Deep Learning (DL) models processing images to recognize the health state of large infrastructure components can exhibit biases and rely on non-causal shortcuts. eXplainable Artificial Intelligence (XAI) can address these issues but manually analyzing explanations generated by XAI techniques is time-consuming and prone to errors. This work proposes a novel framework that combines post-hoc explanations with semi-supervised learning to automatically identify anomalous explanations that deviate from those of correctly classified images and may therefore indicate model abnormal behaviors. This significantly reduces the workload for maintenance decision-makers, who only need to manually reclassify images flagged as having anomalous explanations. The proposed framework is applied to drone-collected images of insulator shells for power grid infrastructure monitoring, considering two different Convolutional Neural Networks (CNNs), GradCAM explanations and Deep Semi-Supervised Anomaly Detection. The average classification accuracy on two faulty classes is improved by 8% and maintenance operators are required to manually reclassify only 15% of the images. We compare the proposed framework with a state-of-the-art approach based on the faithfulness metric: the experimental results obtained demonstrate that the proposed framework consistently achieves F_1 scores larger than those of the faithfulness-based approach. Additionally, the proposed framework successfully identifies correct classifications that result from non-causal shortcuts, such as the presence of ID tags printed on insulator shells.

翻译：利用图像识别大型基础设施部件健康状态的深度学习模型可能存在偏差并依赖于非因果捷径。可解释人工智能技术能够解决这些问题，但手动分析XAI方法生成的解释耗时且易出错。本研究提出一种新颖框架，将事后解释与半监督学习相结合，自动识别偏离正确分类图像解释的异常解释，从而可能指示模型异常行为。这显著减少了维护决策者的工作量，他们仅需手动重新分类被标记为具有异常解释的图像。该框架应用于电网基础设施监测中无人机采集的绝缘子护套图像，考虑两种不同的卷积神经网络、GradCAM解释方法及深度半监督异常检测技术。在两个故障类别上的平均分类准确率提升8%，维护人员仅需手动重新分类15%的图像。我们将所提框架与基于忠实度度量的前沿方法进行比较：实验结果表明，所提框架始终获得高于基于忠实度方法的F_1分数。此外，该框架成功识别了由非因果捷径导致的正确分类，例如绝缘子护套上印刷的ID标签。