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  • © 2018

Human and Machine Learning

Visible, Explainable, Trustworthy and Transparent

Editors:

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  • Creates a systematic view of relations between human and machine learning from the perspectives of visualisation, explanation, trustworthiness and transparency

  • Explores human aspects in machine learning based on algorithms, human cognitive responses, human evaluation, domain knowledge and real-world applications

  • Provides the first dedicated source of the state-of-the-art advances in theories, techniques and applications of trustworthy and transparent machine learning

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

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  • ISBN: 978-3-319-90403-0
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Table of contents (22 chapters)

  1. Front Matter

    Pages i-xxiii
  2. Transparency in Machine Learning

    1. Front Matter

      Pages 1-1
    2. Transparency Communication for Machine Learning in Human-Automation Interaction

      • David V. Pynadath, Michael J. Barnes, Ning Wang, Jessie Y. C. Chen
      Pages 75-90
  3. Visual Explanation of Machine Learning Process

    1. Front Matter

      Pages 91-91
    2. Deep Learning for Plant Diseases: Detection and Saliency Map Visualisation

      • Mohammed Brahimi, Marko Arsenovic, Sohaib Laraba, Srdjan Sladojevic, Kamel Boukhalfa, Abdelouhab Moussaoui
      Pages 93-117
    3. Critical Challenges for the Visual Representation of Deep Neural Networks

      • Kieran Browne, Ben Swift, Henry Gardner
      Pages 119-136
  4. Algorithmic Explanation of Machine Learning Models

    1. Front Matter

      Pages 137-137
    2. Perturbation-Based Explanations of Prediction Models

      • Marko Robnik-Šikonja, Marko Bohanec
      Pages 159-175
  5. User Cognitive Responses in ML-Based Decision Making

    1. Front Matter

      Pages 223-223
    2. Revealing User Confidence in Machine Learning-Based Decision Making

      • Jianlong Zhou, Kun Yu, Fang Chen
      Pages 225-244
    3. Do I Trust a Machine? Differences in User Trust Based on System Performance

      • Kun Yu, Shlomo Berkovsky, Dan Conway, Ronnie Taib, Jianlong Zhou, Fang Chen
      Pages 245-264
    4. Trust of Learning Systems: Considerations for Code, Algorithms, and Affordances for Learning

      • Joseph Lyons, Nhut Ho, Jeremy Friedman, Gene Alarcon, Svyatoslav Guznov
      Pages 265-278
    5. Group Cognition and Collaborative AI

      • Janin Koch, Antti Oulasvirta
      Pages 293-312

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.

Keywords

  • Machine Learning
  • Human Factors
  • Visualization
  • Explanation
  • Transparency

Editors and Affiliations

  • DATA61, CSIRO, Eveleigh, Australia

    Jianlong Zhou, Fang Chen

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

Buying options

eBook
USD 59.99
Price excludes VAT (USA)
  • ISBN: 978-3-319-90403-0
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD 79.99
Price excludes VAT (USA)
Hardcover Book
USD 119.99
Price excludes VAT (USA)