Machine learning (ML) tools with graphical user interfaces (GUI) are facing demand from novice users who do not have the background of their underlying concepts. These tools are frequently complex and pose unique challenges in terms of interaction and comprehension by novice users. There is yet to be an established set of usability heuristics to guide and assess GUI ML tool design. To address this gap, in this paper, we extend Nielsen's heuristics for evaluating GUI ML Tools through a set of empirical evaluations. To validate the proposed heuristics, user testing was conducted by novice users on a prototype that reflects those heuristics. Based on the results of the evaluations, our new heuristics set improves upon existing heuristics in the context of ML tools. It can serve as a resource for practitioners designing and evaluating these tools.

翻译：机器学习（ML）图形用户界面（GUI）工具正面临来自缺乏相关概念背景的新手用户的需求。这些工具通常复杂，并在新手用户的交互和理解方面带来独特挑战。目前尚缺乏一套成熟的可用性启发式方法来指导和评估GUI ML工具的设计。为弥补这一空白，本文通过一系列实证评估对Nielsen启发式方法进行了扩展，用于评估GUI ML工具。为验证所提出的启发式方法，我们基于反映这些启发式方法的原型对新手用户进行了用户测试。评估结果表明，在机器学习工具场景下，我们的新启发式方法集改进了现有启发式方法，可为设计和评估此类工具的实践者提供参考资源。