  With the recent advances in the field of artificial intelligence, an
increasing number of decision-making tasks are delegated to software systems. A
key requirement for the success and adoption of such systems is that users must
trust system choices or even fully automated decisions. To achieve this,
explanation facilities have been widely investigated as a means of establishing
trust in these systems since the early years of expert systems. With today's
increasingly sophisticated machine learning algorithms, new challenges in the
context of explanations, accountability, and trust towards such systems
constantly arise. In this work, we systematically review the literature on
explanations in advice-giving systems. This is a family of systems that
includes recommender systems, which is one of the most successful classes of
advice-giving software in practice. We investigate the purposes of explanations
as well as how they are generated, presented to users, and evaluated. As a
result, we derive a novel comprehensive taxonomy of aspects to be considered
when designing explanation facilities for current and future decision support
systems. The taxonomy includes a variety of different facets, such as
explanation objective, responsiveness, content and presentation. Moreover, we
identified several challenges that remain unaddressed so far, for example
related to fine-grained issues associated with the presentation of explanations
and how explanation facilities are evaluated.
