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Article . 2025
License: CC BY SA
Data sources: ZENODO
ZENODO
Article . 2025
License: CC BY SA
Data sources: Datacite
ZENODO
Article . 2025
License: CC BY SA
Data sources: Datacite
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How AI Can Improve Recruitment Efficiency and HR Functions

Authors: Nair, Aishwaria Rajesh;

How AI Can Improve Recruitment Efficiency and HR Functions

Abstract

Abstract Artificial intelligence (AI) is transforming human resources (HR) and recruiting by automating repetitive tasks, improving applicant matching, reducing time-to-hire, and enabling strategic HR work. This study synthesizes prior research, discusses ethical, legal, and operational risks, outlines assessment metrics, and proposes an integrated AI-driven HR paradigm. In our view, well-designed AI systems can significantly increase operational efficiency and improve the candidate experience when applied transparently, with minimal prejudice, and after careful assessment. This study explores how AI-driven systems—such as applicant tracking systems (ATS), chatbots, predictive analytics, and resume screening tools—contribute to increased recruitment efficiency and strategic HR outcomes. It examines the integration of AI frameworks that reduce time-to-hire, improve candidate-job matching accuracy, and enhance the overall candidate experience. The paper also emphasizes ethical considerations, focusing on fairness, transparency, and bias mitigation in algorithmic decision-making. Findings indicate that when implemented responsibly with human oversight and governance mechanisms, AI technologies can significantly enhance operational productivity and foster equitable hiring practices. Future research should explore scalable, culturally adaptive AI models and standardized evaluation metrics to ensure fairness and accountability in global recruitment contexts

Keywords

artificial intelligence, recruitment, applicant tracking system, resume screening, HR automation, bias mitigation, evaluation metrics

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    popularity
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    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Green