Explainable and Interpretable Models in Computer Vision and Machine Learning

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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.