  The need for systems to explain behavior to users has become more evident
with the rise of complex technology like machine learning or self-adaptation.
In general, the need for an explanation arises when the behavior of a system
does not match the user's expectations. However, there may be several reasons
for a mismatch including errors, goal conflicts, or multi-agent interference.
Given the various situations, we need precise and agreed descriptions of
explanation needs as well as benchmarks to align research on explainable
systems. In this paper, we present a taxonomy that structures needs for an
explanation according to different reasons. We focus on explanations to improve
the user interaction with the system. For each leaf node in the taxonomy, we
provide a scenario that describes a concrete situation in which a software
system should provide an explanation. These scenarios, called explanation
cases, illustrate the different demands for explanations. Our taxonomy can
guide the requirements elicitation for explanation capabilities of interactive
intelligent systems and our explanation cases build the basis for a common
benchmark. We are convinced that both, the taxonomy and the explanation cases,
help the community to align future research on explainable systems.
