-- Explainable AI (XAI) has recently emerged proposing a set of techniques attempting to explain machine learning (ML) models. The recipients (explainee) are intended to be humans or other intelligent virtual entities. Transparency, trust, and debuging are the underlying features calling for XAI. However, in real-world settings, systems are distributed, data are heterogeneous, the “system” knowledge is bounded, and privacy concerns are subject to variable constraints. Current XAI approaches cannot cope with such requirements.
HES-SO
University of Applied Sciences and Arts Western Switzerland
Find out more! UNIBO
Alma Mater Studiorum Università di Bologna
Find out more! UNILU
University of Luxembourg
Find out more! OZU
Özyeğin University
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The team has multidisciplinary competences sharing the Multi-Agent Systems as common thread.
HES-SO People (from Switzerland) Prof. Michael I. Schumacher
Full Professor at HES-SO
Personal Homepage Dr. Davide Calvaresi
Senior researcher at HES-SO
Personal Homepage Dr. Jean-Paul Calbimonte
Senior researcher at HES-SO
Personal Homepage Victor Hugo Contreras Ordonez
PhD Student at HES-SO
by Davide Calvaresi, Giovanni Ciatto, Amro Najjar, Reyhan Aydoğan, Leon Van der Torre, Andrea Omicini, and Michael I. Schumacher
Abstract Explainable AI (XAI) has emerged in recent years as a set of techniques and methodologies to interpret and explain machine learning (ML) predictors. To date, many initiatives have been proposed. Nevertheless, current research efforts mainly focus on methods tailored to specific ML tasks and algorithms, such as image classification and sentiment analysis.
EXPECTATION