Editorial
xxAI - Beyond explainable Artificial Intelligence
Current Methods and Challenges
Explainable AI Methods - A Brief Overview
Challenges in Deploying Explainable Machine Learning
Methods for Machine Learning Models
CLEVR-X: A Visual Reasoning Dataset for Natural Language Explanations
New Developments in Explainable AI
A Rate-Distortion Framework for Explaining Black-box Model Decisions
Explaining the Predictions of Unsupervised Learning Models
Towards Causal Algorithmic Recourse
Interpreting Generative Adversarial Networks for Interactive Image Generation
XAI and Strategy Extraction via Reward Redistribution
Interpretable, Verifiable, and Robust Reinforcement Learning via Program Synthesis
Interpreting and improving deep-learning models with reality checks
Beyond the Visual Analysis of Deep Model Saliency
ECQ^2: Quantization for Low-Bit and Sparse DNNs
A whale’s tail - Finding the right whale in an uncertain world
Explainable Artificial Intelligence in Meteorology and Climate Science: Model fine-tuning, calibrating trust and learning new science
An Interdisciplinary Approach to Explainable AI.-Varieties of AI Explanations under the Law - From the GDPR to the AIA, and beyond
Towards Explainability for AI Fairness
Logic and Pragmatics in AI Explanation.