Chapter 1 2D Transparency Space—Bring Domain Users and Machine Learning Experts Together

Chapter 2 Transparency in Fair Machine Learning: the Case of Explainable Recommender Systems

Chapter 3 Beyond Human-in-the-Loop: Empowering End-Users with Transparent Machine Learning

Chapter 4 Effective Design in Human and Machine Learning: A Cognitive Perspective

Chapter 5 Transparency Communication for Machine Learning in Human-Automation Interaction

Chapter 6 Deep Learning for Plant Diseases: Detection and Saliency Map Visualisation

Chapter 7 Critical Challenges for the Visual Representation of Deep Neural Networks

Chapter 8 Explaining the Predictions of an Arbitrary Prediction Model: Feature Contributions and Quasi-nomograms

Chapter 9 Perturbation-Based Explanations of Prediction Models

Chapter 10 Model Explanation and Interpretation Concepts for Stimulating Advanced Human-Machine Interaction with “Expert-in-the-Loop”

Chapter 11 Revealing User Confidence in Machine Learning-Based Decision Making

Chapter 12 Do I Trust a Machine? Differences in User Trust Based on System Performance

Chapter 13 Trust of Learning Systems: Considerations for Code, Algorithms, and Affordances for Learning

Chapter 14 Trust and Transparency in Machine Learning-Based Clinical Decision Support

Chapter 15 Group Cognition and Collaborative AI

Chapter 16 User-Centred Evaluation for Machine Learning

Chapter 17 Evaluation of Interactive Machine Learning Systems

Chapter 18 Water Pipe Failure Prediction: A Machine Learning Approach Enhanced By Domain Knowledge

Chapter 19 Analytical Modelling of Point Process and Application to Transportation

Chapter 20 Structural Health Monitoring Using Machine Learning Techniques and Domain Knowledge Based Features

Chapter 21 Domain Knowledge in Predictive Maintenance for Water Pipe Failures

Chapter 22 Interactive Machine Learning for Applications in Food Science