1. Introduction
2. Explainable Deep Learning: Methods, Concepts and New Developments
3. Compact Visualization of DNN Classification Performances for Interpretation and Improvement
4. Explaining How Deep Neural Networks Forget by Deep Visualization
5. Characterizing a scene recognition model by identifying the effect of input features via semantic- wise attribution
6. A Feature Understanding Method for Explanation of Image Classification by Convolutional Neural Networks
7. Explainable Deep Learning for decrypting disease signature in Multiple Sclerosis
8. Explanation of CNN Image Classifiers with Hiding Parts
9. Remove to Improve?
10. Explaining CNN classifier using Association Rule Mining Methods on time-series
11. A Methodology to compare XAI Explanations on Natural Language Processing
12. Improving Malware Detection with Explainable Machine Learning
13. AI Explainability. A Bridge between Machine Vision and Natural Language Processing
14. Explainable Deep Learning for Multimedia Indexing and Retrieval
15. User Tests and Techniques for the Post-Hoc Explainability of Deep Learning Models
16. Conclusion