  It is oftentimes impossible to understand how machine learning models reach a
decision. While recent research has proposed various technical approaches to
provide some clues as to how a learning model makes individual decisions, they
cannot provide users with ability to inspect a learning model as a complete
entity. In this work, we propose a new technical approach that augments a
Bayesian regression mixture model with multiple elastic nets. Using the
enhanced mixture model, we extract explanations for a target model through
global approximation. To demonstrate the utility of our approach, we evaluate
it on different learning models covering the tasks of text mining and image
recognition. Our results indicate that the proposed approach not only
outperforms the state-of-the-art technique in explaining individual decisions
but also provides users with an ability to discover the vulnerabilities of a
learning model.
