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Estimating uncertainty is at the core of performing scientific measurementsin HEP: a measurement is not useful without an estimate of its uncertainty. Thegoal of uncertainty quantification (UQ) is inextricably linked to the question,"how do we physically and statistically interpret these uncertainties?" Theanswer to this question depends not only on the computational task we aim toundertake, but also on the methods we use for that task. For artificialintelligence (AI) applications in HEP, there are several areas whereinterpretable methods for UQ are essential, including inference, simulation,and control/decision-making. There exist some methods for each of these areas,but they have not yet been demonstrated to be as trustworthy as moretraditional approaches currently employed in physics (e.g., non-AI frequentistand Bayesian methods).  Shedding light on the questions above requires additional understanding ofthe interplay of AI systems and uncertainty quantification. We briefly discussthe existing methods in each area and relate them to tasks across HEP. We thendiscuss recommendations for avenues to pursue to develop the necessarytechniques for reliable widespread usage of AI with UQ over the next decade.

Interpretable Uncertainty Quantification in AI for HEP