  The ubiquity of machine learning based predictive models in modern society
naturally leads people to ask how trustworthy those models are? In predictive
modeling, it is quite common to induce a trade-off between accuracy and
interpretability. For instance, doctors would like to know how effective some
treatment will be for a patient or why the model suggested a particular
medication for a patient exhibiting those symptoms? We acknowledge that the
necessity for interpretability is a consequence of an incomplete formalisation
of the problem, or more precisely of multiple meanings adhered to a particular
concept. For certain problems, it is not enough to get the answer (what), the
model also has to provide an explanation of how it came to that conclusion
(why), because a correct prediction, only partially solves the original
problem. In this article we extend existing categorisation of techniques to aid
model interpretability and test this categorisation.
