V3I5P36

A Study on the Effectiveness of AI-Powered Recruitment Platforms in Reducing Hiring Bias and Improving Talent Quality

Thrisha. P^1*,  Dishna Sai²

Abstract

Hiring in today’s organisations is caught between two realities: the well-documented limitations of human judgment in recruitment, and the still-unproven promise of algorithmic alternatives. This study examines the effectiveness of AI-powered recruitment platforms in reducing hiring bias and improving talent quality, comparing technology-driven hiring approaches with conventional, manual-based methods. Using a quantitative research design, primary data were gathered from 99 respondents comprising HR professionals, hiring managers, talent acquisition specialists, and job seekers through a structured questionnaire. The analysis focused on perceived bias reduction, candidate-role fit, diversity of shortlists, time-to-hire efficiency, recruiter confidence, and overall satisfaction across both recruitment approaches. Findings reveal that an overwhelming 84.8% of respondents acknowledged traditional hiring methods as more prone to unconscious bias, while 83.8% recognised AI-driven screening tools as effective in reducing personal bias during candidate selection. Time efficiency emerged as AI’s strongest advantage, with 78.8% confirming significant reductions in time-to-shortlist. Diversity outcomes were also positively viewed, with 70.7% agreeing that AI tools produce more diverse shortlists. However, overall satisfaction with candidate quality remained notably low, with 57.5% expressing dissatisfaction, and a striking 37.4% staying neutral on end- to-end platform effectiveness signalling that AI in recruitment, while promising, has not yet consistently delivered on its full potential. The study concludes that AI-powered recruitment platforms meaningfully advance hiring fairness and operational efficiency, but require deliberate configuration, regular bias audits, and robust human oversight to bridge the gap between their theoretical promise and their measurable real- world impact.

Keywords:

AI Recruitment, Hiring Bias, Talent Quality, Algorithmic Screening, HR Technology, Candidate Experience, Diversity in Hiring, India