Perceived political bias in large language models reduces persuasive abilities

Abstract

Abstract Conversational AI has been proposed as a scalable way to correct public misconceptions, yet its effectiveness may depend on perceptions of political neutrality. As large language models become objects of partisan conflict, political elites increasingly portray them as ideologically aligned. We test whether such credibility attacks reduce persuasion by conversational AI. In a preregistered US survey experiment (N = 2,144), participants completed a three-round conversation with ChatGPT (GPT-4.1) about an economic policy misconception they personally held. The conversation corrected misconceptions across all conditions, but a short message indicating that the model was biased against the respondent's political party attenuated this correction by 28% relative to a neutral control and reduced trust in and willingness to use AI. Even under this adversarial framing, the majority of the persuasion effect survived. Pairwise transcript comparisons suggest a behavioral mechanism: treated respondents pushed back more and engaged less receptively rather than disengaging. These findings identify perceived partisan alignment as a user-side lever that conditions, but does not eliminate, the persuasive power of conversational AI.