  Understanding and interpreting how machine learning (ML) models make
decisions have been a big challenge. While recent research has proposed various
technical approaches to provide some clues as to how an ML model makes
individual predictions, they cannot provide users with an ability to inspect a
model as a complete entity. In this work, we propose a novel technical approach
that augments a Bayesian non-parametric regression mixture model with multiple
elastic nets. Using the enhanced mixture model, we can extract generalizable
insights for a target model through a global approximation. To demonstrate the
utility of our approach, we evaluate it on different ML models in the context
of image recognition. The empirical results indicate that our proposed approach
not only outperforms the state-of-the-art techniques in explaining individual
decisions but also provides users with an ability to discover the
vulnerabilities of the target ML models.
