Enhancing Resume Parsing System with Explainable AI for Fair and Efficient Hiring

TITLE	Enhancing Resume Parsing System with Explainable AI for Fair and Efficient Hiring
ABSTRACT	In today’s competitive employment environment, Companies and organization receive mountains of resumes for each opening position. Manual screening of each and all resumes are timeconsuming, prone to biasness and mistake, leading to inefficiencies in hiring decisions.[3] Automated Resume Parsing Systems (ARPS), powered by Artificial Intelligence (AI) and Applicant Tracking Systems (ATS), have emerged as solutions to streamline the hiring process. However, these systems have been criticized for inherent biases that disadvantage women and minority candidates.[4] This paper proposes the integration of Explainable AI (XAI) into ARPS to enhance transparency, fairness, and accountability in hiring. XAI can help clarify scoring mechanisms, detect biases, guide candidates, enable audits, and support human reviews. Our research explores the impact of XAI-driven resume parsing in reducing biases while improving hiring efficiency
AUTHOR	Thingbaijam Celina, Swatilin Swain, Tejaswini HJ Student, Dept. of MCA, CMR Institute of Technology, Bengaluru, Karnataka, India
PUBLICATION DATE	2025-09-08
VOLUME	12
DOI	DOI:10.15680/IJARETY.2025.1204071
PDF	71_Enhancing Resume Parsing System with Explainable AI for Fair and Efficient Hiring.pdf
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