External attribution to economic inequality increases algorithm preference

Five studies comprising seven experiments (N = 961)showed that attributing inequality to external factors increased individuals’preference for algorithmic decisions. This effect was observed across multiple contexts (Study 1A: healthcare; Study 1B: recruitment; Study 1C: education) and diverse methods (Study 2), and was driven by the psychological mechanism of decreased system-justifying beliefs (Studies 3 and 4). Furthermore, Study 5 explored the social consequences of this effect, showing that externally attributing individuals perceived higher fairness from algorithmic decisions in unequal contexts, whereas internally attributing individuals perceived reduced fairness. These findings indicate that the effectiveness of algorithms in mitigating inequalitydepends on contextual factors and individual cognitive styles, rather thanremaining universally applicable. Future research should attend to potential backfire effects of algorithmic interventions, particularly the risk of undermining individuals’perceptions of fairness in unequal contexts.