Artificial Intelligence (AI) systems are increasingly used to make consequential decisions in a wide range of domains, including banking, healthcare, transportation and beyond. The need to understand AI to improve, contest, develop appropriate trust, and better interact with AI systems has spurred great academic and public interest in Explainable AI (XAI). The technical field of XAI has produced a vast collection of algorithms in recent years. As a result, several methods and tools have been developed to explain the decisions of these systems, with the aim to deliver XAI solutions suitable for a human-centred world. Purpose of this school is to let PhD students and young researchers come in touch with recent, challenging, scientific problems related to innovative applications in XAI, with an introduction to suitable mathematical and computational methods.  More specifically, we intend to address the following topics:

– Review of the main XAI methods;
– Analytical and geometrical methods for the Robustness in AI;
– Computational aspects, also from the Green computing point of view, of XAI;
– Neuroscience, control and Neuro symbolic AI;
– XAI and decision in medicine and bioscience.

PAST EDITIONS 

• SMILE  Sustainable Medical Imaging with Learning and Regularization.
  28 August – 1 September 2023

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