Explaining black boxes with a SMILE: Statistical Model-agnostic
  Interpretability with Local Explanations
Koorosh Aslansefat Mojgan Hashemian Martin Walker Mohammed Naveed Akram Ioannis Sorokos Yiannis Papadopoulos
Machine learning is currently undergoing an explosion in capability,
popularity, and sophistication. However, one of the major barriers to
widespread acceptance of machine learning (ML) is trustworthiness: most ML
models operate as black boxes, their inner workings opaque and mysterious, and
it can be difficult to trust their conclusions without understanding how those
conclusions are reached. Explainability is therefore a key aspect of improving
trustworthiness: the ability to better understand, interpret, and anticipate
the behaviour of ML models. To this end, we propose SMILE, a new method that
builds on previous approaches by making use of statistical distance measures to
improve explainability while remaining applicable to a wide range of input data
domains.