FAIR AI Models in High Energy Physics. (arXiv:2212.05081v1 [hep-ex])

The findable, accessible, interoperable, and reusable (FAIR) data principles
have provided a framework for examining, evaluating, and improving how we share
data with the aim of facilitating scientific discovery. Efforts have been made
to generalize these principles to research software and other digital products.
Artificial intelligence (AI) models — algorithms that have been trained on
data rather than explicitly programmed — are an important target for this
because of the ever-increasing pace with which AI is transforming scientific
and engineering domains. In this paper, we propose a practical definition of
FAIR principles for AI models and create a FAIR AI project template that
promotes adherence to these principles. We demonstrate how to implement these
principles using a concrete example from experimental high energy physics: a
graph neural network for identifying Higgs bosons decaying to bottom quarks. We
study the robustness of these FAIR AI models and their portability across
hardware architectures and software frameworks, and report new insights on the
interpretability of AI predictions by studying the interplay between FAIR
datasets and AI models. Enabled by publishing FAIR AI models, these studies
pave the way toward reliable and automated AI-driven scientific discovery.