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Introduction

In recent years, Artificial Intelligence (AI) has been increasingly used in critical systems such as autonomous vehicles, medical diagnosis, and financial trading. However, the lack of transparency and interpretability in AI algorithms has raised concerns about the reliability and safety of these systems. Explainable AI (XAI) aims to address this issue by providing explanations for the decisions made by AI systems, enhancing trust, reliability, and accountability in critical applications. This thesis focuses on the implementation of XAI techniques for autonomous decision-making in critical systems.

1.2 Background of Study
1.3 Problem Statement
1.4 Objective of Study
1.5 Limitation of Study
1.6 Scope of Study
1.7 Significance of Study
1.8 Structure of the Thesis
1.9 Definition of Terms

Chapter 2: Literature Review
2.1 Introduction to XAI
2.2 Importance of XAI in Critical Systems
2.3 XAI Techniques and Approaches
2.4 XAI in Autonomous Vehicles
2.5 XAI in Healthcare
2.6 XAI in Finance
2.7 Challenges and Limitations of XAI
2.8 Ethics and Bias in XAI
2.9 XAI Implementation in Real-world Systems
2.10 Summary of Literature Review

Chapter 3: System Design and Methodology
3.1 Introduction to System Design
3.2 Data Collection and Preprocessing
3.3 XAI Model Selection
3.4 XAI Model Training
3.5 Interpretability Evaluation Metrics
3.6 XAI Integration with Autonomous Decision-making
3.7 Performance Evaluation
3.8 Validation and Testing
3.9 Ethical Considerations
3.10 Summary of System Design and Methodology

Chapter 4: System Implementation
4.1 Introduction to System Implementation
4.2 Software and Hardware Requirements
4.3 XAI Model Development
4.4 Data Integration
4.5 System Integration
4.6 Testing and Validation
4.7 Performance Tuning
4.8 Deployment in Critical Systems
4.9 Maintenance and Updates
4.10 Summary of System Implementation

Chapter 5: Conclusion and Summary
5.1 Summary of Findings
5.2 Contributions to Knowledge
5.3 Implications for Future Research
5.4 Limitations of the Study
5.5 Conclusion
5.6 Practical Applications
5.7 Recommendations
5.8 Final Remarks

Thesis Overview:

Explainable AI (XAI) has emerged as a crucial technology in the development of autonomous decision-making systems in critical applications. This thesis aims to explore the implementation of XAI techniques to enhance the transparency and interpretability of AI algorithms in critical systems such as autonomous vehicles, medical diagnosis, and financial trading.

The introduction provides a background of the study, problem statement, objective, limitations, scope, significance, structure of the thesis, and definition of terms. The literature review examines the importance of XAI in critical systems, XAI techniques and approaches, challenges, and limitations of XAI, ethics and bias in XAI, and real-world implementation of XAI.

The system design and methodology chapter details the steps involved in designing and implementing an XAI system for autonomous decision-making, including data collection, preprocessing, model selection, training, evaluation metrics, integration, and testing. The system implementation chapter focuses on the practical aspects of implementing the XAI system, including software and hardware requirements, model development, data integration, testing, deployment, maintenance, and updates.

In conclusion, this thesis summarizes the findings, contributions to knowledge, implications for future research, limitations, practical applications, recommendations, and final remarks on the implementation of XAI for autonomous decision-making in critical systems.

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