Can Interviewees Fake Out AI? Comparing the Susceptibility and Mechanisms of Faking Across Self-Reports, Human Interview Ratings, and AI Interview Ratings

Artificial intelligence (AI) is increasingly used to score employment interviews in the early stages of the hiring process, but AI algorithms may be particularly prone to interviewee faking. Our study compared the extent to which people can improve their scores on self-report scales, structured and less structured human interview ratings, and AI interview ratings. Further, we replicate and extend prior research by examining how interviewee abilities and impression management tactics influence score inflation across scoring methods. Participants (N = 152) completed simulated, asynchronous interviews in honest and applicant-like conditions in a within-subjects design. The AI algorithms in the study were trained to replicate question-level structured interview ratings. Participants' scores increased most on self-reports (overall Cohen's d = 0.62) and least on AI interview ratings (overall Cohen's d = 0.14), although AI score increases were similar to those observed for human interview ratings (overall Cohen's d = 0.22). On average, across conditions, AI interview ratings converged more strongly with structured human ratings based on behaviorally anchored rating scales than with less structured human ratings. Verbal ability only predicted score improvement on self-reports, while increased use of honest defensive impression management tactics predicted improvement in AI and less structured human interview scores. Ability to identify criteria did not predict score improvement. Overall, these AI interview scores behaved similarly to structured human ratings. We discuss future possibilities for investigating faking in AI interviews, given that interviewees may try to “game” the system when aware that they are being evaluated by AI.