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Full Description
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 AI models and how these needs can be met. This book is structured in three main sections exploring artificial intelligence's impact, limitations, and solutions.
The first section 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 section establishes AI's fundamental capacity to envision and implement sustainable business transformations.
The second section 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.
The final section presents techniques and approaches for creating sustainable, interpretable, and trustworthy AI models. It explores model-agnostic frameworks and methodologies designed to
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.
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. Generative Adversarial Networks in Artificial Intelligence: Advances, Applications, and Future Directions 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 Ideas to Reality: Tools and Technologies for Interpretable and Trustworthy AI 12. AI Audit and Compliance Frameworks: Building Trust Through Systematic Validation 13. Data Privacy and Security 14. 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: A StyleGAN-Based Framework for Synthetic Generation of Rare Skin Lesion Images to Enhance Diagnostic Sensitivity in Dermatology 18. Navigating the Challenges of Interpretable Machine Learning
