With the rising necessity of explainable artificial intelligence (XAI), we see an increase in task-dependent XAI methods on varying abstraction levels. XAI techniques on a global level explain model behavior and on a local level explain sample predictions. We propose a visual analytics workflow to support seamless transitions between global and local explanations, focusing on attributions and counterfactuals on time series classification. In particular, we adapt local XAI techniques (attributions) that are developed for traditional datasets (images, text) to analyze time series classification, a data type that is typically less intelligible to humans. To generate a global overview, we apply local attribution methods to the data, creating explanations for the whole dataset. These explanations are projected onto two dimensions, depicting model behavior trends, strategies, and decision boundaries. To further inspect the model decision-making as well as potential data errors, a what-if analysis facilitates hypothesis generation and verification on both the global and local levels. We constantly collected and incorporated expert user feedback, as well as insights based on their domain knowledge, resulting in a tailored analysis workflow and system that tightly integrates time series transformations into explanations. Lastly, we present three use cases, verifying that our technique enables users to (1)~explore data transformations and feature relevance, (2)~identify model behavior and decision boundaries, as well as, (3)~the reason for misclassifications.

翻译：随着可解释人工智能（XAI）需求的日益增长，我们在不同抽象层级上看到越来越多任务相关的XAI方法。全局层面的XAI技术解释模型行为，局部层面则解释样本预测结果。我们提出一个可视化分析工作流，支持全局与局部解释之间的无缝切换，重点关注时间序列分类中的归因与反事实。具体而言，我们将为传统数据集（如图像、文本）开发的局部XAI技术（归因方法）调整应用于时间序列分类——这种数据类型通常对人类较难理解。为生成全局概览，我们对数据应用局部归因方法，为整个数据集创建解释。这些解释被投影至二维空间，呈现模型行为趋势、策略和决策边界。为进一步探究模型决策过程及潜在数据错误，我们设计了"假设分析"工具，支持在全局与局部层面进行假设生成与验证。通过持续收集并整合专家用户反馈及其领域知识，最终形成了紧密集成时间序列变换的定制化分析工作流与系统。最后，我们通过三个用例验证：该技术能使使用者（1）探索数据变换与特征重要性，（2）识别模型行为与决策边界，以及（3）定位误分类原因。