Human and Machine Learning Visible Explainable Trustworthy and Transparent 1st Edition by Jianlong Zhou, Fang Chen ISBN 9783319904023 3319904027

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Product details:

ISBN 10: 3319904027

ISBN 13: 9783319904023

Author: Jianlong Zhou, Fang Chen

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.

Table of contents: 

1. 2D Transparency Space—Bring Domain Users and Machine Learning Experts Together
2. Transparency in Fair Machine Learning: the Case of Explainable Recommender Systems
3. Beyond Human-in-the-Loop: Empowering End-Users with Transparent Machine Learning
4. Effective Design in Human and Machine Learning: A Cognitive Perspective
5. Transparency Communication for Machine Learning in Human-Automation Interaction
6. Deep Learning for Plant Diseases: Detection and Saliency Map Visualisation
7. Critical Challenges for the Visual Representation of Deep Neural Networks
8. Explaining the Predictions of an Arbitrary Prediction Model: Feature Contributions and Quasi-nomograms
9. Perturbation-Based Explanations of Prediction Models
10. Model Explanation and Interpretation Concepts for Stimulating Advanced Human-Machine Interaction with “Expert-in-the-Loop”
11. Revealing User Confidence in Machine Learning-Based Decision Making
12. Do I Trust a Machine? Differences in User Trust Based on System Performance
13. Trust of Learning Systems: Considerations for Code, Algorithms, and Affordances for Learning
14. Trust and Transparency in Machine Learning-Based Clinical Decision Support
15. Group Cognition and Collaborative AI
16. User-Centred Evaluation for Machine Learning
17. Evaluation of Interactive Machine Learning Systems
18. Water Pipe Failure Prediction: A Machine Learning Approach Enhanced By Domain Knowledge
19. Analytical Modelling of Point Process and Application to Transportation
20. Structural Health Monitoring Using Machine Learning Techniques and Domain Knowledge Based Features
21. Domain Knowledge in Predictive Maintenance for Water Pipe Failures
22. Interactive Machine Learning for Applications in Food Science

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Tags:

Jianlong Zhou,Fang Chen,Human,Machine Learning,Visible,Explainable,Trustworthy,Transparent