Explainable, Interpretable, and Transparent AI Systems

✍ Scribed by B. K. Tripathy (editor), Hari Seetha (editor)

• Publisher: CRC Press
• Year: 2024
• Tongue: English
• Leaves: 328
• Edition: 1
• Category: Library

✦ Synopsis

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.

✦ Table of Contents

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