  The increasing incorporation of Artificial Intelligence in the form of
automated systems into decision-making procedures highlights not only the
importance of decision theory for automated systems but also the need for these
decision procedures to be explainable to the people involved in them.
Traditional realist accounts of explanation, wherein explanation is a relation
that holds (or does not hold) eternally between an explanans and an
explanandum, are not adequate to account for the notion of explanation required
for artificial decision procedures. We offer an alternative account of
explanation as used in the context of automated decision-making that makes
explanation an epistemic phenomenon, and one that is dependent on context. This
account of explanation better accounts for the way that we talk about, and use,
explanations and derived concepts, such as `explanatory power', and also allows
us to differentiate between reasons or causes on the one hand, which do not
need to have an epistemic aspect, and explanations on the other, which do have
such an aspect. Against this theoretical backdrop we then review existing
approaches to explanation in Artificial Intelligence and Machine Learning, and
suggest desiderata which truly explainable decision systems should fulfill.
