Explainable AI for Communications and Networking: Toward Responsible Automation gives a tour into the realm of Explainable Artificial Intelligence (XAI) and its impact on transparent and autonomous communication networks. The book equips readers from diverse backgrounds in communications and networking with a variety of XAI tools, metrics and frameworks to demystify AI systems through graphical taxonomies and mathematical formulations, which are further enriched with code snippets. The book also examines XAI implementation in wireless communications, network management, generative AI for telecom and cybersecurity, before presenting practical use-cases emanating from an industry perspective. Finally, the regulatory, ethical, and legal implications of XAI in telecommunications are reviewed, before concluding with key challenges and takeaways.
1. Title of Book - p. i 2. Cover image - p. i 3. Title page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iv 6. Dedication - p. v 7. List of figures - p. xiii 8. List of tables - p. xv 9. Contributors - p. xvii 10. About the editors - p. xix 11. Preface - p. xxi 12. Chapter One: AI-driven network automation - p. 1 13. Chapter Two: Explainable AI overview - p. 23 14. Chapter Three: XAI metrics and frameworks - p. 49 15. Chapter Four: XAI for wireless communications - p. 73 16. Chapter Five: Neuro-symbolic XAI for communications - p. 93 17. Chapter Six: XAI for network management - p. 113 18. Chapter Seven: Responsible generative AI for telecom - p. 129 19. Chapter Eight: Explainable AI for cybersecurity decisions: challenges and opportunities - p. 145 20. Chapter Nine: Explainable AI for telecom use cases - p. 167 21. Chapter Ten: XAI: regulatory and ethical aspects - p. 181 22. Chapter Eleven: Future directions and challenges - p. 209 23. Chapter Twelve: Conclusions: a human-centric perspective - p. 215 24. Index - p. 219
Explainable AI for Communications and Networking: Toward Responsible Automation gives a tour into the realm of Explainable Artificial Intelligence (XAI) and its impact on transparent and autonomous communication networks. The book equips readers from diverse backgrounds in communications and networking with a variety of XAI tools, metrics and frameworks to demystify AI systems through graphical taxonomies and mathematical formulations, which are further enriched with code snippets. The book also examines XAI implementation in wireless communications, network management, generative AI for telecom and cybersecurity, before presenting practical use-cases emanating from an industry perspective. Finally, the regulatory, ethical, and legal implications of XAI in telecommunications are reviewed, before concluding with key challenges and takeaways.
1. Title of Book - p. i 2. Cover image - p. i 3. Title page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iv 6. Dedication - p. v 7. List of figures - p. xiii 8. List of tables - p. xv 9. Contributors - p. xvii 10. About the editors - p. xix 11. Preface - p. xxi 12. Chapter One: AI-driven network automation - p. 1 13. Chapter Two: Explainable AI overview - p. 23 14. Chapter Three: XAI metrics and frameworks - p. 49 15. Chapter Four: XAI for wireless communications - p. 73 16. Chapter Five: Neuro-symbolic XAI for communications - p. 93 17. Chapter Six: XAI for network management - p. 113 18. Chapter Seven: Responsible generative AI for telecom - p. 129 19. Chapter Eight: Explainable AI for cybersecurity decisions: challenges and opportunities - p. 145 20. Chapter Nine: Explainable AI for telecom use cases - p. 167 21. Chapter Ten: XAI: regulatory and ethical aspects - p. 181 22. Chapter Eleven: Future directions and challenges - p. 209 23. Chapter Twelve: Conclusions: a human-centric perspective - p. 215 24. Index - p. 219
