Explainable Artificial Intelligence (XAI) aims to provide insights into the decision-making process of AI models, allowing users to understand their results beyond their decisions. A significant goal of XAI is to improve the performance of AI models by providing explanations for their decision-making processes. However, most XAI literature focuses on how to explain an AI system, while less attention has been given to how XAI methods can be exploited to improve an AI system. In this work, a set of well-known XAI methods typically used with Machine Learning (ML) classification tasks are investigated to verify if they can be exploited, not just to provide explanations but also to improve the performance of the model itself. To this aim, two strategies to use the explanation to improve a classification system are reported and empirically evaluated on three datasets: Fashion-MNIST, CIFAR10, and STL10. Results suggest that explanations built by Integrated Gradients highlight input features that can be effectively used to improve classification performance.

翻译：可解释人工智能（XAI）旨在揭示AI模型的决策过程，使用户能够理解其决策结果之外的信息。XAI的一个重要目标是通过解释其决策过程来提升AI模型的性能。然而，现有XAI文献大多聚焦于如何解释AI系统，而较少关注如何利用XAI方法来改进AI系统本身。本研究针对机器学习分类任务中常用的多组主流XAI方法进行验证，探究其是否不仅能提供解释，还能提升模型自身性能。为此，本文提出了两种利用解释信息改进分类系统的策略，并在Fashion-MNIST、CIFAR10和STL10三个数据集上进行了实证评估。结果表明，基于集成梯度生成的特征解释能有效突出可用于提升分类性能的输入特征。