Human and Machine Learning : Visible, Explainable, Trustworthy and Transparent / edited by Jianlong Zhou, Fang Chen.

Format:

Book

Contributor:

Zhou, Jianlong, editor.

Chen, Fang, editor.

SpringerLink (Online service)

Series:

Computer Science (Springer-11645)

Human-computer interaction series 1571-5035

Human-Computer Interaction Series, 1571-5035

Language:

English

Subjects (All):

User interfaces (Computer systems).

Artificial intelligence.

Pattern perception.

User Interfaces and Human Computer Interaction.

Artificial Intelligence.

Pattern Recognition.

Local Subjects:

User Interfaces and Human Computer Interaction.

Artificial Intelligence.

Pattern Recognition.

Physical Description:

1 online resource (XXIII, 482 pages) : 140 illustrations, 114 illustrations in color.

Edition:

First edition 2018.

Contained In:

Springer eBooks

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2018.

System Details:

text file PDF

Summary:

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.

Contents:

Part I Transparency in Machine Learning

Part II Visual Explanation of Machine Learning Process

Part III Algorithmic Explanation of Machine Learning Models

Part IV User Cognitive Responses in ML-Based Decision Making

Part V Human and Evaluation of Machine Learning

Part VI Domain Knowledge in Transparent Machine Learning Applications.

Other Format:

Printed edition:

ISBN:

978-3-319-90403-0

9783319904030

9783319904023

9783319904047

9783030080075

Access Restriction:

Restricted for use by site license.