  Explainable AI (XAI) is a necessity in safety-critical systems such as in
clinical diagnostics due to a high risk for fatal decisions. Currently,
however, XAI resembles a loose collection of methods rather than a well-defined
process. In this work, we elaborate on conceptual similarities between the
largest subgroup of XAI, interpretable machine learning (IML), and classical
statistics. Based on these similarities, we present a formalization of IML
along the lines of a statistical process. Adopting this statistical view allows
us to interpret machine learning models and IML methods as sophisticated
statistical tools. Based on this interpretation, we infer three key questions,
which we identify as crucial for the success and adoption of IML in
safety-critical settings. By formulating these questions, we further aim to
spark a discussion about what distinguishes IML from classical statistics and
what our perspective implies for the future of the field.

Computer Vision and Pattern Recognition;Artificial Intelligence;Machine Learning