Applicant perceptions of fairness and equity in selection

Machine-learning algorithms provide organizations with the opportunity to quickly and efficiently process information about potential employees while reducing costs associated with selection and turnover. However, any bias or error present in the programming as a result of information drawn from historically biased data is evident in the algorithm output (Illingworth, 2015). Additionally, human decision-makers rely on deductive reasoning by creating hypotheses, examining several variables, and drawing formal conclusions between the predictor variables and outcomes (Tambe et al., 2019). Recently, there has been growing fairness and equity concerns about the risks associated with the use of algorithms in selection processes from applicants; existing research has not fully addressed differences in applicant perceptions towards algorithmic or human decision-makers in the selection process. The present experiment analyzes applicant reactions to the selection process to understand whether algorithmic or human hiring decision-makers influence perceptions of fairness and equity and ultimately organizational attraction and job pursuit intentions. A t-test will capture participant reactions toward algorithmic and human-decision makers in the selection process after viewing a randomly assigned vignette about an organization’s review of application materials. Additionally, we are also measuring perceptions of organizational attraction and job pursuit intentions using correlational analyses and a mediation analysis using PROCESS