Explainable and Interpretable Models in Computer Vision and Machine Learning

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: nbsp;        Evaluation and Generalization in Interpretable Machine Learningnbsp;        Explanation Methods in Deep Learningnbsp;        Learning Functional Causal Models with Generative Neural Networksnbsp;        Learning Interpreatable Rules for Multi-Label Classificationnbsp;        Structuring Neural Networks for More Explainable Predictionsnbsp;        Generating Post Hoc Rationales of Deep Visual Classification Decisionsnbsp;        Ensembling Visual Explanationsnbsp;        Explainable Deep Driving by Visualizing Causal Attentionnbsp;        Interdisciplinary Perspective on Algorithmic Job Candidate Searchnbsp;        Multimodal Personality Trait Analysis for Explainable Modeling of Job Interview Decisions nbsp;        Inherent Explainability Pattern Theory-based Video Event Interpretations

1 Considerations for Evaluation and Generalization in Interpretable Machine Learning.- 2 Explanation Methods in Deep Learning: Users, Values, Concerns and Challenges.- 3 Learning Functional Causal Models with Generative Neural Networks.- 4 Learning Interpretable Rules for Multi-label Classification.- 5 Structuring Neural Networks for More Explainable Predictions.- 6 Generating Post-Hoc Rationales of Deep Visual Classification Decisions.- 7 Ensembling Visual Explanations.- 8 Explainable Deep Driving by Visualizing Causal Action.- 9 Psychology Meets Machine Learning: Interdisciplinary Perspectives on Algorithmic Job Candidate Screening.- 10 Multimodal Personality Trait Analysis for Explainable Modeling of Job Interview Decisions.- 11 On the Inherent Explainability of Pattern Theory-based Video Event Interpretations.