Strategies to exploit XAI to improve classification systems
This addresses the problem of enhancing AI model performance for users by leveraging XAI insights, though it is incremental as it builds on existing XAI methods.
The paper investigated whether XAI methods can improve classification performance, not just provide explanations, and found that Integrated Gradients highlighted features that effectively boosted performance on datasets like Fashion-MNIST, CIFAR10, and STL10.
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.