Good Counterfactuals and Where to Find Them: A Case-Based Technique for Generating Counterfactuals for Explainable AI (X
Recently, a groundswell of research has identified the use of counterfactual explanations as a potentially significant solution to the Explainable AI (XAI) problem. It is argued that (i) technically, these counterfactual cases can be generated by permutin
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Ian Watson Rosina Weber (Eds.)
Case-Based Reasoning Research and Development 28th International Conference, ICCBR 2020 Salamanca, Spain, June 8–12, 2020 Proceedings
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Lecture Notes in Artificial Intelligence Subseries of Lecture Notes in Computer Science
Series Editors Randy Goebel University of Alberta, Edmonton, Canada Yuzuru Tanaka Hokkaido University, Sapporo, Japan Wolfgang Wahlster DFKI and Saarland University, Saarbrücken, Germany
Founding Editor Jörg Siekmann DFKI and Saarland University, Saarbrücken, Germany
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More information about this series at http://www.springer.com/series/1244
Ian Watson Rosina Weber (Eds.) •
Case-Based Reasoning Research and Development 28th International Conference, ICCBR 2020 Salamanca, Spain, June 8–12, 2020 Proceedings
123
Editors Ian Watson School of Computer Science University of Auckland Auckland, New Zealand
Rosina Weber College of Computing and Informatics Drexel University Philadelphia, PA, USA
ISSN 0302-9743 ISSN 1611-3349 (electronic) Lecture Notes in Artificial Intelligence ISBN 978-3-030-58341-5 ISBN 978-3-030-58342-2 (eBook) https://doi.org/10.1007/978-3-030-58342-2 LNCS Sublibrary: SL7 – Artificial Intelligence © Springer Nature Switzerland AG 2020 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland
Preface
This volume contains the papers presented at the 28th International Conference on Case-Based Reasoning (ICCBR 2020), which was held June 8–12, 2020. ICCBR is the premier annual meeting of the Case-Based Reasoning (CBR) research community. The theme of ICCBR 2020 was “CBR Across Bridges,” aiming to help guide future developments in CBR by encouraging members from within and outside the CBR community to discuss new ideas. Previo