Explainable AI – Explanations in AI
One important challenge in machine learning is the “black box” problem, in which an
artificial intelligence reaches a result without any humans being able to explain why.
This problem is typically present in deep artificial neural networks, in which the hidden
layers are impenetrable. To tackle this problem, researchers have introduced the notion
of explainable AI (XAI). In this workshop we discuss a variety of approaches to this topic in connection to fundamental questions in artificial intelligence. What are explanations in AI? What do AI systems explain and how? How does AI explanation relate to the topics of human understanding and intelligence?
For information on the program and participating, please see the links below.