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.

翻译：机器学习（ML）或人工智能（AI）模型预测的可重复性，以及融入此类ML/AI预测的科学工作流中结果的复现能力，受多种因素驱动。一种能够定量评估关注量（QoI）可复现性的不确定性感知指标，将有助于提高涉及ML/AI模型的科学工作流所得结果的可靠性。本文探讨了贝叶斯范式下的不确定性量化（UQ）如何为复杂科学工作流的可复现性提供通用且严谨的框架。此类框架有望填补当前科学工作流中ML/AI领域的关键空白，使研究人员能够确定ML/AI模型预测变异性对ML/AI驱动工作流预测结果的影响。我们预期，这一构想框架将有助于设计出面向多样化科学应用、更具可复现性与可信度的工作流，并最终加速科学发现。