Aleatoric and epistemic uncertainty in machine learning: an introduction to concepts and methods

• Published in: Machine learning Vol. 110; no. 3; pp. 457 - 506
• Main Authors: Hüllermeier, Eyke, Waegeman, Willem
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.03.2021; Springer Nature B.V
• Subjects: Artificial Intelligence; Computer Science; Control; Machine Learning; Mechatronics; Natural Language Processing (NLP); Robotics; Simulation and Modeling; Statistical analysis; Uncertainty; Uncertainty; Deep neural networks; Probability; Version space learning; Credal sets and classifiers; Calibration; Generative models; Ensembles; Epistemic uncertainty; Gaussian processes; Bayesian inference; Likelihood-based methods; Set-valued prediction; Conformal prediction
• ISSN: 0885-6125; 1573-0565
• DOI: 10.1007/s10994-021-05946-3

The notion of uncertainty is of major importance in machine learning and constitutes a key element of machine learning methodology. In line with the statistical tradition, uncertainty has long been perceived as almost synonymous with standard probability and probabilistic predictions. Yet, due to the steadily increasing relevance of machine learning for practical applications and related issues such as safety requirements, new problems and challenges have recently been identified by machine learning scholars, and these problems may call for new methodological developments. In particular, this includes the importance of distinguishing between (at least) two different types of uncertainty, often referred to as aleatoric and epistemic . In this paper, we provide an introduction to the topic of uncertainty in machine learning as well as an overview of attempts so far at handling uncertainty in general and formalizing this distinction in particular.