Evolving local interpretable model-agnostic explanations for deep neural networks in image classification
Bin Wang, Wenbin Pei, Bing Xue, Mengjie Zhang · Proceedings of the Genetic and Evolutionary Computation Conference Companion · 2021
For deep convolutional neural networks (deep CNNs), a severe drawback is the poor interpretability. To address this drawback, this paper proposes a novel genetic algorithm-based method for the first time to automatically evolve local interpretable explanations that can assist users to decide whether to trust the predictions of deep CNNs. In the experiments, the results show that the evolved explanations can explain the predictions of deep CNNs on images by successfully capturing meaningful interpretable features.