The Role of Predictive Uncertainty Quantification in Enhancing Model Credibility and Decision-Making Accuracy

Posted: Sep 02, 2021

• Liam Lee
• Riley Nelson
• Matthew Clark

Abstract

The proliferation of machine learning systems in high-stakes decision-making contexts has exposed a critical limitation in conventional modeling approaches: the failure to adequately quantify and communicate predictive uncertainty. Traditional models typically provide point estimates without accompanying measures of confidence, leaving decision-makers ill-equipped to assess model reliability or manage risk effectively. This research addresses this fundamental gap by developing a comprehensive framework for predictive uncertainty quantification that enhances both model credibility and decision-making accuracy across diverse application domains. Current approaches to uncertainty quantification often treat epistemic and aleatoric uncertainty as separate concerns or focus exclusively on one type while neglecting the other. Our work bridges this methodological divide by introducing a hybrid Bayesian-ensemble approach that simultaneously captures both uncertainty types and provides calibrated uncertainty estimates that decision-makers can reliably interpret and utilize.