FAIR AI Models in High Energy Physics

¹ University of California San Diego, La Jolla, California 92093, USA
² University of Illinois Urbana-Champaign, Urbana, Illinois 61801, USA
³ Argonne National Laboratory, Lemont, Illinois 60439, USA
⁴ The University of Chicago, Chicago, Illinois 60637, USA
⁵ Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
⁶ Halıcıog˘lu Data Science Institute, La Jolla, California 92093, USA
⁷ The University of Minnesota, Minneapolis, Minnesota 55405, USA

^* e-mail: hal113@ucsd.edu

Published online: 6 May 2024

Abstract

The findable, accessible, interoperable, and reusable (FAIR) data principles serve as a framework for examining, evaluating, and improving data sharing to advance scientific endeavors. There is an emerging trend to adapt these principles for machine learning models—algorithms that learn from data without specific coding—and, more generally, AI models, due to AI’s swiftly growing impact on scientific and engineering sectors. In this paper, we propose a practical definition of the FAIR principles for AI models and provide a template program for their adoption. We exemplify this strategy with an implementation from high-energy physics, where a graph neural network is employed to detect Higgs bosons decaying into two bottom quarks.