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© 2018

Human and Machine Learning

Visible, Explainable, Trustworthy and Transparent

  • Jianlong Zhou
  • Fang Chen
Textbook

Part of the Human–Computer Interaction Series book series (HCIS)

Table of contents

  1. Front Matter
    Pages i-xxiii
  2. Transparency in Machine Learning

  3. Visual Explanation of Machine Learning Process

    1. Front Matter
      Pages 91-91
    2. Mohammed Brahimi, Marko Arsenovic, Sohaib Laraba, Srdjan Sladojevic, Kamel Boukhalfa, Abdelouhab Moussaoui
      Pages 93-117
    3. Kieran Browne, Ben Swift, Henry Gardner
      Pages 119-136
  4. Algorithmic Explanation of Machine Learning Models

  5. User Cognitive Responses in ML-Based Decision Making

    1. Front Matter
      Pages 223-223
    2. Jianlong Zhou, Kun Yu, Fang Chen
      Pages 225-244
    3. Kun Yu, Shlomo Berkovsky, Dan Conway, Ronnie Taib, Jianlong Zhou, Fang Chen
      Pages 245-264
    4. Joseph Lyons, Nhut Ho, Jeremy Friedman, Gene Alarcon, Svyatoslav Guznov
      Pages 265-278
    5. Janin Koch, Antti Oulasvirta
      Pages 293-312

About this book

Introduction

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-box” in ML methods, ML still needs to be interpreted to link human and machine learning for transparency and user acceptance of delivered solutions. This edited book addresses such links from the perspectives of visualisation, explanation, trustworthiness and transparency. The book establishes the link between human and machine learning by exploring transparency in machine learning, visual explanation of ML processes, algorithmic explanation of ML models, human cognitive responses in ML-based decision making, human evaluation of machine learning and domain knowledge in transparent ML applications.

This is the first book of its kind to systematically understand the current active research activities and outcomes related to human and machine learning. The book will not only inspire researchers to passionately develop new algorithms incorporating human for human-centred ML algorithms, resulting in the overall advancement of ML, but also help ML practitioners proactively use ML outputs for informative and trustworthy decision making.

This book is intended for researchers and practitioners involved with machine learning and its applications. The book will especially benefit researchers in areas like artificial intelligence, decision support systems and human-computer interaction.

Keywords

Machine Learning Human Factors Visualization Explanation Transparency

Editors and affiliations

  • Jianlong Zhou
    • 1
  • Fang Chen
    • 2
  1. 1.DATA61CSIROEveleighAustralia
  2. 2.DATA61CSIROEveleighAustralia

About the editors

Dr. Jianlong Zhou’s research interests include interactive behaviour analytics, human-computer interaction, machine learning, and visual analytics. He has extensive experience in data driven multimodal cognitive load and trust measurement in predictive decision making. He leads interdisciplinary research on applying visualization and human behaviour analytics in trustworthy and transparent machine learning. He also works with industries in advanced data analytics for transforming data into actionable operations, particularly by incorporating human user aspects into machine learning to translate machine learning into impacts in real world applications.

Dr. Fang Chen works in the field of behaviour analytics and machine learning in data driven business solutions. She pioneered the theoretical framework of multimodal cognitive load measurement, and provided much of the empirical evidence on using human behaviour signals and physiological responses to measure and monitor cognitive load. She also leads many taskforces in applying advanced data analytic techniques to help industries make use of data, leading to improved productivity and innovation through business intelligence. Her extensive experience on cognition and machine learning applications across different industries brings unique insights on bridging the gap of machine learning and its impact.


Bibliographic information

  • Book Title Human and Machine Learning
  • Book Subtitle Visible, Explainable, Trustworthy and Transparent
  • Editors Jianlong Zhou
    Fang Chen
  • Series Title Human–Computer Interaction Series
  • Series Abbreviated Title Human–Computer Interaction Series
  • DOI https://doi.org/10.1007/978-3-319-90403-0
  • Copyright Information Springer International Publishing AG, part of Springer Nature 2018
  • Publisher Name Springer, Cham
  • eBook Packages Computer Science Computer Science (R0)
  • Hardcover ISBN 978-3-319-90402-3
  • Softcover ISBN 978-3-030-08007-5
  • eBook ISBN 978-3-319-90403-0
  • Series ISSN 1571-5035
  • Series E-ISSN 2524-4477
  • Edition Number 1
  • Number of Pages XXIII, 482
  • Number of Illustrations 26 b/w illustrations, 114 illustrations in colour
  • Topics User Interfaces and Human Computer Interaction
    Artificial Intelligence
    Pattern Recognition
  • Buy this book on publisher's site
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