Position Paper: Bridging the Gap Between Machine Learning and
  Sensitivity Analysis
Christian A. Scholbeck Julia Moosbauer Giuseppe Casalicchio Hoshin Gupta Bernd Bischl Christian Heumann
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.