Abstract
The field of eXplainable Artificial Intelligence (XAI) emerged in response to the growing need for more transparent and reliable models. However, using raw features to provide explanations has been discussed in several works lately, advocating for more user-understandable explanations. To address this issue, a wide range of papers proposed Concept-based XAI (C-XAI) methods have been published in recent years. Nevertheless, a unified categorization and precise field definition are still missing. In this talk, we will try to fill the gap through a review of C-XAI approaches. We will identify and define what is a concept and a concept-based explanation. We will then provide guidelines for selecting a suitable category based on the application context. Additionally, we will analyse a few prominent supervised concept-based models and their application in the field of voice disorder analysis.
- Gabriele Ciravegna
Intesa Sanpaolo Innovation Center's Artificial Intelligence LabGabriele Ciravegna is a Researcher at the Intesa Sanpaolo Innovation Center’s Artificial Intelligence Lab, where his work focuses on advancing the explainability, robustness, and efficiency of Deep Neural Networks. He is particularly recognized for his contributions to Concept-based Explainable AI (XAI), with applications across Computer Vision, Natural Language Processing, and Healthcare. An active member of the research community since 2019, Gabriele frequently publishes in and reviews for top-tier venues, including NeurIPS, ICML, AAAI, IJCAI, and IEEE TPAMI. He earned his Ph.D. under the guidance of Prof. Marco Gori, receiving the IEEE Caianiello Award and the Città di Firenze Award for the Best Ph.D. Thesis in 2023. In addition to his research, he co-lectures Machine Learning courses at Université Côte d’Azur and Politecnico di Torino, where he also completed two postdoctoral fellowships.