Transparent Artificial Intelligence Systems facilitate understanding the decision-making process and provide opportunities in various aspects of providing explainability of AI models. This book provides up-to-date information on 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 the domains of health care, finance, network security etc. It also covers open-source interpretable tool kits such 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 good handling with respect to existing software and evaluation issues of interpretability.
• Provides learnings on simple interpretable models such as decision trees, decision rules, and linear regression.
• Focusses on interpreting black box models like feature importance and accumulated local effects.
• Discusses explainability and interpretability capabilities.

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