DATA for: AI versus humans as authority figures: Evidence from a rule-compliance experiment

DATA AND ANALYSIS FILES for: "Can AI tell us what to do? Experimental evidence on rule compliance", by Dominik Suri, Simon Gächter, and Sebastian Kube. ABSTRACT: Interactions between humans and artificial intelligence are becoming increasingly common across social and economic domains, yet the behavioral consequences of AI as an authority figure remain poorly understood. We investigate whether individuals differ in their willingness to comply with arbitrary rules depending on whether these rules are attributed to an AI agent (ChatGPT) or to a fellow human. In a between-subject design, 977 US Prolific users completed the coins task, in which they could earn a monetary payoff by stopping the disappearance of coins at any time, but were instructed to wait for a signal before doing so. There are no conventional reasons to follow this rule: complying is costly and nobody is harmed by non-compliance. Despite this, we find high levels of rule compliance: 64.3\% followed a rule set by ChatGPT and 63.9\% complied with a human rule setter. Personal, descriptive, and normative beliefs about rule following, as well as compliance conditional on these beliefs, are also unaffected by the rule’s origin. However, subjective social closeness to the rule setter significantly predicts how participants condition their behavior on social expectations: when participants perceive the rule setter as subjectively closer, conditional compliance is higher and associated beliefs are stronger, irrespective of whether the rule setter is human or AI.