  Predictions obtained by, e.g., artificial neural networks have a high
accuracy but humans often perceive the models as black boxes. Insights about
the decision making are mostly opaque for humans. Particularly understanding
the decision making in highly sensitive areas such as healthcare or fifinance,
is of paramount importance. The decision-making behind the black boxes requires
it to be more transparent, accountable, and understandable for humans. This
survey paper provides essential definitions, an overview of the different
principles and methodologies of explainable Supervised Machine Learning (SML).
We conduct a state-of-the-art survey that reviews past and recent explainable
SML approaches and classifies them according to the introduced definitions.
Finally, we illustrate principles by means of an explanatory case study and
discuss important future directions.
