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

arXiv: https://arxiv.org/abs/2501.03282 · HTML: https://mlsanity.com/paper/2501.03282/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jan 2025.

## Built on by (most cited first, 18 of 18)

- [Uncertainty Quantification of Large Language Models through Multi-Dimensional Responses](https://mlsanity.com/paper/2502.16820/) (2025)
- [Epistemic Uncertainty in Conformal Scores: A Unified Approach](https://mlsanity.com/paper/2502.06995/) (2025)
- [Symmetric Aggregation of Conformity Scores for Efficient Uncertainty Sets](https://mlsanity.com/paper/2512.06945/) (2025)
- [HybridFlow: Quantification of Aleatoric and Epistemic Uncertainty with a Single Hybrid Model](https://mlsanity.com/paper/2510.05054/) (2025)
- [Tracing the Data Trail: A Survey of Data Provenance, Transparency and Traceability in LLMs](https://mlsanity.com/paper/2601.14311/) (2026)
- [Distributional Regression with Tabular Foundation Models: Evaluating Probabilistic Predictions via Proper Scoring Rules](https://mlsanity.com/paper/2603.08206/) (2026)
- [Safe Uncertainty-Aware Learning of Robotic Suturing](https://mlsanity.com/paper/2505.16596/) (2025)
- [Multivariate Uncertainty Quantification with Tomographic Quantile Forests](https://mlsanity.com/paper/2512.16383/) (2025)
- [Managing Uncertainty in LLM-based Multi-Agent System Operation](https://mlsanity.com/paper/2602.23005/) (2026)
- [The Challenge of Out-Of-Distribution Detection in Motor Imagery BCIs](https://mlsanity.com/paper/2603.13324/) (2026)
- [A neurosymbolic Approach with Epistemic Deep Learning for Hierarchical Image Classification](https://mlsanity.com/paper/2605.16383/) (2026)
- [LucidAtlas: Learning Uncertainty-Aware, Covariate-Disentangled, Individualized Atlas Representations](https://mlsanity.com/paper/2502.08445/) (2025)
- [Localising Dropout Variance in Twin Networks](https://mlsanity.com/paper/2507.03622/) (2025)
- [Uncertainty Quantification in Probabilistic Machine Learning Models: Theory, Methods, and Insights](https://mlsanity.com/paper/2509.05877/) (2025)
- [Conditional Uncertainty-Aware Political Deepfake Detection with Stochastic Convolutional Neural Networks](https://mlsanity.com/paper/2602.10343/) (2026)
- [FedCausal-Dyn: A Causal-Dynamic Paradigm for Federated Learning under Dynamic Feature Drift](https://mlsanity.com/paper/2607.09695/) (2026)
- [Graph-Based Modeling of Financial Volatility Dynamics](https://mlsanity.com/paper/2608.26127/) (2026)
- [Impact of Data Augmentation on Confidence Calibration in Melanoma Classification](https://mlsanity.com/paper/2610.06146/) (2026)

## Abstract (arXiv)

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.