.- Counterfactual explanations and causality for eXplainable AI. .- Sub-SpaCE: Subsequence-based Sparse Counterfactual Explanations for Time Series Classification Problems. .- Human-in-the-loop Personalized Counterfactual Recourse. .- COIN: Counterfactual inpainting for weakly supervised semantic segmentation for medical images. .- Enhancing Counterfactual Explanation Search with Diffusion Distance and Directional Coherence. .- CountARFactuals -- Generating plausible model-agnostic counterfactual explanations with adversarial random forests. .- Causality-Aware Local Interpretable Model-Agnostic Explanations. .- Evaluating the Faithfulness of Causality in Saliency-based Explanations of Deep Learning Models for Temporal Colour Constancy. .- CAGE: Causality-Aware Shapley Value for Global Explanations. .- Fairness, trust, privacy, security, accountability and actionability in eXplainable AI. .- Exploring the Reliability of SHAP Values in Reinforcement Learning. .- Categorical Foundation of Explainable AI: A Unifying Theory. .- Investigating Calibrated Classification Scores through the Lens of Interpretability. .- XentricAI: A Gesture Sensing Calibration Approach through Explainable and User-Centric AI. .- Toward Understanding the Disagreement Problem in Neural Network Feature Attribution. .- ConformaSight: Conformal Prediction-Based Global and Model-Agnostic Explainability Framework. .- Differential Privacy for Anomaly Detection: Analyzing the Trade-off Between Privacy and Explainability. .- Blockchain for Ethical & Transparent Generative AI Utilization by Banking & Finance Lawyers. .- Multi-modal Machine learning model for Interpretable Mobile Malware Classification. .- Explainable Fraud Detection with Deep Symbolic Classification. .- Better Luck Next Time: About Robust Recourse in Binary Allocation Problems. .- Towards Non-Adversarial Algorithmic Recourse. .- Communicating Uncertainty in Machine Learning Explanations: A Visualization Analytics Approach for Predictive Process Monitoring. .- XAI for Time Series Classification: Evaluating the Benefits of Model Inspection for End-Users.