Explainable AI aims to render model behavior understandable by humans, which can be seen as an intermediate step in extracting causal relations from correlative patterns. Due to the high risk of possible fatal decisions in image-based clinical diagnostics, it is necessary to integrate explainable AI into these safety-critical systems. Current explanatory methods typically assign attribution scores to pixel regions in the input image, indicating their importance for a model's decision. However, they fall short when explaining why a visual feature is used. We propose a framework that utilizes interpretable disentangled representations for downstream-task prediction. Through visualizing the disentangled representations, we enable experts to investigate possible causation effects by leveraging their domain knowledge. Additionally, we deploy a multi-path attribution mapping for enriching and validating explanations. We demonstrate the effectiveness of our approach on a synthetic benchmark suite and two medical datasets. We show that the framework not only acts as a catalyst for causal relation extraction but also enhances model robustness by enabling shortcut detection without the need for testing under distribution shifts.

翻译：可解释人工智能旨在使模型行为对人类可理解，这可被视为从相关模式中提取因果关系的中间步骤。由于图像临床诊断中可能出现致命决策的高风险，有必要将可解释AI集成到这些安全关键系统中。当前的解释方法通常为输入图像中的像素区域分配归因分数，以表明其对模型决策的重要性。然而，这些方法在解释某个视觉特征为何被使用时存在不足。我们提出一个框架，该框架利用可解释的解耦表示进行下游任务预测。通过可视化这些解耦表示，专家可借助其领域知识研究潜在的因果关系。此外，我们采用多路径归因映射来丰富和验证解释内容。我们在合成基准测试集和两个医学数据集上验证了该方法的有效性。实验表明，该框架不仅可作为因果关系的催化剂，还能在不需测试分布偏移的情况下，通过实现快捷检测来增强模型鲁棒性。