  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. The paper presents an initial design space for visualizing rule
sets and 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.
