Author(s)

Chengli Zhang ¹, Shen Li ¹, Yifan Liu ¹, Yingying Li ¹, Jiaqin Zhu ¹

Affiliation(s)

¹ University of Shanghai for Science and Technology, Shanghai, 200093, China

Corresponding Author

Chengli Zhang

ABSTRACT

As organizations increasingly adopt Artificial Intelligence (AI) technologies in their hiring processes, the issues of fairness and efficiency have gained significant attention. The potential risks of bias and discrimination may disproportionately affect vulnerable groups. Clearly defining fairness and efficiency is crucial, as it provides measurable criteria for assessing and mitigating bias. This ensures that AI recruitment systems do not worsen existing inequalities, but instead foster equal opportunities for all job candidates, while enhancing recruitment efficiency. This paper categorizes and examines AI recruitment use cases from the perspectives of fairness and efficiency across four dimensions. As AI technology in recruitment is still in its early stages, the topic remains a frontier area in academic research. To provide valuable references for future research and strengthen the theoretical foundation, this paper offers a comprehensive review of the available literature.

KEYWORDS

Equity, Recruitment, Hiring Processes, Efficiency, Artificial Intelligence

CITE THIS PAPER

Chengli Zhang, Shen Li, Yifan Liu, Yingying Li, Jiaqin Zhu, Artificial Intelligence Recruitment—A Literature Review Based on Equity and Efficiency Perspectives. Journal of Human Resource Development (2025) Vol. 7: 15-21. DOI: http://dx.doi.org/10.23977/jhrd.2025.070103.

REFERENCES

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