用概念解释AI在多标签图像分类中的困惑，发现模型弱点与数据偏见。
Identifying Confusion Trends in Concept-based XAI for Multi-Label Classification
- 用CRP和CRAFT方法生成概念级解释，分析模型决策逻辑。
- 概念越清晰，标签与概念混淆越少，模型表现更稳定。
- 环境概念暴露数据集带来的偏差，适合医疗自动驾驶领域审慎使用。
部署于医疗、自动驾驶等高风险领域的深度神经网络（DNN）不仅需高精度，还需可解释以建立用户信任。在真实计算机视觉任务中，模型常处理含背景噪声的复杂图像且标注密集。为提升可解释性，需评估概念基础可解释人工智能（CXAI）方法的适用性与解决问题能力。本文在MS-COCO数据集中选取20个标注最丰富的标签，训练VGG16与ResNet50模型，并应用CRP与CRAFT两种CXAI方法生成概念级解释进行整体评估。分析发现：（1）CXAI揭示了DNN的学习弱点；（2）概念区分度越高，标签与概念混淆越低；（3）环境概念暴露了数据集引发的偏差。结果表明，CXAI有助于理解模型泛化能力，并诊断由数据集诱发的偏见。
原文摘要 · Abstract (English)
Deep Neural Networks (DNNs) deployed in high-risk domains, such as healthcare and autonomous driving, must be not only accurate but also understandable to ensure user trust. In real-world computer vision tasks, these models often operate on complex images containing background noise and are heavily annotated. To make such models explainable, Concept-based Explainable AI (CXAI) methods need to be assessed for their applicability and problem-solving capacity. In this work, we explore CXAI use cases in multi-label classification by training two DNNs, VGG16 and ResNet50, on the 20 most annotated labels in the MS-COCO dataset (Microsoft Common Objects in Context). We apply two CXAI methods, CRP (Concept Relevance Propagation) and CRAFT (Concept Recursive Activation FacTorization), to generate concept-level explanations and investigate the overall evaluations. Our analysis reveals three key findings: (1) CXAI highlights learning weaknesses in DNNs, (2) higher concept distinctiveness reduces label and concept confusion, and (3) environmental concepts expose dataset-induced biases. Our results demonstrate the potential of CXAI to enhance the understanding of model generalizability and to diagnose bias instigated by the dataset.
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