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Modern artificial intelligence (AI) can be used to drive cars, to decide on loans, or to detect cancer. Yet, the inner workings of many modern AI systems remain hidden – even to experts. Given the increasingly important role that AI systems play in our society, this seems problematic at the very least. In response to this problem, calls for making AI systems explainable are becoming louder – especially among users and regulators of modern AI. In a nutshell, explainable AI (or XAI) is about revealing the inner processes of an AI system such that they become understandable to different recipients. With this general focus, research on explainability is very diverse, extends into many different directions, and spreads across various disciplines including psychology, philosophy, HCI, computer science, law and many more. In this course, we will look into recent research on various topics surrounding XAI from philosophy and beyond.

The course will be run in hybrid. It will start with an introductory session, followed by a mixture of joint sessions with researchers in the field and sessions in which we prepare background knowledge. Everyone is welcome, even without prior knowledge of the subject. However, willingness to engage with state-of-the-art research articles from different disciplines is presupposed.

Please join the MS Teams Team for this course with the following code: yww93s7.