FAIR for AI: An interdisciplinary and international community building perspective

TL;DR

This paper discusses the adaptation and implementation of FAIR principles for AI models and datasets, emphasizing interdisciplinary and international community efforts to promote FAIR AI research.

Contribution

It presents diverse perspectives and experiences on applying FAIR principles to AI, highlighting community-driven initiatives and potential outcomes.

Findings

FAIR principles are being extended to AI models and datasets.

Community efforts are crucial for FAIR AI adoption.

Potential for improved AI data management and reuse.

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.