The emerging field of eXplainable Artificial Intelligence (XAI) aims to bring transparency to today's powerful but opaque deep learning models. While local XAI methods explain individual predictions in form of attribution maps, thereby identifying where important features occur (but not providing information about what they represent), global explanation techniques visualize what concepts a model has generally learned to encode. Both types of methods thus only provide partial insights and leave the burden of interpreting the model's reasoning to the user. Only few contemporary techniques aim at combining the principles behind both local and global XAI for obtaining more informative explanations. Those methods, however, are often limited to specific model architectures or impose additional requirements on training regimes or data and label availability, which renders the post-hoc application to arbitrarily pre-trained models practically impossible. In this work we introduce the Concept Relevance Propagation (CRP) approach, which combines the local and global perspectives of XAI and thus allows answering both the "where" and "what" questions for individual predictions, without additional constraints imposed. We further introduce the principle of Relevance Maximization for finding representative examples of encoded concepts based on their usefulness to the model. We thereby lift the dependency on the common practice of Activation Maximization and its limitations. We demonstrate the capabilities of our methods in various settings, showcasing that Concept Relevance Propagation and Relevance Maximization lead to more human interpretable explanations and provide deep insights into the model's representations and reasoning through concept atlases, concept composition analyses, and quantitative investigations of concept subspaces and their role in fine-grained decision making.

该论文介绍了概念相关传播 (CRP) 方法，该方法结合了本地和全局 XAI 的观点，从而允许回答单个预测的“何时”和“何物”的问题，而无需施加其他限制。作者进一步介绍了相关性最大化的原则，以找到编码概念的有用代表性示例。作者演示了该方法在各种不同的设置中的能力，展示了概念相关传播和相关性最大化如何通过概念地图、概念组合分析和概念子空间的量化调查及其在细粒度决策中的作用，提供更人性化的解释和深入的模型表示和推理见解。

从“在哪里”到“是什么”: 通过概念关联传播实现人类可理解的解释