A Categorisation of Post-hoc Explanations for Predictive Models

The ubiquity of machine learning based predictive models inmodern society naturally leads people to ask how trustworthythose models are? In predictive modeling, it is quite commonto induce a trade-off between accuracy and interpretability.For instance, doctors would like to know how effective sometreatment will be for a patient or why the model suggesteda particular medication for a patient exhibiting those symptoms? We acknowledge that the necessity for interpretabilityis a consequence of an incomplete formalisation of the prob-lem, or more precisely of multiple meanings adhered to a par-ticular concept. For certain problems, it is not enough to getthe answer (what), the model also has to provide an expla-nation of how it came to that conclusion (why), because acorrect prediction, only partially solves the original problem.In this article we extend existing categorisation of techniquesto aid model interpretability and test this categorisation