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Introduction:

Fairness and Bias Mitigation in AI Systems is a critical topic in the field of artificial intelligence. As AI becomes increasingly integrated into various aspects of society, it is essential to ensure that these systems are fair and unbiased. Bias in AI can lead to unjust discrimination, reinforcing systemic inequalities, and undermining trust in these systems. This thesis will explore the concepts of fairness and bias mitigation in AI systems, examining current strategies and proposing new approaches to address these challenges.

Masters Thesis Table of Contents:

Chapter 1: Introduction
1.1 Introduction
1.2 Objective of Study
1.3 Limitation of Study
1.4 Scope of Study

Chapter 2: Literature Review
2.1 Definition of Fairness and Bias in AI Systems
2.2 Types of Bias in AI Systems
2.3 Current Approaches to Fairness and Bias Mitigation
2.4 Critiques of Existing Strategies

Chapter 3: Research Methodology
3.1 Research Design
3.2 Data Collection
3.3 Data Analysis
3.4 Evaluation Metrics

Chapter 4: Discussion of Findings
4.1 Analysis of Empirical Results
4.2 Comparison of Different Approaches
4.3 Implications for Future Research

Chapter 5: Conclusion and Summary
5.1 Summary of Findings
5.2 Contributions to the Field
5.3 Recommendations for Future Research

Thesis Overview:

Fairness and Bias Mitigation in AI Systems is a crucial area of research as AI technologies continue to advance and become more integrated into society. This thesis will explore the concepts of fairness and bias in AI systems, examining the different types of bias that can emerge and the potential consequences of biased AI systems.

The objectives of this study are to analyze current strategies for fairness and bias mitigation in AI systems, identify their limitations, and propose new approaches to address these challenges. By conducting a comprehensive literature review and empirical analysis, this thesis aims to contribute to the ongoing conversation surrounding fairness and bias in AI systems.

The limitations of this study include the focus on specific types of bias and the constraints of available data for analysis. The scope of this study is limited to examining fairness and bias mitigation strategies within the context of AI systems, without delving into broader ethical considerations.

Overall, this thesis will provide valuable insights into the complex issues of fairness and bias in AI systems, offering recommendations for future research and practical applications. By addressing these challenges, we can work towards creating more equitable and trustworthy AI systems for the benefit of all.

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