  Artificial Intelligence algorithms have now become pervasive in multiple
high-stakes domains. However, their internal logic can be obscure to humans.
Explainable Artificial Intelligence aims to design tools and techniques to
illustrate the predictions of the so-called black-box algorithms. The
Human-Computer Interaction community has long stressed the need for a more
user-centered approach to Explainable AI. This approach can benefit from
research in user interface, user experience, and visual analytics. This paper
proposes a visual-based method to illustrate rules paired with feature
importance. A user study with 15 participants was conducted comparing our
visual method with the original output of the algorithm and textual
representation to test its effectiveness with users.
