Team up with AI or Human? Investigating Candidates’ Self-Categorization as Fluidity and Ingroup-Serving Attribution When Judged by a Human–AI Hybrid Jury

As artificial intelligence (AI) judges are increasingly pervasive in decision-making, it is important to investigate candidates’ reactions to decisions made by human–AI hybrid juries. This study investigates candidates’ attribution of credit for success and blame for failure to the three agents in question: a human judge, an algorithmic judge, and the candidate oneself. An experiment with 3 (jury type: human-dominated, algorithm-dominated, vs. equally dominated) × 2 (decision outcome: positive vs. negative) between-subjects factorial design was conducted, with 346 valid responses. Our findings demonstrate a partial ingroup-serving attribution dependent on the outcome favorability and a significant effect of relative power status within the human–AI hybrid jury on grouping and attribution. This study reflects the fluidity of identity and self-categorization of human users when facing AI and other humans. We propose that people take a utility-oriented glance at AI in multi-agent decision-making situations.