Formal Verification of Bayesian Network Classifiers


Andy Shih, Arthur Choi, Adnan Darwiche ;
Proceedings of the Ninth International Conference on Probabilistic Graphical Models, PMLR 72:427-438, 2018.


A new approach was recently proposed for {\em explaining} the decisions made by Bayesian network classifiers. This approach is based on first compiling a given classifier (i.e., its decision function) into a tractable representation called an Ordered Decision Diagram (ODD). Given an ODD representation of the decision function, we get the ability to provide reasons for why a classifier labels a given instance positively or negatively. We show in this paper that this approach also gives us the ability to {\em verify} the behavior of classifiers. We also provide case studies in explaining and verifying classifiers for some real-world domains, such as in medical diagnosis and educational assessment.

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