In interactive visual machine learning (IVML), humans and machine learning algorithms collaborate to achieve tasks mediated by interactive visual interfaces. This human-in-the-loop approach to machine learning brings forth not only numerous intelligibility, trust, and usability issues, but also many open questions with respect to the evaluation of the IVML system, both as separate components, and as a holistic entity that includes both human and machine intelligence. This article describes the challenges and research gaps identified in an IEEE VIS workshop on the evaluation of IVML systems.

中文翻译：

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评估交互式视觉机器学习系统的挑战

在交互式视觉机器学习 (IVML) 中，人类和机器学习算法合作完成由交互式视觉界面介导的任务。这种机器学习的人在环方法不仅带来了许多可理解性、信任和可用性问题，而且在评估 IVML 系统方面也带来了许多悬而未决的问题，既作为单独的组件，又作为一个整体实体这包括人类和机器智能。本文描述了 IEEE VIS 研讨会中关于评估 IVML 系统的挑战和研究差距。