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.
