When Machine and Bandwagon Heuristics Compete: Understanding Users’ Response to Conflicting AI and Crowdsourced Fact-Checking
John A. Banas, Nicholas A. Palomares, Adam S. Richards, David M. Keating, Nick Joyce, Stephen A. Rains
Abstract
Abstract Three experiments tested if the machine and bandwagon heuristics moderate beliefs in fact-checked claims under different conditions of human/machine (dis)agreement and of transparency of the fact-checking system. Across experiments, people were more likely to align their belief in the claim when artificial intelligence (AI) and crowdsourcing agents’ fact-checks were congruent rather than incongruent. The heuristics provided further nuance to the processes, especially as a particular agent suggested truth verdicts. That is, people with stronger belief in the machine heuristic were more likely to judge the claim as true when an AI agent’s fact-check suggested the claim was likely true but not false; likewise, people with stronger belief in the bandwagon heuristic were more likely to judge the claim as true when the crowdsource agent fact-checked the claim to be true but not false. Making the system more transparent to users does not appear to change results.