Fiper: a Visual-based Explanation Combining Rules and Feature Importance
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866917651253559296 |
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| author | Cappuccio, Eleonora Fadda, Daniele Lanzilotti, Rosa Rinzivillo, Salvatore |
| author_facet | Cappuccio, Eleonora Fadda, Daniele Lanzilotti, Rosa Rinzivillo, Salvatore |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_16903 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Fiper: a Visual-based Explanation Combining Rules and Feature Importance Cappuccio, Eleonora Fadda, Daniele Lanzilotti, Rosa Rinzivillo, Salvatore Human-Computer Interaction Artificial Intelligence I.2.0 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. |
| title | Fiper: a Visual-based Explanation Combining Rules and Feature Importance |
| topic | Human-Computer Interaction Artificial Intelligence I.2.0 |
| url | https://arxiv.org/abs/2404.16903 |