[ad_1]

Introduction

The use of predictive analytics and algorithmic decision-making in the hiring and employment process has become increasingly popular in recent years. Employers are turning to data-driven tools to help them make more informed decisions about who to hire and promote. However, the use of these technologies raises a number of legal issues, including concerns about fairness, discrimination, and privacy. This thesis will examine the legal implications of using predictive analytics and algorithms in the hiring and employment process, with a focus on how these technologies intersect with existing anti-discrimination laws and regulations.

1.1 Introduction
1.2 Background of study
1.3 Problem Statement
1.4 Objectives of study
1.5 Limitations 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 The rise of predictive analytics in hiring
2.2 Legal framework for anti-discrimination in employment
2.3 Criticisms of algorithmic decision-making
2.4 Challenges of interpreting algorithmic outputs
2.5 Impact of algorithms on diversity and inclusion
2.6 Case studies of legal challenges in algorithmic hiring
2.7 Ethical considerations in algorithmic decision-making
2.8 Regulatory responses to algorithmic hiring
2.9 Best practices for using predictive analytics in hiring
2.10 Future trends in algorithmic decision-making

Chapter 3: Research Methodology
3.1 Research design
3.2 Data collection methods
3.3 Data analysis techniques
3.4 Sampling strategy
3.5 Ethical considerations
3.6 Validity and reliability of research findings
3.7 Research limitations
3.8 Research implications

Chapter 4: Discussion of Findings
4.1 Overview of legal issues in predictive analytics
4.2 Analysis of case studies
4.3 Comparison of regulatory responses
4.4 Implications for employers
4.5 Recommendations for policy-makers
4.6 Addressing bias and discrimination in algorithmic hiring
4.7 Ensuring transparency and accountability
4.8 Balancing efficiency with fairness

Chapter 5: Conclusion and Summary
5.1 Recap of key findings
5.2 Implications for future research
5.3 Recommendations for practitioners
5.4 Closing remarks

Thesis Overview:

The use of predictive analytics and algorithmic decision-making in the hiring and employment process has generated significant interest and debate in the legal, academic, and business communities. This thesis seeks to analyze the legal issues surrounding the use of these technologies, with a specific focus on how they intersect with existing anti-discrimination laws and regulations.

Chapter 1 provides an introduction to the topic, outlining the background of the study, problem statement, objectives, limitations, scope, significance, structure, and definition of key terms. Chapter 2 conducts a comprehensive literature review, examining the rise of predictive analytics in hiring, legal frameworks for anti-discrimination in employment, criticisms of algorithmic decision-making, and ethical considerations in algorithmic hiring.

Chapter 3 details the research methodology, including the research design, data collection methods, analysis techniques, sampling strategy, ethical considerations, validity and reliability of findings, limitations, and implications. Chapter 4 delves into a discussion of findings, exploring legal issues in predictive analytics, case studies, regulatory responses, implications for employers, policy recommendations, bias and discrimination considerations, transparency and accountability measures, and balancing efficiency with fairness.

Chapter 5 concludes the thesis with a summary of key findings, implications for future research, recommendations for practitioners, and closing remarks. This thesis aims to contribute to the ongoing dialogue surrounding the use of predictive analytics and algorithmic decision-making in the hiring and employment process, highlighting the importance of ensuring fairness, transparency, and accountability in the use of these technologies.

[ad_2]

---

Purchase Detail

Download the complete project materials to this project with Abstract, Chapters 1 – 5, References and Appendix (Questionaire, Charts, etc), Click Here to place an order via whatsapp. Got question or enquiry; Click here to chat us up via Whatsapp.
You can also call 08111770269 or +2348059541956 to place an order or use the whatsapp button below to chat us up.
Bank details are stated below.

Bank: UBA
Account No: 1021412898
Account Name: Starnet Innovations Limited

The Blazingprojects Mobile App

Download and install the Blazingprojects Mobile App from Google Play to enjoy over 50,000 project topics and materials from 73 departments, completely offline (no internet needed) with monthly update to topics, click here to install.