Explainable, Interpretable, and Transparent AI Systems

Autorentext

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 SITE school of VIT; Vellore. He received his Ph.D. degree in 1983. During his student career, he received three gold medals for standing first at graduation level, standing first at 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, proceeding 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 National Institute of Technology (formerly R. E. C.) Warangal and obtained Ph.D. from 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 and machine learning. She received Best paper award for the paper entitled On improving the generalization of SVM Classifier in 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 Editor for the edited volume on Modern Technologies for Big Data Classification and Clustering published in 2017. She is a member of editorial board for various International Journals.

Klappentext

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.

Zusammenfassung

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.

Inhalt

1. Unveiling the Power of Explainable AI: Real-World Applications and Implications

2. Looking at exploratory paradigms of explainability in creative computing

3. Applications of XAI in Modern Automotive, Financial and Manufacturing Sectors

4. Explainable AI in Distributed Denial of Service Detection

5. Adaptations of XAI in Smart Agricultural Systems

6. Explainable artificial intelligence for Healthcare applications using Random Forest Classifier with LIME and SHAP

7. Explainable AI and its implications in the business world

8. Fair and Explainable Systems: Informed Decision Making in Machine Learning

9. A Review on Interpretation of Deep Neural Network Predictions on the Various Data through LIME

10. Comprehensive study on Social Trust with XAI Techniques, Evaluation and Future Directions

11. Fuzzy Clustering for Streaming Environment with Explainable Parameter Determination

12. Demystifying the Black Box: Unveiling the Decision-Making Process of AI Systems

13. Explainable Deep Learning Architectures to Study the Customers purchase Behaviour for Product Recommendations

14. Metamorphic Testing for Trustworthy AI

15. Software For Explainable AI

16. Interpretations and Visualization in AI Systems- Methods and Approaches

17. A Study on Transparent Recommendation Systems