Fiper: a Visual-based Explanation Combining Rules and Feature Importance
<|reference_start|>Fiper: a Visual-based Explanation Combining Rules and Feature Importance: 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.<|reference_end|>
@article{cappuccio2024fiper:,
    title={Fiper: a Visual-based Explanation Combining Rules and Feature Importance},
    author={Eleonora Cappuccio, Daniele Fadda, Rosa Lanzilotti, Salvatore
  Rinzivillo},
    journal={arXiv preprint arXiv:2404.16903},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.16903},
    primaryClass={cs.HC cs.AI}
}