<p>Interpretable machine learning is an increasingly popular field as machine learning grows in prevalence and power. Though the fuzzy (rule-based) modeling field has long considered interpretability to be a great strength of its models, fuzzy rule-based models are often missing in the mainstream discussion about interpretable machine learning. The original contribution of this work is to uncover the reasons for this absence, and to propose actionable steps so that the fuzzy modeling field may join this mainstream discussion. To do so, we perform a narrative review by analyzing a select number of the most highly cited and broad papers in the interpretable machine learning and fuzzy modeling fields through three lenses of interpretability: scale, audience, and stage dependence. We identify several gaps in the fuzzy modeling interpretability discussion and give recommendations for future work in each lens. Based on the lenses of interpretability, we provide a simple manner in which to analyze the interpretability of a model that should be used in future works when discussing the interpretability of a model. Building interpretable models that humans can understand is an important task, and this analysis will help bring another beneficial voice to the mainstream conversation.</p>