4.7 Editorial Material

FAIR for AI: An interdisciplinary and international community building perspective

Journal

SCIENTIFIC DATA
Volume 10, Issue 1, Pages -

Publisher

NATURE PORTFOLIO
DOI: 10.1038/s41597-023-02298-6

Keywords

-

Ask authors/readers for more resources

A set of FAIR principles was proposed in 2016 to ensure proper data management and enable the reusability of scholarly data. Over time, these principles have been expanded to include software, tools, algorithms, and workflows. Currently, FAIR principles are being applied in the context of AI models and datasets.
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.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.7
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available