Strong intelligent machines powered by deep neural networks are increasingly deployed as black boxes to make decisions in risksensitive domains, such as finance and medical. To reduce potential risk and build trust with users, it is critical to interpret how such machines make their decisions. Existing works interpret a pretrained neural network by analyzing hidden neurons, mimicking pre-trained models or approximating local predictions. However, these methods do not provide a guarantee on the exactness and consistency of their interpretations. In this paper, we propose an elegant closed form solution named OpenBox to compute exact and consistent interpretations for the family of Piecewise Linear Neural Networks (PLNN). The major idea is to first transform a PLNN into a mathematically equivalent set of linear classifiers, then interpret each linear classifier by the features that dominate its prediction. We further apply OpenBox to demonstrate the effectiveness of nonnegative and sparse constraints on improving the interpretability of PLNNs. The extensive experiments on both synthetic and real world data sets clearly demonstrate the exactness and consistency of our interpretation.
REF proposed interpreting solutions for piece-wise linear neural networks.
Exact and Consistent Interpretation for Piecewise Linear Neural Networks: A Closed Form Solution
Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
Computer Science