Mapping the Challenges and Opportunities of CBR for eXplainable AI

摘要

The problem of explainability in Artificial Intelligence is not new but the rise of the autonomous intelligent systems has increased the necessity to understand how an intelligent system achieves its solution, makes a prediction or a recommendation or reasons to support a decision in order to increase transparency and users' trust in these systems. The CBR research community has a great opportunity to provide general methods of self-understanding and introspection on other AI systems, not necessarily case-based. CBR provides a methodology to reuse experiences in interactive explanations and can exploit memory-based techniques to generate explanations to different AI techniques and domains of applications. This talk will review the state of the art of XCBR, the synergies with the XAI community, and will give the opportunity to review the underlying issues like confidence, transparency, justification, interfaces, personalization and evaluation of explanations. It will include a review of the lessons learnt at the XCBR workshop and the challenges and promising research lines for CBR research related to the explanation of intelligent systems.