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ORCIDHuman and Machine Learning, 2018
- Jianlong Zhou
, Fang Chen:
Human and Machine Learning - Visible, Explainable, Trustworthy and Transparent. Human-Computer Interaction Series, Springer 2018, ISBN 978-3-319-90402-3
Transparency in Machine Learning
- Jianlong Zhou, Fang Chen:
2D Transparency Space - Bring Domain Users and Machine Learning Experts Together. 3-19 - Behnoush Abdollahi, Olfa Nasraoui:
Transparency in Fair Machine Learning: the Case of Explainable Recommender Systems. 21-35 - Patrick C. Shih:
Beyond Human-in-the-Loop: Empowering End-Users with Transparent Machine Learning. 37-54 - Robert Zheng, Kevin Greenberg:
Effective Design in Human and Machine Learning: A Cognitive Perspective. 55-74 - David V. Pynadath, Michael J. Barnes, Ning Wang, Jessie Y. C. Chen:
Transparency Communication for Machine Learning in Human-Automation Interaction. 75-90
Visual Explanation of Machine Learning Process
- Mohammed Brahimi, Marko Arsenovic, Sohaib Laraba, Srdjan Sladojevic, Kamel Boukhalfa, Abdelouahab Moussaoui:
Deep Learning for Plant Diseases: Detection and Saliency Map Visualisation. 93-117 - Kieran Browne, Ben Swift, Henry J. Gardner:
Critical Challenges for the Visual Representation of Deep Neural Networks. 119-136
Algorithmic Explanation of Machine Learning Models
- Erik Strumbelj, Igor Kononenko:
Explaining the Predictions of an Arbitrary Prediction Model: Feature Contributions and Quasi-nomograms. 139-157 - Marko Robnik-Sikonja, Marko Bohanec:
Perturbation-Based Explanations of Prediction Models. 159-175 - Edwin Lughofer:
Model Explanation and Interpretation Concepts for Stimulating Advanced Human-Machine Interaction with "Expert-in-the-Loop". 177-221
User Cognitive Responses in ML-Based Decision Making
- Jianlong Zhou, Kun Yu, Fang Chen:
Revealing User Confidence in Machine Learning-Based Decision Making. 225-244 - Kun Yu, Shlomo Berkovsky, Dan Conway, Ronnie Taib, Jianlong Zhou, Fang Chen:
Do I Trust a Machine? Differences in User Trust Based on System Performance. 245-264 - Joseph B. Lyons, Nhut Tan Ho, Jeremy Friedman, Gene M. Alarcon, Svyatoslav Guznov:
Trust of Learning Systems: Considerations for Code, Algorithms, and Affordances for Learning. 265-278 - Cosima Gretton:
Trust and Transparency in Machine Learning-Based Clinical Decision Support. 279-292 - Janin Koch, Antti Oulasvirta:
Group Cognition and Collaborative AI. 293-312
Human and Evaluation of Machine Learning
- Scott Allen Cambo, Darren Gergle:
User-Centred Evaluation for Machine Learning. 315-339 - Nadia Boukhelifa, Anastasia Bezerianos, Evelyne Lutton:
Evaluation of Interactive Machine Learning Systems. 341-360
Domain Knowledge in Transparent Machine Learning Applications
- Bang Zhang, Ting Guo, Lelin Zhang, Peng Lin, Yang Wang, Jianlong Zhou, Fang Chen:
Water Pipe Failure Prediction: A Machine Learning Approach Enhanced By Domain Knowledge. 363-383 - Le Minh Kieu:
Analytical Modelling of Point Process and Application to Transportation. 385-408 - Nguyen Lu Dang Khoa, Mehrisadat Makki Alamdari, Thierry Rakotoarivelo, Ali Anaissi, Yang Wang:
Structural Health Monitoring Using Machine Learning Techniques and Domain Knowledge Based Features. 409-435 - Zhidong Li, Yang Wang:
Domain Knowledge in Predictive Maintenance for Water Pipe Failures. 437-457 - Alberto Paolo Tonda, Nadia Boukhelifa, Thomas Chabin, Marc Barnabé, Benoît Génot, Evelyne Lutton, Nathalie Perrot:
Interactive Machine Learning for Applications in Food Science. 459-477
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