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Human and Machine Learning

Overview of attention for book
Cover of 'Human and Machine Learning'

Table of Contents

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

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Readers on

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71 Mendeley
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Chapter title
Transparency in Fair Machine Learning: the Case of Explainable Recommender Systems
Chapter number 2
Book title
Human and Machine Learning
Published by
Springer, Cham, January 2018
DOI 10.1007/978-3-319-90403-0_2
Book ISBNs
978-3-31-990402-3, 978-3-31-990403-0
Authors

Behnoush Abdollahi, Olfa Nasraoui, Abdollahi, Behnoush, Nasraoui, Olfa

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 71 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 71 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 13 18%
Student > Ph. D. Student 11 15%
Researcher 8 11%
Student > Bachelor 6 8%
Student > Doctoral Student 5 7%
Other 9 13%
Unknown 19 27%
Readers by discipline Count As %
Computer Science 33 46%
Business, Management and Accounting 5 7%
Engineering 4 6%
Psychology 2 3%
Social Sciences 2 3%
Other 3 4%
Unknown 22 31%