Explainable AI (xAI) is not a new buzzword, but it is a new name for a very old field in science: to help to provide answers to questions of why. In engineering we follow a human-centered AI approach towards causality research and integrate ethical, legal, psychological and sociological issues for the design of interpretable algorithms. The goal is to enable human experts to understand the underlying explanatory factors (causality) of why an AI-decision has been made, paving the way for ethical responsible AI and transparent verifiable machine learning for decision support.
Following on from our successful workshops in recent years, we are organising the next xAI-Workshop again at our IFIP CD-MAKE conference in Dublin, August 24-28, 2020. We again publish a Springer/Nature Volume Lecture Notes in Computer Science, papers are due to March, 29, 2020: