SHAP-based explanations to improve classification systems

Explainable Artificial Intelligence (XAI) is a field usually dedicated to offering insights into the decision-making mechanisms of AI models. Its purpose is to enable users to comprehend the reasoning behind the results provided by these models, going beyond mere outputs. In addition, one of the main goals of XAI is to improve the performance of AI models by exploiting the explanations of their decision-making processes. However, a predominant portion of XAI research concentrates on elucidating the functioning of AI systems, with comparatively fewer studies delving into how XAI techniques can be leveraged to enhance the performance of an AI system. This underlines a potential area for further exploration and development in the field of XAI. In this paper we focus on the possibility to enhance the performance of an already trained AI model. To this aim we propose a new scheme of interaction between explanations provided by SHAP XAI method and computations of the responses of a given AI model. This new proposal was tested using the well-known CIFAR-10 dataset and EfficientNet-B2 model, showing promising results.