  Explainable artificial intelligence (XAI) seeks to produce explanations for
those machine learning methods which are deemed opaque. However, there is
considerable disagreement about what this means and how to achieve it. Authors
disagree on what should be explained (topic), to whom something should be
explained (stakeholder), how something should be explained (instrument), and
why something should be explained (goal). In this paper, I employ insights from
means-end epistemology to structure the field. According to means-end
epistemology, different means ought to be rationally adopted to achieve
different epistemic ends. Applied to XAI, different topics, stakeholders, and
goals thus require different instruments. I call this the means-end account of
XAI. The means-end account has a descriptive and a normative component: on the
one hand, I show how the specific means-end relations give rise to a taxonomy
of existing contributions to the field of XAI; on the other hand, I argue that
the suitability of XAI methods can be assessed by analyzing whether they are
prescribed by a given topic, stakeholder, and goal.
