AI-POWERED RECRUITMENT: BALANCING EFFICIENCY WITH BIAS PREVENTION
Keywords:
Artificial Intelligence; Recruitment; Algorithmic bias; HR technology; Talent acquisition; Machine learning; Hiring automation; Diversity and inclusion; Predictive analytics; Ethical AIAbstract
Artificial Intelligence has rapidly transformed recruitment practices, promising unprecedented efficiency in candidate sourcing, screening, and selection. However, the deployment of AI systems in hiring has raised critical concerns about algorithmic bias, fairness, and the potential perpetuation of historical discrimination. This research examines the current state of AI-powered recruitment, analyzing both its efficiency gains and bias risks through empirical data, case studies, and comparative analysis. The findings reveal that while AI can significantly reduce time-to-hire and improve candidate quality when properly implemented, organizations must adopt rigorous bias detection and mitigation strategies to ensure equitable hiring outcomes. This paper provides evidence-based recommendations for HR professionals seeking to leverage AI's benefits while maintaining ethical recruitment standards.
