Algorithmic Bias in AI-Driven Recruitment Systems: A Systematic Review of Implications for Fairness and Diversity in Talent Acquisition

Abstract

The integration of artificial intelligence (AI) in recruitment and talent acquisition has transformed human resource management by enhancing efficiency, scalability, and data-driven decision-making. However, the growing reliance on AI-based hiring systems has raised critical concerns regarding algorithmic bias and its implications for fairness and diversity. This secondary research paper aims to systematically review existing literature on algorithmic bias in AI-driven recruitment systems, with a particular focus on its sources, impacts, and mitigation strategies.
The study synthesises findings from academic journals, industry reports, and case studies to examine how biases emerge in AI models due to historical data, flawed algorithm design, and lack of transparency in decision-making processes. It highlights that AI systems, when trained on biased datasets, may unintentionally reinforce existing inequalities related to gender, education, or socioeconomic background. The review also explores the concept of “black-box” algorithms, which limit accountability and hinder trust among stakeholders.
Furthermore, the paper evaluates various strategies proposed in the literature to address algorithmic bias, including data diversification, algorithmic audits, ethical AI frameworks, and human oversight in recruitment decisions. Special attention is given to the relevance of these issues in emerging economies, where AI adoption is rapidly increasing but regulatory frameworks are still evolving.
The findings suggest that while AI has the potential to improve recruitment efficiency, its unchecked implementation may compromise fairness and inclusivity. The study concludes by emphasising the need for responsible AI governance and transparent hiring practices to ensure equitable talent acquisition in the digital era.