New Statistical Framework for Extreme Error Probability in High-Stakes Domains for Reliable Machine Learning
This work addresses the need for reliable uncertainty quantification in AI deployment for critical applications, representing a foundational advancement rather than an incremental improvement.
The paper tackles the problem of quantifying extreme errors in machine learning for high-stakes domains by introducing a new statistical framework based on Extreme Value Theory (EVT), which enables robust estimation of catastrophic failure probabilities and overcomes limitations of standard validation methods.
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.