Journal of Advances in Mathematics and Computer Science

Aims: This study aims to explore the ethical challenges and privacy concerns associated with the use of big data in recruitment processes, focusing on algorithmic bias, data privacy, and fairness in hiring practices.

Study Design: The research employs a mixed-methods design, integrating qualitative interviews with HR professionals and quantitative data analysis to assess the implications of big data utilization in recruitment.

Place and Duration of Study: The study was conducted across various organizations, focusing on their recruitment practices, over six months.

Methodology: Qualitative interviews were conducted with HR professionals to gather insights on real-world experiences related to ethical challenges in recruitment. Additionally, a quantitative analysis of recruitment algorithms was performed to identify prevalent biases and their impact on hiring decisions, using statistical evidence to highlight significant findings.

Results: The findings reveal that algorithmic bias is a profound issue in recruitment, with 62% of surveyed HR professionals acknowledging its influence on hiring decisions. Moreover, significant concerns regarding data privacy emerged, with 75% of respondents indicating that handling sensitive candidate information lacks adequate safeguards, increasing the risk of unauthorized access.

Conclusion: The study concludes that while big data enhances recruitment efficiency, it simultaneously raises critical ethical challenges that must be addressed. Organizations need to implement robust frameworks to ensure fairness and transparency, thereby safeguarding candidates' privacy and fostering equitable hiring practices. These insights provide crucial guidance for HR professionals seeking to navigate the complexities of big data in recruitment.