Transparency Is Not Enough: Political Identity and the Acceptance of AI-Generated Governance Information

Political transparency promotes accountability only if citizens are willing to accept the information provided. As artificial intelligence (AI) increasingly mediates the production and evaluation of public information, an important question is whether AI-generated political information remains subject to partisan filtering. We examine this question using an online survey experiment in Taiwan that varies two potential partisan cues surrounding an AI-generated legislative-performance evaluation: the partisan camp of the legislator receiving a favorable evaluation and the partisan alignment of the media outlet associated with the AI system. The results show that acceptance is strongly shaped by congruence with the favorably evaluated legislator. Respondents are more accepting when the legislator belongs to their own partisan camp and substantially less accepting when the legislator belongs to an opposing camp. By contrast, congruence with the media outlet associated with the AI system has no statistically significant effect. Further, prior generative-AI usage is positively associated with acceptance, but this association is significantly weaker when the favorably evaluated legislator is politically congruent with the respondent. These findings demonstrate that AI-mediated transparency does not eliminate partisan filtering and suggest that the partisan implications of AI-generated outputs may matter more than the political alignment of the institutions associated with their production.