Algorithmic Bias in AI-Based Recruitment Systems: Implications for Fair Hiring Practices

Artificial Intelligence (AI) has become an integral component of modern recruitment by enabling organizations to automate candidate sourcing, resume screening, and selection processes. While AI-driven recruitment systems improve efficiency and decision-making, they also introduce significant concerns regarding algorithmic bias, fairness, transparency, and accountability. This study presents a conceptual and systematic review of contemporary literature published between 2020 and 2026 to examine the nature, sources, and implications of algorithmic bias in AI-based recruitment systems. The review synthesizes multidisciplinary evidence from Human Resource Management, Artificial Intelligence, Business Analytics, Organizational Behaviour, and AI Ethics to identify key factors influencing fair hiring practices. The findings indicate that algorithmic bias primarily arises from biased training data, model design, proxy variables, and organizational implementation practices, potentially leading to discriminatory recruitment outcomes and reduced workforce diversity. The study further highlights the importance of explainable AI, ethical governance, human oversight, and continuous bias monitoring in promoting transparent and accountable recruitment systems. Particular attention is given to the emerging Indian context alongside global developments in AI governance. The study proposes a conceptual framework linking algorithmic bias, transparency, organizational trust, and recruitment outcomes while providing practical recommendations for HR professionals and organizations seeking to implement responsible AI-driven recruitment and support equitable hiring practices.