تقرير
Fiper: a Visual-based Explanation Combining Rules and Feature Importance
| العنوان: | Fiper: a Visual-based Explanation Combining Rules and Feature Importance |
|---|---|
| المؤلفون: | Cappuccio, Eleonora, Fadda, Daniele, Lanzilotti, Rosa, Rinzivillo, Salvatore |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Human-Computer Interaction, Computer Science - Artificial Intelligence, I.2.0 |
| الوصف: | 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 |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2404.16903 |
| رقم الأكسشن: | edsarx.2404.16903 |
| قاعدة البيانات: | arXiv |
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