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

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

Part I Transparency in Machine Learning
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

Part II Visual Explanation of Machine Learning Process
Chapter 6 Deep Learning For Plant Diseases: Detection And Saliency Map Visualisation
Chapter 7 Critical Challenges For The Visual Representation Of Deep Neural Networks

Part III Algorithmic Explanation of Machine Learning Models
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”

Part IV User Cognitive Responses in ML-Based Decision Making
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

Part V Human and Evaluation of Machine Learning
Chapter 16 User-Centred Evaluation For Machine Learning
Chapter 17 Evaluation Of Interactive Machine Learning Systems

Part VI Domain Knowledge in Transparent Machine Learning Applications
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