Users expect proper explanation and interpretability of all the decisions being taken by machine and deep learning (ML/ DL) algorithms. Interpretable and Trustworthy AI: Techniques and Frameworks covers key requirements for interpretability and trustworthiness of artificial intelligence (AI) models and how these needs can be met. This book explores artificial intelligence’s impact, limitations, and solutions.
It examines AI’s role as a transformative technological paradigm. It explores how AI drives business advancement through intelligent software solutions, enabling automation, augmentation, and acceleration of IT-enabled business processes. The book establishes AI’s fundamental capacity to envision and implement sustainable business transformations.
It addresses critical challenges in AI adoption, focusing on two key concerns:
- AI Interpretability: Models typically optimize for accuracy but struggle to capture real-world costs, especially regarding ethics and fairness. Interpretability features help understand model learning processes, available information, and decision justifications within real-world contexts.
- Trustworthy AI: Business leaders demand responsible AI solutions that prioritize human needs, safety, and privacy. Researchers are developing methods to enhance trust in AI models and their conclusions to accelerate adoption.
Finally, the book presents techniques and approaches for creating sustainable, interpretable, and trustworthy AI models. It explores model-agnostic frameworks and methodologies designed to Trustworthy and Transparent AI, Explainable and Interpretable AI, Responsible AI, Generative AI, Agentic AI, and Efficient and Edge AI.
With its comprehensive structure, the book provides a comprehensive examination of AI’s potential, its current limitations, and pathways to overcome these challenges for wider adoption.
Table of Contents:
1. Demystifying AI: A Comparative Study on Artificial General Intelligence and Artificial Superintelligence 2. Interpretable and Trustworthy Sleep Pattern Analysis for Sleep Disorders Using Explainable AI (XAI) Techniques 3. Navigating the Landscape of Interpretable and Trustworthy AI: Key Challenges and Solutions 4. Emerging Trends in Deep Learning 5. Deep Learning: Innovations, Applications, and Future Directions 6. Generative Adversarial Networks: Architecture, Training Dynamics, Applications, and Future Directions in AI 7. Exploring Generative Adversarial Networks Core Concepts, Innovation, and Future Implications in AI 8. Local Interpretable Model- Agnostic Explanations (LIME) 9. Analysis of SHAP-Based Interpretable Feature Selection Techniques for Advancing Healthcare Decision-Making 10. DALEX (Model Agnostic Exploration, Explanation and Learning Implementation in Interpretable AI) 11. Bridging Concepts to Reality: Tools and Technologies for Interpretable and Reliable AI 12. AI Audit and Compliance Frameworks: Building Trust Through Systematic Validation 13. Data Privacy and Security in Artificial Intelligence: Tools, Challenges, and Innovations14. Interpretable AI in Healthcare: Frameworks, Applications, and Future Directions 15. AI Applications for Finance and Banking: Techniques, Challenges, and Future Directions 16. Interpretable AI in Finance: Enhancing Transparency and Trust 17. SkinGAN: Enhancing Diagnostic Sensitivity of Rare Skin Lesions through StyleGAN-Based Synthesis18. Advancing Interpretable Machine Learning: Principles, Challenges, and Practical Insights
