标题： 登山者：用于比较局部解释的拓扑驱动可视化分析 显示英文标题

标题： MOUNTAINEER: Topology-Driven Visual Analytics for Comparing Local Explanations

摘要： 随着黑盒机器学习（ML）技术在关键应用中的日益普及，对于能够为模型预测提供透明度和问责制的方法的需求也在不断增加。 因此，已经开发并推广了许多针对黑盒模型的局部可解释性方法。 然而，由于这些方法的高维性、异构表示、不同尺度以及随机性质，机器学习解释仍然难以评估和比较。 拓扑数据分析（TDA）在此领域可能是一种有效的方法，因为它可以将属性转换为统一的图表示，为不同解释方法之间的比较提供一个共同的基础。 我们提出了一种新颖的拓扑驱动的可视化分析工具Mountaineer，它允许ML从业者通过将拓扑图与原始数据分布、模型预测和特征属性联系起来，以交互方式分析和比较这些表示。 Mountaineer促进了ML解释的快速和迭代探索，使专家能够更深入地了解解释技术，理解底层的数据分布，从而对模型行为得出有根据的结论。 此外，我们通过两个使用真实世界数据的案例研究展示了Mountaineer的实用性。 在第一个案例中，我们展示了Mountaineer如何帮助我们比较黑盒ML解释，并识别不同解释之间分歧的区域和原因。 在第二个案例中，我们演示了该工具如何用于比较和理解ML模型本身。 最后，我们采访了三位行业专家，以帮助我们评估我们的工作。 显示英文摘要

摘要： With the increasing use of black-box Machine Learning (ML) techniques in critical applications, there is a growing demand for methods that can provide transparency and accountability for model predictions. As a result, a large number of local explainability methods for black-box models have been developed and popularized. However, machine learning explanations are still hard to evaluate and compare due to the high dimensionality, heterogeneous representations, varying scales, and stochastic nature of some of these methods. Topological Data Analysis (TDA) can be an effective method in this domain since it can be used to transform attributions into uniform graph representations, providing a common ground for comparison across different explanation methods. We present a novel topology-driven visual analytics tool, Mountaineer, that allows ML practitioners to interactively analyze and compare these representations by linking the topological graphs back to the original data distribution, model predictions, and feature attributions. Mountaineer facilitates rapid and iterative exploration of ML explanations, enabling experts to gain deeper insights into the explanation techniques, understand the underlying data distributions, and thus reach well-founded conclusions about model behavior. Furthermore, we demonstrate the utility of Mountaineer through two case studies using real-world data. In the first, we show how Mountaineer enabled us to compare black-box ML explanations and discern regions of and causes of disagreements between different explanations. In the second, we demonstrate how the tool can be used to compare and understand ML models themselves. Finally, we conducted interviews with three industry experts to help us evaluate our work.