New AI Technology for Selection Interviews

This study aims to examine the roles of trust in artificial intelligence (AI) and augmentation in moderating the effect of fairness on applicant satisfaction in a novel selection context of AI selection interview. Field survey data were collected at two time points separated by three to five weeks from a sample of 125 university applicants who were assessed by AI selection interviews for their industry internships. Moderated regressions were conducted to test the hypotheses. Results in the new technology setting reaffirm the positive relationship between fairness and applicant satisfaction on AI interviews, but not the compensatory effect between fairness and outcome favorability. Results also reveal that procedural fairness is most effective when trust is low and augmentation present. By contrast, interactional fairness is most effective either when trust is high and augmentation absent, or when trust is low and augmentation present. This study extends the research on fairness to the novel context of AI technology. Drawing on uncertainty management theory of fairness and findings from a field survey, this study sheds light on how procedural and interactional fairness are bounded by applicants’ trust in AI and augmentation, rather than interview outcomes with the new AI technology.