New Statistical Framework for Extreme Error Probability in High-Stakes Domains for Reliable Machine Learning
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866916667983921152 |
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| author | Michelucci, Umberto Venturini, Francesca |
| author_facet | Michelucci, Umberto Venturini, Francesca |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2503_24262 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | New Statistical Framework for Extreme Error Probability in High-Stakes Domains for Reliable Machine Learning Michelucci, Umberto Venturini, Francesca Machine Learning Artificial Intelligence Methodology 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. |
| title | New Statistical Framework for Extreme Error Probability in High-Stakes Domains for Reliable Machine Learning |
| topic | Machine Learning Artificial Intelligence Methodology |
| url | https://arxiv.org/abs/2503.24262 |