oliver Rübel and Zhizhen Zhao and Ruike Zhu},
journal= {arXiv preprint arXiv:2210.08973},
year = {2023}
}
Comments
10 pages, comments welcome!; v2: 12 pages, accepted to Scientific Data