Explainable Artificial Intelligence (XAI) seeks to elucidate the decision-making mechanisms of AI models, enabling users to glean insights beyond the results they produce. While a key objective of XAI is to enhance the performance of AI models through explanatory processes, a notable portion of XAI literature predominantly addresses the explanation of AI systems, with limited focus on leveraging XAI methods for performance improvement. This study introduces a novel approach utilizing Integrated Gradients explanations to enhance a classification system, which is subsequently evaluated on three datasets: Fashion-MNIST, CIFAR10, and STL10. Empirical findings indicate that Integrated Gradients explanations effectively contribute to enhancing classification performance.

An XAI-based masking approach to improve classification systems

Apicella A.;
2023

Abstract

Explainable Artificial Intelligence (XAI) seeks to elucidate the decision-making mechanisms of AI models, enabling users to glean insights beyond the results they produce. While a key objective of XAI is to enhance the performance of AI models through explanatory processes, a notable portion of XAI literature predominantly addresses the explanation of AI systems, with limited focus on leveraging XAI methods for performance improvement. This study introduces a novel approach utilizing Integrated Gradients explanations to enhance a classification system, which is subsequently evaluated on three datasets: Fashion-MNIST, CIFAR10, and STL10. Empirical findings indicate that Integrated Gradients explanations effectively contribute to enhancing classification performance.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11386/4911404
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