Back to the Feature: A Neural-Symbolic Perspective on Explainable AI
We discuss a perspective aimed at making black box models more eXplainable, within the eXplainable AI (XAI) strand of research. We argue that the traditional end-to-end learning approach used to train Deep Learning (DL) models does not fit the tenets and
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Andreas Holzinger · Peter Kieseberg · A Min Tjoa · Edgar Weippl (Eds.)
Machine Learning and Knowledge Extraction 4th IFIP TC 5, TC 12, WG 8.4, WG 8.9, WG 12.9 International Cross-Domain Conference, CD-MAKE 2020 Dublin, Ireland, August 25–28, 2020, Proceedings
Lecture Notes in Computer Science Founding Editors Gerhard Goos Karlsruhe Institute of Technology, Karlsruhe, Germany Juris Hartmanis Cornell University, Ithaca, NY, USA
Editorial Board Members Elisa Bertino Purdue University, West Lafayette, IN, USA Wen Gao Peking University, Beijing, China Bernhard Steffen TU Dortmund University, Dortmund, Germany Gerhard Woeginger RWTH Aachen, Aachen, Germany Moti Yung Columbia University, New York, NY, USA
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More information about this series at http://www.springer.com/series/7409
Andreas Holzinger Peter Kieseberg A Min Tjoa Edgar Weippl (Eds.) •
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Machine Learning and Knowledge Extraction 4th IFIP TC 5, TC 12, WG 8.4, WG 8.9, WG 12.9 International Cross-Domain Conference, CD-MAKE 2020 Dublin, Ireland, August 25–28, 2020 Proceedings
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Editors Andreas Holzinger Human-Centered AI Lab, Institute for Medical Informatics, Statistics and Doumentation Medical University Graz Graz, Austria xAI Lab, Alberta Machine Intelligence Institute University of Alberta Edmonton, AB, Canada
Peter Kieseberg UAS St. Pölten St. Pölten, Austria Edgar Weippl SBA Research Vienna, Austria Research Group Security and Privacy University of Vienna Vienna, Austria
A Min Tjoa Institute of Software Technology and Interactive Systems Technical University of Vienna Vienna, Austria
ISSN 0302-9743 ISSN 1611-3349 (electronic) Lecture Notes in Computer Science ISBN 978-3-030-57320-1 ISBN 978-3-030-57321-8 (eBook) https://doi.org/10.1007/978-3-030-57321-8 LNCS Sublibrary: SL3 – Information Systems and Applications, incl. Internet/Web, and HCI © IFIP International Federation for Information Processing 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
