This book compiles leading research on the development of explainable and interpretable machine
learning methods in the context of computer vision and machine learning. Research progress in
computer vision and pattern recognition has led to a variety of modeling techniques with almost
human-like performance. Although these models have obtained astounding results they are
limited in their explainability and interpretability: what is the rationale behind the decision
made? what in the model structure explains its functioning? Hence while good performance is a
critical required characteristic for learning machines explainability and interpretability
capabilities are needed to take learning machines to the next step to include them in decision
support systems involving human supervision. This book written by leading international
researchers addresses key topics of explainability and interpretability including the
following: · Evaluation and Generalization in Interpretable Machine Learning · Explanation
Methods in Deep Learning · Learning Functional Causal Models with Generative Neural Networks ·
Learning Interpreatable Rules for Multi-Label Classification · Structuring Neural Networks for
More Explainable Predictions · Generating Post Hoc Rationales of Deep Visual Classification
Decisions · Ensembling Visual Explanations · Explainable Deep Driving by Visualizing Causal
Attention · Interdisciplinary Perspective on Algorithmic Job Candidate Search · Multimodal
Personality Trait Analysis for Explainable Modeling of Job Interview Decisions · Inherent
Explainability Pattern Theory-based Video Event Interpretations
