Applicants’ Fairness Perceptions of Algorithm-Driven Hiring Procedures

Maude Lavanchy (), Patrick Reichert, Jayanth Narayanan () and Krishna Savani ()
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Maude Lavanchy: IMD International Institute for Management Development
Jayanth Narayanan: NUS Business School
Krishna Savani: The Hong Kong Polytechnic University

Journal of Business Ethics, 2023, vol. 188, issue 1, No 8, 125-150

Abstract: Abstract Despite the rapid adoption of technology in human resource departments, there is little empirical work that examines the potential challenges of algorithmic decision-making in the recruitment process. In this paper, we take the perspective of job applicants and examine how they perceive the use of algorithms in selection and recruitment. Across four studies on Amazon Mechanical Turk, we show that people in the role of a job applicant perceive algorithm-driven recruitment processes as less fair compared to human only or algorithm-assisted human processes. This effect persists regardless of whether the outcome is favorable to the applicant or not. A potential mechanism underlying algorithm resistance is the belief that algorithms will not be able to recognize their uniqueness as a candidate. Although the use of algorithms has several benefits for organizations such as improved efficiency and bias reduction, our results highlight a potential cost of using them to screen potential employees during recruitment.

Keywords: Algorithms; Organizational justice; Fairness; Applicant reactions to selection; Selection; Recruitment (search for similar items in EconPapers)
JEL-codes: J20 L20 M12 M51 O15 (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s10551-022-05320-w

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