From Aleatoric to Epistemic: Exploring Uncertainty Quantification Techniques in Artificial Intelligence
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arXiv
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| Format: | Preprint |
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2025
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| author | Wang, Tianyang Wang, Yunze Zhou, Jun Peng, Benji Song, Xinyuan Zhang, Charles Sun, Xintian Niu, Qian Liu, Junyu Chen, Silin Chen, Keyu Li, Ming Feng, Pohsun Bi, Ziqian Liu, Ming Zhang, Yichao Fei, Cheng Yin, Caitlyn Heqi Yan, Lawrence KQ |
| author_facet | Wang, Tianyang Wang, Yunze Zhou, Jun Peng, Benji Song, Xinyuan Zhang, Charles Sun, Xintian Niu, Qian Liu, Junyu Chen, Silin Chen, Keyu Li, Ming Feng, Pohsun Bi, Ziqian Liu, Ming Zhang, Yichao Fei, Cheng Yin, Caitlyn Heqi Yan, Lawrence KQ |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_03282 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | From Aleatoric to Epistemic: Exploring Uncertainty Quantification Techniques in Artificial Intelligence Wang, Tianyang Wang, Yunze Zhou, Jun Peng, Benji Song, Xinyuan Zhang, Charles Sun, Xintian Niu, Qian Liu, Junyu Chen, Silin Chen, Keyu Li, Ming Feng, Pohsun Bi, Ziqian Liu, Ming Zhang, Yichao Fei, Cheng Yin, Caitlyn Heqi Yan, Lawrence KQ Artificial Intelligence Machine Learning 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. |
| title | From Aleatoric to Epistemic: Exploring Uncertainty Quantification Techniques in Artificial Intelligence |
| topic | Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2501.03282 |