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In the future people will collaborate more and more with machines to solve complex problems using AI techniques. Such a collaboration requires adequate communication, trust, clarity and understanding. eXplainable AI (XAI) aims at addressing such challenges by combining the best of symbolic AI and Machine Learning including neural models, evolutionary computing and fuzzy systems. Such topic has been studied for years by all different communities of AI, with different definitions, evaluation metrics, motivations and results. In addition to technology, this involves social and legal issues as well as a wide range of real-world applications and domains. Both interpretability by design methods and post-hoc methods for explaining complex models have been proposed and investigated. Research has also redirected its emphasis on the structure of explanations and human-centered Artificial Intelligence, recognizing that the ultimate users of interactive technologies are humans.

Conference Areas

1 . Technology

2 . Social and Legal Issues

3 . Applications

Conference Chair

Francesco Marcelloni, University of Pisa, Italy

PROGRAM CO-CHAIRS

Kurosh Madani, University of Paris-EST Créteil (UPEC), France
Niki van Stein, Leiden University, Netherlands

Keynote Speakers

Lars Kotthoff, University of St Andrews, United Kingdom
Hervé Panetto, University of Lorraine, France
Gabriella Casalino, Universita degli Studi di Bari, Italy