Abstract: The need for explainability in AI systems has been identified to be a necessity for acceptance almost since the beginning of AI. Research activity around explainability has been going in waves, following the popularity and trends in AI. Despite the emergence of the new name Explainable AI (XAI), most of the challenges identified for XAI remain the same as before. The talk will give an overview of the history of XAI, the current trends, the main challenges and some guesses on what the future of XAI looks like. There will also be an overview of how the XAI workshop is expected to make the XAI domain progress.

Abstract: It is important that autonomous agents are able to explain their selected course of action. Such explanation can help humans develop an appropriate level of trust in the agent, and can improve the transparency and understanding of the agent, its capabilities, and its limitations. In this talk I review some recent work in the area, focussing on cognitive agents, i.e. agents structure in terms of folk-psychological constructs, such as goals and plans. I pose some questions and challenges, including the issue of bridging between cognitive (symbolic) systems, and non-symbolic systems.