Programme Committee Derek Bridge, University College Cork, Ireland Stelios Kapetanakis, University of Brighton, UK David Leake, Indiana University, USA Santiago Ontan, Drexel University, USA Antonio A. Snchez Ruiz-Granados, UCM, Spain Barry Smyth, University College Dublin, Ireland David W. Aha Naval Research Laboratory, USA
Preface
The problem of explainability in Artificial Intelligence is not new but the rise of autonomous intelligent systems has created the necessity to understand how these intelligent systems achieve a solution, make a prediction or a recommendation or reason to support a decision in order to increase users reliability in these systems. The goal of Explainable Artificial Intelligence (XAI) is to create a suite of new or modified AI techniques that produce explainable models that, when combined with e↵ ective explanation techniques, enable end users to understand, appropriately trust, and e↵ ectively manage the emerging generation of Artificial Intelligence systems. Case-Based Reasoning (CBR) systems have previous experiences in interactive explanations and in exploiting memory-based techniques to generate these explanations that can be successfully applied to the explanation of other AI techniques.
The workshop program includes an invited talk by David Aha introducing what’s happening in XAI today. Then we have organized 2 sessions with 5 presentations and one discussion panel. This year most of the contributions are oriented to the practical experience of CBR for explanation in recommender systems including issues like explanation interfaces, evaluation and applications. Besides, we have a contribution summarizing the recent trends in XAI, providing an overview on current approaches, methodologies and interactions.
We wish to thank all who contributed to the success of this workshop, especially the authors, the Program Committee, and the editors of the workshop proceedings!