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Human and Machine Learning - Visible, Explainable, Trustworthy and Transparent. Jianlong ZhouFang Chen (Eds). Springer Cham. 2018, 482 p.
Print ISBN: 978-3-319-90402-3. Online ISBN: 978-3-319-90403-0. © Springer Intl Publishing

Human and Machine Learning - Visible, Explainable, Trustworthy and Transparent

Human and Machine Learning creates a systematic view of relations between human and machine learning, explores human aspects in machine learning and provides the first dedicated source of the state-of-the-art advances in theories, techniques and applications of trustworthy and transparent machine learning.

Updated on 01/22/2019
Published on 01/22/2019

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. Human and Machine Learning 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.

 

Human and Machine Learning - Visible, Explainable, Trustworthy and Transparent. Jianlong ZhouFang Chen (Eds). Springer Cham. 2018, 482 p.
Print ISBN: 978-3-319-90402-3. Online ISBN: 978-3-319-90403-0.
More information

With the contribution of Nadia Boukhelifa, INRA Researcher at the Joint Research Unit for Microbiology and Food Process Engineering (AGROPARISTECH, INRA) as co-author of the chapters "Evaluation of Interactive Machine Learning Systems" and "Interactive Machine Learning for Applications in Food Science".