Algorithmic Bias in AI-Driven Talent Acquisition: A Secondary Study of Recruitment Practices in Indian IT Corporations

Abstract

The integration of artificial intelligence (AI) in recruitment has significantly transformed talent acquisition processes, particularly within the Indian information technology (IT) sector. Organisations increasingly rely on AI-driven tools such as resume screening systems, predictive analytics, and automated interview platforms to enhance efficiency and decision-making. While these technologies offer advantages like reduced hiring time and improved scalability, they also raise critical concerns regarding algorithmic bias. This study adopts a secondary research approach by analysing existing literature, industry reports, and academic studies to examine the presence and implications of bias in AI-driven recruitment systems.

The research highlights that algorithmic bias often arises from biased training data, historical hiring patterns, and lack of transparency in AI models. Such biases may lead to discriminatory outcomes, particularly affecting candidates based on gender, educational background, or socio-economic status. In the Indian IT context, where diversity and inclusion are becoming strategic priorities, biased AI systems can hinder fair hiring practices and limit access to diverse talent pools.

Furthermore, the study identifies a significant gap in empirical research focused on the Indian corporate environment, as most existing studies are centred on Western contexts. The findings suggest that while AI enhances recruitment efficiency, it requires robust governance frameworks, regular audits, and human oversight to ensure fairness and accountability. The study concludes that organisations must adopt ethical AI practices and transparent decision-making mechanisms to balance technological advancement with inclusive hiring. This research contributes to the growing discourse on responsible AI adoption in human resource management.