Fiper: a Visual-based Explanation Combining Rules and Feature Importance
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.
Cite
@article{arxiv.2404.16903,
title = {Fiper: a Visual-based Explanation Combining Rules and Feature Importance},
author = {Eleonora Cappuccio and Daniele Fadda and Rosa Lanzilotti and Salvatore Rinzivillo},
journal= {arXiv preprint arXiv:2404.16903},
year = {2024}
}
Comments
15 pages, 4 figures, to be published in ECML PKDD International Workshop on eXplainable Knowledge Discovery in Data Mining