  We argue that interpretations of machine learning (ML) models or the
model-building process can bee seen as a form of sensitivity analysis (SA), a
general methodology used to explain complex systems in many fields such as
environmental modeling, engineering, or economics. We address both researchers
and practitioners, calling attention to the benefits of a unified SA-based view
of explanations in ML and the necessity to fully credit related work. We bridge
the gap between both fields by formally describing how (a) the ML process is a
system suitable for SA, (b) how existing ML interpretation methods relate to
this perspective, and (c) how other SA techniques could be applied to ML.
