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Full Description
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.
Contents
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
