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.
B.K. Tripathy is a distinguished researcher in the fields of Computer Science and Mathematics and is working as a professor (Higher Academic Grade) in the SCORE School of VIT, Vellore. He received his Ph.D. degree in 1983. During his student career, he received three gold medals for securing first position at the graduation level, securing first position at the postgraduate level, and being adjudged as the best postgraduate of the year from Berhampur University, Odisha. He has the distinction of receiving the national scholarship at PG level, UGC (Govt. of India) fellowship for pursuing his research, DST (Govt. of India) fellowship for pursuing M. Tech. (Computer Science) in Pune University, and the SERC fellowship (DOE, Govt. India) for joining IIT Kharagpur as a visiting fellow. He has published more than 740 articles in international journals, proceedings of international conferences of repute, chapters in edited research volumes. Also, he has edited 11 research volumes, written two books and two monographs. He has acted as member of international advisory committee/Technical Program Committee of more than 140 international conferences and in some of them has delivered the key note addresses.
Hari Seetha obtained her master’s degree from the National Institute of Technology (formerly R.E.C.) Warangal and obtained her Ph.D. from the School of Computer Science and Engineering, VIT University, Vellore, India. She worked on Large Data Classification during her Ph.D. She has research interests in the fields of pattern recognition, data mining, text mining, soft computing, XAI, IDS, and machine learning. She received the Best Paper Award for the paper entitled “On improving the generalization of SVM Classifier” at the Fifth International Conference on Information Processing held at Bangalore. She has published several research papers in national and international journals of repute. She has been one of the editors for the edited volume, Modern Technologies for Big Data Classification and Clustering published in 2017. She is a member of editorial board for various international journals.
