Explainable, Interpretable, and Transparent AI Systems

1. Auflage 2024
ISBN: 978-1-032-52856-4
Verlag: CRC Press

Buch, Englisch, 354 Seiten, Format (B × H): 161 mm x 240 mm, Gewicht: 696 g

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.

Zielgruppe

Academic, Postgraduate, and Professional Reference

Autoren/Hrsg.

Tripathy, B. K.

Seetha, Hari

Fachgebiete

• Technische Wissenschaften Energietechnik | Elektrotechnik Elektrotechnik

Inhaltsverzeichnis

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

Über Autor(innen)

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.