Visualizing Rule Sets: Exploration and Validation of a Design Space
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 9 of its 31 references that are in the map. First posted on arXiv 1 Mar 2021.
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References · 9
- 2018A Survey Of Methods For Explaining Black Box Modelscited by 970
- 2017Learning Explanatory Rules from Noisy Datacited by 210
- 2018Local Rule-Based Explanations of Black Box Decision Systemscited by 165
- 2015Interpretable classifiers using rules and Bayesian analysis: Building a better stroke prediction modelcited by 140
- 2019An Evaluation of the Human-Interpretability of Explanationcited by 71
- 2016Scalable Bayesian Rule Listscited by 64
- 2018RuleMatrix: Visualizing and Understanding Classifiers with Rulescited by 62
- 2014Falling Rule Listscited by 58
- 2020Explainable Matrix -- Visualization for Global and Local Interpretability of Random Forest Classification Ensemblescited by 23
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