  In the meantime, a wide variety of terminologies, motivations, approaches,
and evaluation criteria have been developed within the research field of
explainable artificial intelligence (XAI). With the amount of XAI methods
vastly growing, a taxonomy of methods is needed by researchers as well as
practitioners: To grasp the breadth of the topic, compare methods, and to
select the right XAI method based on traits required by a specific use-case
context. Many taxonomies for XAI methods of varying level of detail and depth
can be found in the literature. While they often have a different focus, they
also exhibit many points of overlap. This paper unifies these efforts and
provides a complete taxonomy of XAI methods with respect to notions present in
the current state of research. In a structured literature analysis and
meta-study, we identified and reviewed more than 50 of the most cited and
current surveys on XAI methods, metrics, and method traits. After summarizing
them in a survey of surveys, we merge terminologies and concepts of the
articles into a unified structured taxonomy. Single concepts therein are
illustrated by more than 50 diverse selected example methods in total, which we
categorize accordingly. The taxonomy may serve both beginners, researchers, and
practitioners as a reference and wide-ranging overview of XAI method traits and
aspects. Hence, it provides foundations for targeted, use-case-oriented, and
context-sensitive future research.
