Interaction as Explanation: A User Interaction-based Method for Explaining Image Classification Models

arXiv:2404.09828v1 Announce Type: cross
Abstract: In computer vision, explainable AI (xAI) methods seek to mitigate the 'black-box' problem by making the decision-making process of deep learning models more interpretable and transparent. Traditional xAI methods concentrate on visualizing input features that influence model predictions, providing insights primarily suited for experts. In this work, we present an interaction-based xAI method that enhances user comprehension of image classification models through their interaction. Thus, we developed a web-based prototype allowing users to modify images …

abstract arxiv box classification computer computer vision cs.ai cs.cv cs.hc cs.lg decision deep learning explainable ai features image influence insights making predictions process transparent type vision xai