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FAIR for AI: An interdisciplinary and international community building perspective

Journal Article · · Scientific Data (Online)
 [1];  [1];  [2];  [3];  [4];  [5];  [4];  [6];  [1];  [7];  [8];  [9];  [10];  [11];  [11];  [4];  [12];  [6];  [11];  [13] more »;  [11];  [14];  [11];  [11] « less
  1. Argonne National Laboratory (ANL), Argonne, IL (United States); Univ. of Chicago, IL (United States)
  2. Duke Univ., Durham, NC (United States)
  3. Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States); University of California, Berkeley, CA (United States)
  4. Univ. of California, San Diego, CA (United States)
  5. Lund Univ. (Sweden); Univ. of Manchester (United Kingdom)
  6. Argonne National Laboratory (ANL), Argonne, IL (United States)
  7. Univ. of Virginia, Charlottesville, VA (United States)
  8. Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)
  9. Technical Univ. Munich (Germany)
  10. Brookhaven National Laboratory (BNL), Upton, NY (United States); Rutgers Univ., Piscataway, NJ (United States)
  11. Univ. of Illinois at Urbana-Champaign, IL (United States)
  12. Univ. of Helsinki (Finland)
  13. Institute of Applied Biosciences (Greece)
  14. Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
+ Show Author Affiliations
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.
Research Organization:
Univ. of Virginia, Charlottesville, VA (United States)
Sponsoring Organization:
USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR)
Grant/Contract Number:
SC0023452; AC02-06CH11357; SC0021258; SC0021352; SC0021293
OSTI ID:
3000813
Journal Information:
Scientific Data (Online), Journal Name: Scientific Data (Online) Journal Issue: 1 Vol. 10; ISSN 2052-4463
Publisher:
Nature Publishing GroupCopyright Statement
Country of Publication:
United States
Language:
English

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Related Subjects

97 MATHEMATICS AND COMPUTING
AI
FAIR
data science
research data
research management