Fuzzy Logic Function as a Post-hoc Explanator of the Nonlinear Classifier
This addresses the need for interpretability in AI systems for users who must understand decisions, though it is incremental as it builds on existing post-hoc explanation methods.
The paper tackled the problem of interpreting black-box deep neural network decisions by proposing a post-hoc explainable classifier using Zadeh's fuzzy logic function, achieving matching classification decisions with the black box classifier on MNIST and FashionMNIST databases when using DeconvNet importance for truth values.
Pattern recognition systems implemented using deep neural networks achieve better results than linear models. However, their drawback is the black box property. This property means that one with no experience utilising nonlinear systems may need help understanding the outcome of the decision. Such a solution is unacceptable to the user responsible for the final decision. He must not only believe in the decision but also understand it. Therefore, recognisers must have an architecture that allows interpreters to interpret the findings. The idea of post-hoc explainable classifiers is to design an interpretable classifier parallel to the black box classifier, giving the same decisions as the black box classifier. This paper shows that the explainable classifier completes matching classification decisions with the black box classifier on the MNIST and FashionMNIST databases if Zadeh`s fuzzy logic function forms the classifier and DeconvNet importance gives the truth values. Since the other tested significance measures achieved lower performance than DeconvNet, it is the optimal transformation of the feature values to their truth values as inputs to the fuzzy logic function for the databases and recogniser architecture used.