Human and Machine Learning Visible, Explainable, Trustworthy and Tra
With an evolutionary advancement of Machine Learning (ML) algorithms, a rapid increase of data volumes and a significant improvement of computation powers, machine learning becomes hot in different applications. However, because of the nature of “black-bo
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Jianlong Zhou · Fang Chen Editors
Human and Machine Learning Visible, Explainable, Trustworthy and Transparent
Human–Computer Interaction Series Editors-in-Chief Desney Tan Microsoft Research, Redmond, USA Jean Vanderdonckt Université catholique de Louvain, Louvain-La-Neuve, Belgium
More information about this series at http://www.springer.com/series/6033
Jianlong Zhou Fang Chen •
Editors
Human and Machine Learning Visible, Explainable, Trustworthy and Transparent
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Editors Jianlong Zhou DATA61 CSIRO Eveleigh, NSW, Australia
Fang Chen DATA61 CSIRO Eveleigh, NSW, Australia
ISSN 1571-5035 ISSN 2524-4477 (electronic) Human–Computer Interaction Series ISBN 978-3-319-90402-3 ISBN 978-3-319-90403-0 (eBook) https://doi.org/10.1007/978-3-319-90403-0 Library of Congress Control Number: 2018940655 © Springer International Publishing AG, part of Springer Nature 2018 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, express 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. Printed on acid-free paper This Springer imprint is published by the registered company Springer International Publishing AG part of Springer Nature The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland
Foreword
Machine learning has transitioned from an arcane and obscure research area to one of the hottest technologies around. Like other new and general purpose technologies, this rise to prominence brings social issues to the forefront. When electricity was the plaything of eccentric hobbyists, it was of little concern to most people. When it became infrastructure, many more took an interest because it directly started affecting their lives. Machine learning is now affecting everyone’s lives, and reasonably, higher demands are being made about the technology as a consequence. Thus, the book you hold in your hand is timely and important. Machine learning is arcane. It makes use of sophisticated mathematics and unimaginably com
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