|Lingyang Chu||Simon Fraser University|
|Xia Hu||Simon Fraser University|
|Juhua Hu||Simon Fraser University|
|Lanjun Wang||Huawei Technology Co. Ltd|
|Jian Pei||JD.com and Simon Fraser University|
The paper studies Strong intelligent machines powered by deep neural networks. The authors propose an elegant closed form solution named OpenBox to compute exact and consistent interpretations for the family of Piecewise Linear Neural Networks (PLNN).
Strong intelligent machines powered by deep neural networks are increasingly deployed as black boxes to make decisions in risk-sensitive 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 pre-trained 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 non-negative 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.