no code implementations • 8 Nov 2023 • Roberto Confalonieri, Giancarlo Guizzardi
This paper discusses the different roles that explicit knowledge, in particular ontologies, can play in Explainable AI and in the development of human-centric explainable systems and intelligible explanations.
no code implementations • 30 Oct 2023 • Luca Longo, Mario Brcic, Federico Cabitza, Jaesik Choi, Roberto Confalonieri, Javier Del Ser, Riccardo Guidotti, Yoichi Hayashi, Francisco Herrera, Andreas Holzinger, Richard Jiang, Hassan Khosravi, Freddy Lecue, Gianclaudio Malgieri, Andrés Páez, Wojciech Samek, Johannes Schneider, Timo Speith, Simone Stumpf
As systems based on opaque Artificial Intelligence (AI) continue to flourish in diverse real-world applications, understanding these black box models has become paramount.
Explainable artificial intelligence Explainable Artificial Intelligence (XAI)
no code implementations • 3 Apr 2020 • David F. Nettleton, Dimitrios Katsantonis, Argyris Kalaitzidis, Natasa Sarafijanovic-Djukic, Pau Puigdollers, Roberto Confalonieri
In this study, we compared four models for predicting rice blast disease, two operational process-based models (Yoshino and WARM) and two approaches based on machine learning algorithms (M5Rules and RNN), the former inducing a rule-based model and the latter building a neural network.
no code implementations • 19 Jun 2019 • Roberto Confalonieri, Tillman Weyde, Tarek R. Besold, Fermín Moscoso del Prado Martín
Whilst a plethora of approaches have been developed for post-hoc explainability, only a few focus on how to use domain knowledge, and how this influences the understandability of global explanations from the users' perspective.
no code implementations • 9 Nov 2017 • Nicolas Troquard, Roberto Confalonieri, Pietro Galliani, Rafael Penaloza, Daniele Porello, Oliver Kutz
Ontology engineering is a hard and error-prone task, in which small changes may lead to errors, or even produce an inconsistent ontology.
no code implementations • 6 Mar 2016 • Maximos Kaliakatsos-Papakostas, Roberto Confalonieri, Joseph Corneli, Asterios Zacharakis, Emilios Cambouropoulos
This tool allows a music expert to specify arguments over given transition properties.