aiqbench analysis
This research introduces epistemic calibration as a stricter metric than classical calibration, specifically designed to validate the trustworthiness of uncertainty estimates in high-stakes machine learning deployments. By proposing the Expected Epistemic Calibration Error (EECE), the study provides a consistent estimator capable of revealing failure modes that standard predictive performance metrics overlook. These findings are critical for practitioners relying on second-order classification models, as they highlight significant disparities in uncertainty quantification methods even when predictive accuracy appears comparable.
Can we trust our models? Epistemic calibration in second-order classification
arXiv:2606.10777v1 Announce Type: new Abstract: Uncertainty estimation is critical for deploying machine learning models in high-stakes settings. However, classical calibration only assesses the reliability of predicted probabilities and does not evaluate whether epistemic uncertainty estimates are themselves trustworthy. This limitation is particularly relevant for second-order classification models. We introduce epistemic calibration, a principled criterion that measures whether reported epistemic uncertainty faithfully reflects the dispersion of model predictions around the ground truth. We show that epistemic calibration is a strictly stronger notion than classical calibration and captures failure modes invisible to standard metrics. We relate this work to the existing literature through an impossibility theorem that holds under the epistemic calibration hypothesis. To operationalize this concept, we propose the Expected Epistemic Calibration Error (EECE), which we prove to be a consistent estimator of a True Epistemic Calibration Error (TECE). Experiments across a broad range of uncertainty quantification methods show that epistemic calibration is a coherent and meaningful criterion and reveal substantial differences across methods, despite similar predictive performance.
Title:Can we trust our models? Epistemic calibration in second-order classification
Abstract:Uncertainty estimation is critical for deploying machine learning models in high-stakes settings. However, classical calibration only assesses the reliability of predicted probabilities and does not evaluate whether epistemic uncertainty estimates are themselves trustworthy. This limitation is particularly relevant for second-order classification models. We introduce epistemic calibration, a principled criterion that measures whether reported epistemic uncertainty faithfully reflects the dispersion of model predictions around the ground truth. We show that epistemic calibration is a strictly stronger notion than classical calibration and captures failure modes invisible to standard metrics. We relate this work to the existing literature through an impossibility theorem that holds under the epistemic calibration hypothesis. To operationalize this concept, we propose the Expected Epistemic Calibration Error (EECE), which we prove to be a consistent estimator of a True Epistemic Calibration Error (TECE). Experiments across a broad range of uncertainty quantification methods show that epistemic calibration is a coherent and meaningful criterion and reveal substantial differences across methods, despite similar predictive performance.
Machine Learning (cs.LG)
arXiv:2606.10777 [cs.LG]
(or arXiv:2606.10777v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2606.10777 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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