AI-Based Resume Screening Systems: A Technical Evaluation of Algorithmic Efficiency, Bias, and Implementation Challenges

Ansari Aamina Jasimuddin

Abstract: In 2018, Amazon quietly discontinued its AI recruiting tool after discovering it systematically favored male candidates over equally qualified women. This incident exposed critical vulnerabilities in automated hiring systems that promised to eliminate human bias. This study examines AI-based resume screening through technical evaluation of algorithmic efficiency, fairness concerns, and implementation barriers. Data collected from 16 respondents reveal strong efficiency recognition with 87.5% acknowledging time savings, yet significant fairness concerns persist as 81.25% worry about algorithmic bias. Interestingly, 87.5% prefer hybrid models combining AI with human oversight rather than full automation. Findings suggest successful implementation requires algorithm transparency, bias mitigation, and human- AI collaboration frameworks.

Keywords: artificial intelligence, resume screening, algorithmic bias, machine learning, recruitment technology, NLP, fairness in AI

How to Cite?: Ansari Aamina Jasimuddin, "AI-Based Resume Screening Systems: A Technical Evaluation of Algorithmic Efficiency, Bias, and Implementation Challenges", Volume 14 Issue 12, December 2025, International Journal of Science and Research (IJSR), Pages: 1747-1751, https://www.ijsr.net/getabstract.php?paperid=SR251221124427, DOI: https://dx.doi.org/10.21275/SR251221124427