Explainable Artificial Intelligence: concepts, applications, research challenges and visions -- The Explanation Game: Explaining Machine Learning Models Using Shapley Values -- Back to the Feature: a Neural-Symbolic Perspective on Explainable AI -- Explain Graph Neural Networks to Understand Weighted Graph Features in Node Classification -- Explainable Reinforcement Learning: A Survey -- A Projected Stochastic Gradient algorithm for estimating Shapley Value applied in attribute importance -- Explaining predictive models with mixed features using Shapley values and conditional inference trees -- Explainable Deep Learning for Fault Prognostics in Complex Systems: A Particle Accelerator Use-Case -- eXDiL: A Tool for Classifying and eXplaining Hospital Discharge Letters -- Data Understanding and Interpretation by the Cooperation of Data Analyst and Medical Expert -- A study on the fusion of pixels and patient metadata in CNN-based classification of skin lesion images -- The European legal framework for medical AI -- An Efficient Method for Mining Informative Association Rules in Knowledge Extraction -- Interpretation of SVM using Data Mining Technique to Extract Syllogistic Rules -- Non-Local Second-Order Attention Network For Single Image Super Resolution -- ML-ModelExplorer: An explorative model-agnostic approach to evaluate and compare multi-class classifiers -- Subverting Network Intrusion Detection: Crafting Adversarial Examples Accounting for Domain-Specific Constraints -- Scenario-based Requirements Elicitation for User-Centric Explainable AI A Case in Fraud Detection -- On-the-fly Black-Box Probably Approximately Correct Checking of Recurrent Neural Networks -- Active Learning for Auditory Hierarchy -- Improving short text classification through global augmentation methods -- Interpretable Topic Extraction and Word Embedding Learning using row-stochastic DEDICOM -- A Clustering Backed Deep Learning Approach for Document Layout Analysis -- Calibrating Human-AI Collaboration: Impactof Risk, Ambiguity and Transparency on Algorithmic Bias -- Applying AI in Practice: Key Challenges and Lessons Learned -- Function Space Pooling For Graph Convolutional Networks -- Analysis of optical brain signals using connectivity graph networks -- Property-Based Testing for Parameter Learning of Probabilistic Graphical Models -- An Ensemble Interpretable Machine Learning Scheme for Securing Data Quality at the Edge -- Inter-Space Machine Learning in Smart Environments.

This book constitutes the refereed proceedings of the 4th IFIP TC 5, TC 12, WG 8.4, WG 8.9, WG 12.9 International Cross-Domain Conference, CD-MAKE 2020, held in Dublin, Ireland, in August 2020. The 30 revised full papers presented were carefully reviewed and selected from 140 submissions. The cross-domain integration and appraisal of different fields provides an atmosphere to foster different perspectives and opinions; it will offer a platform for novel ideas and a fresh look on the methodologies to put these ideas into business for the benefit of humanity. Due to the Corona pandemic CD-MAKE 2020 was held as a virtual event.