Human and Machine Learning

Visible, Explainable, Trustworthy and Transparent

  • Jianlong Zhou
  • Fang Chen

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
  6. Human and Evaluation of Machine Learning

    1. Front Matter
      Pages 313-313
    2. Scott Allen Cambo, Darren Gergle
      Pages 315-339
    3. Nadia Boukhelifa, Anastasia Bezerianos, Evelyne Lutton
      Pages 341-360
  7. Domain Knowledge in Transparent Machine Learning Applications

    1. Front Matter
      Pages 361-361
    2. Bang Zhang, Ting Guo, Lelin Zhang, Peng Lin, Yang Wang, Jianlong Zhou et al.
      Pages 363-383
    3. Nguyen Lu Dang Khoa, Mehrisadat Makki Alamdari, Thierry Rakotoarivelo, Ali Anaissi, Yang Wang
      Pages 409-435
    4. Alberto Tonda, Nadia Boukhelifa, Thomas Chabin, Marc Barnabé, Benoît Génot, Evelyne Lutton et al.
      Pages 459-477
  8. Back Matter
    Pages 479-482

About this book


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.


Machine Learning Human Factors Visualization Explanation Transparency

Editors and affiliations

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

Bibliographic information

  • DOI
  • Copyright Information Springer International Publishing AG, part of Springer Nature 2018
  • Publisher Name Springer, Cham
  • eBook Packages Computer Science Computer Science (R0)
  • Print ISBN 978-3-319-90402-3
  • Online ISBN 978-3-319-90403-0
  • Series Print ISSN 1571-5035
  • Series Online ISSN 2524-4477
  • Buy this book on publisher's site