The eXplainable Artificial Intelligence (XAI) research predominantly concentrates to provide explainations about AI model decisions, especially Deep Learning (DL) models. However, there is a growing interest in using XAI techniques to automatically improve the performance of the AI systems themselves. This paper proposes IMPACTX, a novel approach that leverages XAI as a fully automated attention mechanism, without requiring external knowledge or human feedback. Experimental results show that IMPACTX has improved performance respect to the standalone ML model by integrating an attention mechanism based an XAI method outputs during the model training. Furthermore, IMPACTX directly provides proper feature attribution maps for the model's decisions, without relying on external XAI methods during the inference process. Our proposal is evaluated using three widely recognized DL models (EfficientNet-B2, MobileNet, and LeNet-5) along with three standard image datasets: CIFAR-10, CIFAR-100, and STL-10. The results show that IMPACTX consistently improves the performance of all the inspected DL models across all evaluated datasets, and it directly provides appropriate explanations for its responses.

翻译：可解释人工智能（XAI）研究主要集中在为AI模型决策（尤其是深度学习模型）提供解释。然而，利用XAI技术自动提升AI系统性能的研究日益受到关注。本文提出IMPACTX——一种利用XAI作为全自动注意力机制的新方法，无需外部知识或人工反馈。实验结果表明，通过在模型训练过程中集成基于XAI方法输出的注意力机制，IMPACTX相比独立机器学习模型显著提升了性能。此外，IMPACTX可在推理过程中直接为模型决策提供恰当的特征归因图，无需依赖外部XAI方法。我们使用三种广泛认可的深度学习模型（EfficientNet-B2、MobileNet和LeNet-5）以及三个标准图像数据集（CIFAR-10、CIFAR-100和STL-10）进行评估。结果表明，IMPACTX在所有评估数据集上一致提升了所检深度学习模型的性能，并可直接为其响应提供恰当解释。