AI labeling reduces the perceived accuracy of online content but has limited broader effects (2506.16202v1)

Abstract: Explicit labeling of online content produced by AI is a widely mooted policy for ensuring transparency and promoting public confidence. Yet little is known about the scope of AI labeling effects on public assessments of labeled content. We contribute new evidence on this question from a survey experiment using a high-quality nationally representative probability sample (n = 3,861). First, we demonstrate that explicit AI labeling of a news article about a proposed public policy reduces its perceived accuracy. Second, we test whether there are spillover effects in terms of policy interest, policy support, and general concerns about online misinformation. We find that AI labeling reduces interest in the policy, but neither influences support for the policy nor triggers general concerns about online misinformation. We further find that increasing the salience of AI use reduces the negative impact of AI labeling on perceived accuracy, while one-sided versus two-sided framing of the policy has no moderating effect. Overall, our findings suggest that the effects of algorithm aversion induced by AI labeling of online content are limited in scope.