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Human–Computer Interaction Series

Human and Machine Learning

Visible, Explainable, Trustworthy and Transparent

Editors: Zhou, Jianlong, Chen, Fang (Eds.)

  • 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
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Buy this book

eBook 71,39 €
price for Spain (gross)
  • ISBN 978-3-319-90403-0
  • Digitally watermarked, DRM-free
  • Included format: EPUB, PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover 88,39 €
price for Spain (gross)
  • ISBN 978-3-319-90402-3
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
  • The final prices may differ from the prices shown due to specifics of VAT rules
About this Textbook

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.


About the authors

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.


Table of contents (22 chapters)

  • 2D Transparency Space—Bring Domain Users and Machine Learning Experts Together

    Zhou, Jianlong (et al.)

    Pages 3-19

    Preview Buy Chapter 30,19 €
  • Transparency in Fair Machine Learning: the Case of Explainable Recommender Systems

    Abdollahi, Behnoush (et al.)

    Pages 21-35

    Preview Buy Chapter 30,19 €
  • Beyond Human-in-the-Loop: Empowering End-Users with Transparent Machine Learning

    Shih, Patrick C.

    Pages 37-54

    Preview Buy Chapter 30,19 €
  • Effective Design in Human and Machine Learning: A Cognitive Perspective

    Zheng, Robert (et al.)

    Pages 55-74

    Preview Buy Chapter 30,19 €
  • Transparency Communication for Machine Learning in Human-Automation Interaction

    Pynadath, David V. (et al.)

    Pages 75-90

    Preview Buy Chapter 30,19 €

Buy this book

eBook 71,39 €
price for Spain (gross)
  • ISBN 978-3-319-90403-0
  • Digitally watermarked, DRM-free
  • Included format: EPUB, PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover 88,39 €
price for Spain (gross)
  • ISBN 978-3-319-90402-3
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
  • The final prices may differ from the prices shown due to specifics of VAT rules
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Bibliographic Information

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
Copyright
2018
Publisher
Springer International Publishing
Copyright Holder
Springer International Publishing AG, part of Springer Nature
eBook ISBN
978-3-319-90403-0
DOI
10.1007/978-3-319-90403-0
Hardcover ISBN
978-3-319-90402-3
Series ISSN
1571-5035
Edition Number
1
Number of Pages
XXIII, 482
Number of Illustrations
26 b/w illustrations, 114 illustrations in colour
Topics