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.

不确定性的概念在机器学习中非常重要，并且构成了机器学习方法的关键要素。根据统计传统，长期以来，不确定性几乎被视为标准概率和概率预测的同义词。然而，由于机器学习在实际应用中的重要性与日俱增，以及诸如安全性要求之类的相关问题，机器学习学者最近发现了新的问题和挑战，这些问题可能需要新的方法论发展。特别是，这包括区分（至少）两种不同类型的不确定性（通常称为无意识的和认知的）的重要性。。在本文中，我们对机器学习中的不确定性主题进行了介绍，并概述了迄今为止在一般情况下处理不确定性以及特别是将这种区分形式化的尝试。