Explainable, Interpretable, and Transparent AI Systems

Transparent Artificial Intelligence (AI) systems facilitate understanding the decision-making process and provide opportunities in various aspects of explaining of AI models. This book provides up-to-date information on the latest advancements in the field of explainable AI, which is the critical requirement of AI/ML/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 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.