1 research outputs found
A Rigorous Uncertainty-Aware Quantification Framework Is Essential for Reproducible and Replicable Machine Learning Workflows
The ability to replicate predictions by machine learning (ML) or artificial
intelligence (AI) models and results in scientific workflows that incorporate
such ML/AI predictions is driven by numerous factors. An uncertainty-aware
metric that can quantitatively assess the reproducibility of quantities of
interest (QoI) would contribute to the trustworthiness of results obtained from
scientific workflows involving ML/AI models. In this article, we discuss how
uncertainty quantification (UQ) in a Bayesian paradigm can provide a general
and rigorous framework for quantifying reproducibility for complex scientific
workflows. Such as framework has the potential to fill a critical gap that
currently exists in ML/AI for scientific workflows, as it will enable
researchers to determine the impact of ML/AI model prediction variability on
the predictive outcomes of ML/AI-powered workflows. We expect that the
envisioned framework will contribute to the design of more reproducible and
trustworthy workflows for diverse scientific applications, and ultimately,
accelerate scientific discoveries