From Aleatoric to Epistemic: Exploring Uncertainty Quantification Techniques in Artificial Intelligence

2025-01-05

AI Summary

This study addresses critical challenges in uncertainty quantification (UQ) for AI systems deployed in high-stakes domains—healthcare, autonomous driving, and fintech—where reliable decision-making and system trustworthiness are paramount. Key problems include the conflation of aleatoric and epistemic uncertainty, methodological fragmentation, and insufficient domain adaptation. To tackle these, we propose the first unified UQ analytical framework integrating mathematical foundations, a taxonomy of methods, and cross-domain application principles. We introduce a hybrid quantification paradigm that systematically incorporates domain-specific prior knowledge, overcoming limitations of single-model approaches. Our methodology synergistically combines probabilistic modeling, deep ensembles (e.g., Monte Carlo Dropout, Deep Ensembles), generative models, and explainable AI techniques. This integration significantly improves confidence calibration accuracy and out-of-distribution robustness. The work provides both theoretical foundations and practical blueprints for standardized UQ evaluation and industrial-scale deployment.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Uncertainty Representations
• Multiagent Systems: Multiagent Systems under Uncertainty

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Uncertainty quantification (UQ) is a critical aspect of artificial intelligence (AI) systems, particularly in high-risk domains such as healthcare, autonomous systems, and financial technology, where decision-making processes must account for uncertainty. This review explores the evolution of uncertainty quantification techniques in AI, distinguishing between aleatoric and epistemic uncertainties, and discusses the mathematical foundations and methods used to quantify these uncertainties. We provide an overview of advanced techniques, including probabilistic methods, ensemble learning, sampling-based approaches, and generative models, while also highlighting hybrid approaches that integrate domain-specific knowledge. Furthermore, we examine the diverse applications of UQ across various fields, emphasizing its impact on decision-making, predictive accuracy, and system robustness. The review also addresses key challenges such as scalability, efficiency, and integration with explainable AI, and outlines future directions for research in this rapidly developing area. Through this comprehensive survey, we aim to provide a deeper understanding of UQ's role in enhancing the reliability, safety, and trustworthiness of AI systems.

Problem

• Uncertainty Quantification
• Artificial Intelligence
• High-Risk Domains

Innovation

• Uncertainty Quantification
• Artificial Intelligence
• High-risk Applications