Faithful and Plausible Natural Language Explanations for Image Classification: A Pipeline Approach (2024)

Abstract: Existing explanation methods for image classification struggle to provide faithful and plausible explanations. This paper addresses this issue by proposing a post-hoc natural language explanation method that can be applied to any CNN-based classifier without altering its training process or affecting predictive performance. By analysing influential neurons and the corresponding activation maps, the method generates a faithful description of the classifier’s decision process in the form of a structured meaning representation, which is then converted into text by a language model. Through this pipeline approach, the generated explanations are grounded in the neural network architecture, providing accurate insight into the classification process while remaining accessible to non-experts. Experimental results show that the NLEs constructed by our method are significantly more plausible and faithful than baselines. In particular, user interventions in the neural network structure (masking of neurons) are three times more effective.

## introduction despite remarkable advances in computer vision, the deployment of image classification systems, especially in critical domains, poses significant challenges. one of them is the opacity of deep models and the difficulty of providing reliable explanations for their predictions @xcite . therefore, several types of explanation methods have been proposed, including various forms of saliency maps @xcite , feature importances @xcite , concept-based explanations @xcite , counterfactual explanations @xcite , etc. a particularly interesting form of explaining predictions is offered by natural language explanation (nle) techniques @xcite . such explanations are not only understandable by non-expert users, but can also be used to support conversations with the user in a dialogue system @xcite . there are two critical properties of an explanation: faithfulness and plausibility @xcite . a faithful explanation should accurately reflect the inner workings of the system and provide information on the real reasons why the model reached a certain decision. plausibility then refers to how convincing the explanation appears to the user. in the case of nle, obtaining high plausibility is straightforward, as textual explanations are usually human-friendly @xcite , but achieving faithfulness is challenging. in the context of image classification, image captioning methods offer plausible but unfaithful nles @xcite . some methods try to improve faithfulness by conditioning generation on both the predicted class and image features @xcite @xcite , but the faithfulness provided is still limited as the model is not aware of the classifier's decision process. other methods train image classifiers to jointly predict the class and visual rationales, and generate explanations based on them @xcite . however, the rationales are predicted independently of the class and do not participate in the classifier's decision process. most importantly, such methods change the training procedure and the architecture of the classifier, often affecting classification performance. in this paper, we propose a post-hoc natural language explanation method for image classification that can be used with any standard convolutional neural network (cnn) classifier. to illustrate the classification decision process, the method analyses which neurons of the cnn were most influential in reaching a given decision, and which regions of the image caused them to activate. for each influential neuron, a neuron annotation method computes vi-figure 1 : overview of the presented approach. note that the information provided in the text is supported by the model's internal decision process, e.g. there is a convolutional filter specialized in the detection of "gushes of water", which was strongly activated at the mentioned positions of the image. our simple pipeline method (see fig. 1 and sect. 2) produces explanations that are directly grounded in the classifier's network architecture, but without interfering with its training process or affecting its predictive performance. it also does not need any gold-standard training explanations. the provided nles reflect the process underlying classification by specifying the most influential neurons computed by well-established explainable ai methods. at the same time, the final text is fluent and easy to understand. this results in nles that are both plausible and highly faithful -significantly more so than baselines, as demonstrated in sect. 3. our experimental code is publicly available. foot_0 ## method our nle generation approach uses two processing steps, described below: meaning representation (mr) construction and mr-to-text conversion.