  Deep convolutional neural networks have proven their effectiveness, and have
been acknowledged as the most dominant method for image classification.
However, a severe drawback of deep convolutional neural networks is poor
explainability. Unfortunately, in many real-world applications, users need to
understand the rationale behind the predictions of deep convolutional neural
networks when determining whether they should trust the predictions or not. To
resolve this issue, a novel genetic algorithm-based method is proposed for the
first time to automatically evolve local explanations that can assist users to
assess the rationality of the predictions. Furthermore, the proposed method is
model-agnostic, i.e., it can be utilised to explain any deep convolutional
neural network models. In the experiments, ResNet is used as an example model
to be explained, and the ImageNet dataset is selected as the benchmark dataset.
DenseNet and MobileNet are further explained to demonstrate the model-agnostic
characteristic of the proposed method. The evolved local explanations on four
images, randomly selected from ImageNet, are presented, which show that the
evolved local explanations are straightforward to be recognised by humans.
Moreover, the evolved explanations can explain the predictions of deep
convolutional neural networks on all four images very well by successfully
capturing meaningful interpretable features of the sample images. Further
analysis based on the 30 runs of the experiments exhibits that the evolved
local explanations can also improve the probabilities/confidences of the deep
convolutional neural network models in making the predictions. The proposed
method can obtain local explanations within one minute, which is more than ten
times faster than LIME (the state-of-the-art method).
