The Impact of AI Hiring Processes on Applicants’ Perceptions of Justice and Organizational Attractiveness

As organizations increasingly use artificial intelligence (AI) in hiring, understanding how applicants perceive the fairness of algorithmic evaluation is critical. Drawing on organizational justice theory and Gilliland's (1993) applicant reactions model, this study uses a 3 × 2 between-subjects experimental vignette design (N = 419) to examine how decision-maker (human, AI, or AI with explanation) and hiring stage (resume screening or pre-recorded video interview) affect justice perceptions. Results showed that applicants perceived AI-based selection as lower in procedural, interpersonal, and informational justice than human-based selection. However, explaining how AI evaluates candidates and emphasizing its consistency did not improve justice perceptions, and hiring stage did not moderate these effects. Additionally, all three justice dimensions uniquely mediated the effects of human-versus-AI evaluation on both organizational attractiveness and job-pursuit intentions. These findings highlight potential costs for attracting talent in AI-enabled recruitment and underscore the challenge of deploying AI without undermining candidate experience.