New Statistical Framework for Extreme Error Probability in High-Stakes Domains for Reliable Machine Learning

• URL: http://arxiv.org/abs/2503.24262v1
• Date: Mon, 31 Mar 2025 16:08:11 GMT
• Title: New Statistical Framework for Extreme Error Probability in High-Stakes Domains for Reliable Machine Learning
• Authors: Umberto Michelucci, Francesca Venturini,
• Abstract summary: Extreme Value Theory (EVT) is a statistical framework that provides a rigorous approach to estimating worst-case failures.<n>Applying EVT to synthetic and real-world datasets, this method is shown to enable robust estimation of catastrophic failure probabilities.<n>This work establishes EVT as a fundamental tool for assessing model reliability, ensuring safer AI deployment in new technologies.
• Score: 4.14360329494344
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Machine learning is vital in high-stakes domains, yet conventional validation methods rely on averaging metrics like mean squared error (MSE) or mean absolute error (MAE), which fail to quantify extreme errors. Worst-case prediction failures can have substantial consequences, but current frameworks lack statistical foundations for assessing their probability. In this work a new statistical framework, based on Extreme Value Theory (EVT), is presented that provides a rigorous approach to estimating worst-case failures. Applying EVT to synthetic and real-world datasets, this method is shown to enable robust estimation of catastrophic failure probabilities, overcoming the fundamental limitations of standard cross-validation. This work establishes EVT as a fundamental tool for assessing model reliability, ensuring safer AI deployment in new technologies where uncertainty quantification is central to decision-making or scientific analysis.

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