Aadil Ahamed¹, Kamran Alipour¹, Sateesh Kumar¹, Severine Soltani¹ and Michael Pazzani², ¹University of California, USA, ²Information Sciences Institute, USA

In explainable AI (XAI) for deep learning, saliency maps, heatmaps, or attention maps are commonly used to identify important regions for the classification of images of explanations. Recent research has shown that many common XAI methods do not accurately identify the regions that human experts consider important. We propose averaging explanations from ensembles of learners to increase the accuracy of explanations. Our technique is general and can be used with multiple deep learning architectures and multiple XAI algorithms. We show that this method decreases the difference between regions of interest of XAI algorithms and those identified by human experts. Furthermore, we show that human experts prefer the explanations produced by ensembles to those of individual networks.

Neural Networks, Machine Learning, Explainable AI, Image Classification, Computer Vision.