Sources of Uncertainty in Machine Learning -- A Statisticians' View
Cornelia Gruber Patrick Oliver Schenk Malte Schierholz Frauke Kreuter Göran Kauermann
Machine Learning and Deep Learning have achieved an impressive standard
today, enabling us to answer questions that were inconceivable a few years ago.
Besides these successes, it becomes clear, that beyond pure prediction, which
is the primary strength of most supervised machine learning algorithms, the
quantification of uncertainty is relevant and necessary as well. While first
concepts and ideas in this direction have emerged in recent years, this paper
adopts a conceptual perspective and examines possible sources of uncertainty.
By adopting the viewpoint of a statistician, we discuss the concepts of
aleatoric and epistemic uncertainty, which are more commonly associated with
machine learning. The paper aims to formalize the two types of uncertainty and
demonstrates that sources of uncertainty are miscellaneous and can not always
be decomposed into aleatoric and epistemic. Drawing parallels between
statistical concepts and uncertainty in machine learning, we also demonstrate
the role of data and their influence on uncertainty.