Deep learning (DL) models achieve remarkable performance in classification tasks. However, models with high complexity can not be used in many risk-sensitive applications unless a comprehensible explanation is presented. Explainable artificial intelligence (xAI) focuses on the research to explain the decision-making of AI systems like DL. We extend a recent method of Class Activation Maps (CAMs) which visualizes the importance of each feature of a data sample contributing to the classification. In this paper, we aggregate CAMs from multiple samples to show a global explanation of the classification for semantically structured data. The aggregation allows the analyst to make sophisticated assumptions and analyze them with further drill-down visualizations. Our visual representation for the global CAM illustrates the impact of each feature with a square glyph containing two indicators. The color of the square indicates the classification impact of this feature. The size of the filled square describes the variability of the impact between single samples. For interesting features that require further analysis, a detailed view is necessary that provides the distribution of these values. We propose an interactive histogram to filter samples and refine the CAM to show relevant samples only. Our approach allows an analyst to detect important features of high-dimensional data and derive adjustments to the AI model based on our global explanation visualization.

翻译：深度学习模型在分类任务中展现出卓越性能。然而，高复杂度模型若无法提供可理解的解释，则难以应用于许多风险敏感场景。可解释人工智能专注于研究如何解释深度学习等人工智能系统的决策过程。我们扩展了近期提出的类激活图方法，该方法通过可视化数据样本中各特征对分类的贡献程度来呈现其重要性。本文通过聚合多个样本的类激活图，展示语义结构化数据分类的全局解释。该聚合方法使分析师能够提出复杂假设，并通过进一步的钻取式可视化进行分析。我们提出的全局类激活图可视化表示采用包含两个指示符的方形图符展示各特征的影响：方块颜色表示该特征对分类的影响程度，填充方块尺寸描述单个样本间影响程度的变异性。对于需要深入分析的特征，需通过详细视图呈现相关数值的分布情况。我们提出交互式直方图以过滤样本并优化类激活图，仅显示相关样本。该方法使分析师能够检测高维数据中的重要特征，并基于全局解释可视化对AI模型进行相应调整。