The evolution of deep learning and artificial intelligence has significantly reshaped technological landscapes. However, their effective application in crucial sectors such as medicine demands more than just superior performance, but trustworthiness as well. While interpretability plays a pivotal role, existing explainable AI (XAI) approaches often do not reveal {\em Clever Hans} behavior where a model makes (ungeneralizable) correct predictions using spurious correlations or biases in data. Likewise, current post-hoc XAI methods are susceptible to generating unjustified counterfactual examples. In this paper, we approach XAI with an innovative {\em model debugging} methodology realized through Jacobian Saliency Map (JSM). To cast the problem into a concrete context, we employ Alzheimer's disease (AD) diagnosis as the use case, motivated by its significant impact on human lives and the formidable challenge in its early detection, stemming from the intricate nature of its progression. We introduce an interpretable, multimodal model for AD classification over its multi-stage progression, incorporating JSM as a modality-agnostic tool that provides insights into volumetric changes indicative of brain abnormalities. Our extensive evaluation including ablation study manifests the efficacy of using JSM for model debugging and interpretation, while significantly enhancing model accuracy as well.

翻译：深度学习和人工智能的演进已深刻重塑技术格局。然而，在医学等关键领域的有效应用不仅需要卓越的性能，更需要可信赖性。尽管可解释性发挥着关键作用，现有可解释人工智能方法往往无法揭示模型利用数据中虚假相关性或偏差做出正确预测的"聪明汉斯"行为。同样，当前的事后可解释方法也容易生成不合理的反事实示例。本文通过雅可比显著性图提出了一种创新的模型调试方法论来实现可解释人工智能。为将问题置于具体语境，我们以阿尔茨海默病诊断作为应用案例，其动机源于该疾病对人类生活的重大影响及其进展复杂性导致的早期检测难题。我们构建了一个可解释的多模态模型用于阿尔茨海默病多阶段进展分类，将雅可比显著性图作为模态无关工具，提供反映脑部异常的体积变化指标。包含消融研究的全面评估证实了雅可比显著性图在模型调试与解释中的有效性，同时显著提升了模型精度。