Physical Sciences Division
Published July 26, 2023 | Version v1
Journal article Open

FAIR for AI: An interdisciplinary and international community building perspective

Creators

  • 1. University of Chicago
  • 2. Duke University
  • 3. Lawrence Berkeley National Laboratory
  • 4. University of California, San Diego
  • 5. Lund University
  • 6. Argonne National Laboratory
  • 7. University of Virginia
  • 8. Massachusetts Institute of Technology
  • 9. Technical University Munich
  • 10. Computational Science Initiative Brookhaven National Laboratory Upton
  • 11. University of Illinois
  • 12. University of Helsinki
  • 13. Centre for Research and Technology Hellas

Description

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.

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FAIR-for-AI.pdf

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Additional details

Identifiers

DOI
10.1038/s41597-023-02298-6
Other
oai:uchicago.tind.io:6853

Funding

U.S. Department of Energy
FAIR Data program
National Science Foundation
1931306
National Science Foundation
2209892
National Science Foundation
1916481
National Science Foundation
2226453
U.S. Department of Energy
FAIR Data program
U.S. Department of Energy
FAIR Data program
European Research Council
Horizon 2020 research and innovation program
European Research Council
Horizon 2020 research and innovation program
European Research Council
Horizon 2020 research and innovation program
Swedish Research Council
U.S. Department of Energy
Advanced Scientific Computing Program

UChicago Information

Division(s)
Physical Sciences Division
Department(s)
Computer Science