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

• URL: http://arxiv.org/abs/2501.03282v1
• Date: Sun, 05 Jan 2025 23:14:47 GMT
• Title: From Aleatoric to Epistemic: Exploring Uncertainty Quantification Techniques in Artificial Intelligence
• Authors: Tianyang Wang, Yunze Wang, Jun Zhou, Benji Peng, Xinyuan Song, Charles Zhang, Xintian Sun, Qian Niu, Junyu Liu, Silin Chen, Keyu Chen, Ming Li, Pohsun Feng, Ziqian Bi, Ming Liu, Yichao Zhang, Cheng Fei, Caitlyn Heqi Yin, Lawrence KQ Yan,
• Abstract summary: Uncertainty quantification (UQ) is a critical aspect of artificial intelligence (AI) systems. This review explores the evolution of uncertainty quantification techniques in AI. We examine the diverse applications of UQ across various fields, emphasizing its impact on decision-making, predictive accuracy, and system robustness.
• Score: 19.369216778200034
• License:
• 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.

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