Explainable, Interpretable, and Transparent AI Systems
eBook - ePub

Explainable, Interpretable, and Transparent AI Systems

  1. 354 pages
  2. English
  3. ePUB (mobile friendly)
  4. Available on iOS & Android
eBook - ePub

Explainable, Interpretable, and Transparent AI Systems

About this book

Transparent Artificial Intelligence (AI) systems facilitate understanding of the decision-making process and provide opportunities in various aspects of explaining AI models. This book provides up-to-date information on the latest advancements in the field of explainable AI, which is a critical requirement of AI, Machine Learning (ML), and Deep Learning (DL) models. It provides examples, case studies, latest techniques, and applications from domains such as healthcare, finance, and network security. It also covers open-source interpretable tool kits so that practitioners can use them in their domains.

Features:

  • Presents a clear focus on the application of explainable AI systems while tackling important issues of "interpretability" and "transparency".
  • Reviews adept handling with respect to existing software and evaluation issues of interpretability.
  • Provides insights into simple interpretable models such as decision trees, decision rules, and linear regression.
  • Focuses on interpreting black box models like feature importance and accumulated local effects.
  • Discusses capabilities of explainability and interpretability.

This book is aimed at graduate students and professionals in computer engineering and networking communications.

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Yes, you can access Explainable, Interpretable, and Transparent AI Systems by B. K. Tripathy,Hari Seetha in PDF and/or ePUB format, as well as other popular books in Technology & Engineering & Electrical Engineering & Telecommunications. We have over one million books available in our catalogue for you to explore.

Table of contents

  1. Cover
  2. Half Title
  3. Title Page
  4. Copyright Page
  5. Dedication
  6. Table of Contents
  7. Preface
  8. Acknowledgments
  9. About the Editors
  10. List of Contributors
  11. Chapter 1 Unveiling the Power of Explainable AI: Real-World Applications and Implications
  12. Chapter 2 Looking at Exploratory Paradigms of Explainability in Creative Computing
  13. Chapter 3 Applications of XAI in Modern Automotive, Financial, and Manufacturing Sectors
  14. Chapter 4 Explainable AI in Distributed Denial of Service Detection
  15. Chapter 5 Adaptation of XAI for Smart Agriculture Systems
  16. Chapter 6 Explainable Artificial Intelligence for Healthcare Applications Using Random Forest Classifier with LIME and SHAP
  17. Chapter 7 Explainable AI and Its Usefulness in the Business World
  18. Chapter 8 Fair and Explainable Systems: Informed Decision Making in AI/ML
  19. Chapter 9 Interpretation of Deep Network Predictions on Various Data Sets Using LIME
  20. Chapter 10 Comprehensive Study on Social Trust with XAI: Techniques, Evaluation, and Future Direction
  21. Chapter 11 Fuzzy Clustering for Streaming Environment with Explainable Parameter Determination
  22. Chapter 12 Demystifying the Black Box: Unveiling the Decision-Making Process of AI Systems
  23. Chapter 13 Explainable Deep Learning Architectures for Product Recommendations
  24. Chapter 14 Metamorphic Testing for Trustworthy AI
  25. Chapter 15 Software for Explainable AI
  26. Chapter 16 Interpretation and Visualization Techniques in AI Systems and Applications
  27. Chapter 17 A Study on Transparent Recommender Systems
  28. Index