1. Fiper: a Visual-based Explanation Combining Rules and Feature Importance
- Author
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Cappuccio, Eleonora, Fadda, Daniele, Lanzilotti, Rosa, and Rinzivillo, Salvatore
- Subjects
Computer Science - Human-Computer Interaction ,Computer Science - Artificial Intelligence ,I.2.0 - Abstract
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., Comment: 15 pages, 4 figures, to be published in ECML PKDD International Workshop on eXplainable Knowledge Discovery in Data Mining
- Published
- 2024