FAIR for AI: An interdisciplinary and international community building perspective

TLDR

FAIR principles, introduced in 2016 to promote findable, accessible, interoperable, and reusable data, have been extended to software, tools, algorithms, and workflows and are now being adapted for AI models and datasets. The authors aim to share perspectives and experiences of diverse researchers leading FAIR AI adoption and to discuss potential outcomes of incentivizing FAIR AI research. The report is based on the FAIR for AI Workshop held at Argonne National Laboratory on June 7, 2022, compiling perspectives from researchers across disciplines.

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.