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Introduction

In recent years, the use of artificial intelligence (AI) systems in the recruitment process has become increasingly popular. These systems help to automate the initial screening of resumes, saving time for recruiters and providing a more efficient way to identify potential candidates. However, one key challenge with the use of AI in this context is the lack of transparency and explainability in the decision-making process. In many cases, AI algorithms operate as “black boxes,” making it difficult for stakeholders to understand how and why decisions are made.

Explainable AI (XAI) is a growing field that aims to address this challenge by creating AI systems that are transparent, interpretable, and can provide explanations for their decisions. In the context of automated resume screening, XAI can help to ensure that the decisions made by AI systems are fair, unbiased, and in compliance with legal and ethical standards.

This thesis aims to explore the use of Explainable AI for automated resume screening, focusing on the development of a transparent and interpretable AI system that can provide explanations for its decision-making process. The study will investigate the potential benefits and challenges of using XAI in the recruitment process, and will provide practical recommendations for implementing XAI systems in real-world settings.

Table of Contents

Chapter 1: 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 2: Literature Review
2.1 Overview of automated resume screening
2.2 The use of AI in recruitment
2.3 Explainable AI (XAI)
2.4 Importance of transparency and explainability in AI systems
2.5 Challenges in implementing XAI for automated resume screening
2.6 Ethical and legal considerations in AI-based recruitment
2.7 Existing research on XAI for automated resume screening
2.8 Best practices for implementing XAI in recruitment
2.9 Case studies of XAI systems in recruitment
2.10 Summary of key findings

Chapter 3: Research Methodology
3.1 Research design
3.2 Data collection methods
3.3 Data analysis techniques
3.4 Selection of AI algorithms
3.5 Development of XAI system
3.6 Evaluation of XAI system
3.7 Participant recruitment
3.8 Ethical considerations
3.9 Limitations of the study
3.10 Summary of research methodology

Chapter 4: Discussion of Findings
4.1 Overview of research findings
4.2 Analysis of XAI system performance
4.3 Comparison of XAI and traditional AI systems
4.4 User feedback on XAI system
4.5 Implications for recruitment practices
4.6 Recommendations for implementation
4.7 Future research directions
4.8 Conclusion

Chapter 5: Conclusion and Summary
5.1 Summary of key findings
5.2 Contribution to the field of XAI
5.3 Practical implications for recruitment industry
5.4 Limitations of the study
5.5 Recommendations for future research
5.6 Conclusion

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