  Artificial Intelligence (AI) increasingly shows its potential to outperform
predicate logic algorithms and human control alike. In automatically deriving a
system model, AI algorithms learn relations in data that are not detectable for
humans. This great strength, however, also makes use of AI methods dubious. The
more complex a model, the more difficult it is for a human to understand the
reasoning for the decisions. As currently, fully automated AI algorithms are
sparse, every algorithm has to provide a reasoning for human operators. For
data engineers, metrics such as accuracy and sensitivity are sufficient.
However, if models are interacting with non-experts, explanations have to be
understandable. This work provides an extensive survey of literature on this
topic, which, to a large part, consists of other surveys. The findings are
mapped to ways of explaining decisions and reasons for explaining decisions. It
shows that the heterogeneity of reasons and methods of and for explainability
lead to individual explanatory frameworks.
