Investigating the Role of Explainable AI in Enhancing User Trust in Machine Learning Models

1. Introduction 1.1 Background and Motivation 1.2 Research Objectives 1.3 Structure of the Study 2. Literature Review 2.1 Explainable Artificial Intelligence (XAI) Concepts 2.2 User Trust in Technology 2.3 Existing Machine Learning Models 2.4 Relevant Case Studies 2.5 Summary of Findings 3. Methodology 3.1 Research Design 3.2 Data Collection Techniques 3.3 Analytical Methods 3.4 Ethical Considerations 4. Explainable AI Frameworks 4.1 Post-Hoc vs. Intrinsic Explainability 4.2 Tools and Techniques 4.3 Comparative Analysis of Frameworks 5. User Trust Dynamics 5.1 Factors Influencing Trust 5.2 Trust Measurement Approaches 5.3 Trust in AI Context 6. Case Study Analysis 6.1 Case Study Selection Criteria 6.2 Implementation of AI Models 6.3 Results and Discussions 6.4 User Feedback and Insights 7. Findings and Discussion 7.1 Impact of Explainability on Trust 7.2 Strengths and Limitations 7.3 Implications for AI Development 8. Conclusion and Future Work 8.1 Summary of Key Contributions 8.2 Recommendations for Practitioners 8.3 Directions for Future Research