Going beyond Visualization. Verbalization as Complementary Medium to Explain Machine Learning Models-猫眼课题宝

DOI：

发表时间：

2018-09

期刊：

影响因子：

通讯作者：

中科院分区：

其他

文献类型：

作者：

R. Sevastjanova;F. Becker;Basil Ell;C. Turkay;R. Henkin;Miriam Butt;D. Keim;E. Mennatallah

在这份立场文件中，我们认为可视化和语言化技术的结合有利于为机器学习模型的结构和决策过程提供广泛而通用的见解。机器学习模型的可解释性正在成为一个重要的研究领域。因此，深入了解训练模型的内部工作原理可以让用户和分析师理解模型，开发合理性，并获得他们所告知的系统的信任。解释可以通过不同类型的媒体生成，例如可视化和语言化。两者都是支持模型可解释性的强大工具。然而，虽然它们的组合可以说比单独使用每种媒体更强大，但它们目前都是独立应用和研究的。为了支持我们的立场，即这两种技术的组合有利于解释机器学习模型，我们描述了这种组合的设计空间，并讨论了出现的研究问题、差距和机会。

In this position paper, we argue that a combination of visualization and verbalization techniques is beneficial for creating broad and versatile insights into the structure and decision-making processes of machine learning models. Explainability of machine learning models is emerging as an important area of research. Hence, insights into the inner workings of a trained model allow users and analysts, alike, to understand the models, develop justifications, and gain trust in the systems they inform. Explanations can be generated through different types of media, such as visualization and verbalization. Both are powerful tools that enable model interpretability. However, while their combination is arguably more powerful than each medium separately, they are currently applied and researched independently. To support our position that the combination of the two techniques is beneficial to explain machine learning models, we describe the design space of such a combination and discuss arising research questions, gaps, and opportunities.

Going beyond Visualization. Verbalization as Complementary Medium to Explain Machine Learning Models

文献摘要

相似文献