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Introduction

Artificial Intelligence (AI) has revolutionized many industries, including human resources (HR), by enabling automation of various decision-making processes. However, the lack of transparency in AI algorithms poses challenges in understanding the reasoning behind the decisions made by AI systems. This issue has led to the emergence of Explainable AI, which aims to improve the transparency and interpretability of AI models. In the context of HR decision-making, Explainable AI can help organizations make more informed and fair decisions regarding recruitment, performance evaluation, and other HR processes.

Chapter One: Introduction
1.1 Introduction
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 Two: Literature Review
2.1 Overview of AI in HR
2.2 Explainable AI in HR
2.3 Benefits of Explainable AI in HR
2.4 Challenges of Implementing Explainable AI in HR
2.5 Current Trends in Explainable AI for HR Decision-making
2.6 Case Studies on the Use of Explainable AI in HR
2.7 Ethical Considerations in Explainable AI for HR
2.8 Regulatory Framework for Explainable AI in HR
2.9 Industry Best Practices in Implementing Explainable AI in HR
2.10 Future Research Directions in Explainable AI for HR

Chapter Three: Research Methodology
3.1 Research Design
3.2 Data Collection Methods
3.3 Data Analysis Techniques
3.4 Sampling Strategy
3.5 Research Instrument
3.6 Data Validation Methods
3.7 Ethical Considerations
3.8 Limitations of the Research

Chapter Four: Discussion of Findings
4.1 Analysis of Research Results
4.2 Comparison with Existing Literature
4.3 Implications for HR Practice
4.4 Recommendations for Organizations
4.5 Suggestions for Future Research

Chapter Five: Conclusion and Summary
5.1 Summary of Key Findings
5.2 Contributions to the Field
5.3 Practical Implications
5.4 Limitations of the Study
5.5 Suggestions for Future Research

Thesis Overview on Explainable AI for Automated Human Resources Decision-making

Artificial Intelligence (AI) has been increasingly used in human resources (HR) for automating decision-making processes. However, the lack of transparency in AI algorithms has raised concerns about biases and fairness in HR decisions. Explainable AI has emerged as a solution to address these issues by providing insights into how AI systems arrive at decisions. In this thesis, we aim to explore the application of Explainable AI in automated HR decision-making processes.

The literature review will provide an overview of the current state of AI in HR, the concepts of Explainable AI, its benefits, challenges, and best practices in implementing it for HR decision-making. Case studies and ethical considerations will also be discussed to provide a comprehensive understanding of the topic.

The research methodology will outline the design, data collection methods, analysis techniques, and ethical considerations involved in the study. The discussion of findings will analyze the research results, compare them with existing literature, and provide implications and recommendations for HR practice.

In conclusion, this thesis aims to contribute to the field of HR by highlighting the importance of transparency and interpretability in AI-driven decision-making processes. Suggestions for future research will be provided to guide further exploration in this area.

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