Explainable Deep Learning AI: Methods and Challenges presents the latest works of leading researchers in the XAI area, offering an overview of the XAI area, along with several novel technical methods and applications that address explainability challenges for deep learning AI systems. The book overviews XAI and then covers a number of specific technical works and approaches for deep learning, ranging from general XAI methods to specific XAI applications, and finally, with user-oriented evaluation approaches. It also explores the main categories of explainable AI - deep learning, which become the necessary condition in various applications of artificial intelligence.
The groups of methods such as back-propagation and perturbation-based methods are explained, and the application to various kinds of data classification are presented.
- Provides an overview of main approaches to Explainable Artificial Intelligence (XAI) in the Deep Learning realm, including the most popular techniques and their use, concluding with challenges and exciting future directions of XAI
- Explores the latest developments in general XAI methods for Deep Learning
- Explains how XAI for Deep Learning is applied to various domains like images, medicine and natural language processing
- Provides an overview of how XAI systems are tested and evaluated, specially with real users, a critical need in XAI
Sprache
Verlagsort
Verlagsgruppe
Elsevier Science & Techn.
Dateigröße
ISBN-13
978-0-323-99388-3 (9780323993883)
Schweitzer Klassifikation
1. Introduction2. 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
