Applicants’ Fairness Perceptions of Algorithm-Driven Hiring Procedures

Maude Lavanchy, Patrick Reichert, Jayanth Narayanan, Krishna Savani

Research output: Journal article publicationJournal articleAcademic researchpeer-review

10 Citations (Scopus)

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.

Original languageEnglish
JournalJournal of Business Ethics
DOIs
Publication statusPublished - 12 Jan 2023

Keywords

  • Algorithms
  • Applicant reactions to selection
  • Fairness
  • Organizational justice
  • Recruitment
  • Selection

ASJC Scopus subject areas

  • Business and International Management
  • General Business,Management and Accounting
  • Arts and Humanities (miscellaneous)
  • Economics and Econometrics
  • Law

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