Rule sets are often used in Machine Learning (ML) as a way to communicate the model logic in settings where transparency and intelligibility are necessary. Rule sets are typically presented as a text-based list of logical statements (rules). Surprisingly, to date there has been limited work on exploring visual alternatives for presenting rules. In this paper, we explore the idea of designing alternative representations of rules, focusing on a number of visual factors we believe have a positive impact on rule readability and understanding. We then presents a user study exploring their impact. The results show that some design factors have a strong impact on how efficiently readers can process the rules while having minimal impact on accuracy. This work can help practitioners employ more effective solutions when using rules as a communication strategy to understand ML models.

翻译：机器学习(ML)经常使用规则集,作为在需要透明度和智慧的情况下交流模式逻辑的一种方式,规则集通常作为逻辑说明(规则)的文本清单(规则)提出,令人惊讶的是,迄今为止,探索规则展示的视觉替代方法的工作有限,在本文件中,我们探讨了设计规则的替代表述方法的想法,侧重于我们认为对规则可读性和理解性有积极影响的一些视觉因素。然后,我们提出用户研究,探讨规则的可读性和理解性。结果显示,一些设计因素对读者处理规则的效率有重大影响,而对准确性影响最小。这项工作有助于实践者在使用规则作为理解ML模式的沟通战略时采用更有效的解决方案。