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.

本文提出了一个可视化分析工作流程，以支持全局和局部解释的无缝转换，重点是基于时间序列分类的属性和反事实分析，通过采用本地 XAI 技术（归因），将其应用于传统数据集（图像，文本）来分析时间序列分类，进而生成全局概览。

基于归因和反事实的时间序列分类视觉解释

Visual Explanations with Attributions and Counterfactuals on Time Series  Classification

Human-in-the-loop data analysis applications necessitate greater transparency
in machine learning models for experts to understand and trust their decisions.
To this end, we propose a visual analytics workflow to help data scientists and
domain experts explore, diagnose, and understand the decisions made by a binary
classifier. The approach leverages "instance-level explanations", measures of
local feature relevance that explain single instances, and uses them to build a
set of visual representations that guide the users in their investigation. The
workflow is based on three main visual representations and steps: one based on
aggregate statistics to see how data distributes across correct / incorrect
decisions; one based on explanations to understand which features are used to
make these decisions; and one based on raw data, to derive insights on
potential root causes for the observed patterns. The workflow is derived from a
long-term collaboration with a group of machine learning and healthcare
professionals who used our method to make sense of machine learning models they
developed. The case study from this collaboration demonstrates that the
proposed workflow helps experts derive useful knowledge about the model and the
phenomena it describes, thus experts can generate useful hypotheses on how a
model can be improved.

我们提出了一种基于视觉分析的工作流，用于帮助数据科学家和领域专家探索、诊断和理解二元分类器所做决策的过程，这种方法利用 “实例级解释” 来构建一组视觉表示，通过统计聚合数据、解释数据特征和原始数据三种不同的表示形式帮助用户进行研究。我们还通过研究案例验证了该方法的可行性。