AI labeling reduces the perceived accuracy of online content but has limited broader effects

TL;DR

AI labeling of online content decreases perceived accuracy and policy interest but has limited effects on broader attitudes, with salience and framing influencing perceptions.

Contribution

This study provides empirical evidence on how AI labeling impacts public perception and attitudes, highlighting the limited scope of its effects and the importance of contextualization.

Findings

AI labeling reduces perceived accuracy of news articles.

AI labeling decreases interest in the associated policy.

Salience of AI use mitigates negative perception effects.

Abstract

Explicit labeling of online content produced by artificial intelligence (AI) is a widely discussed 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 (\emph{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 and that transparency policies may benefit from contextualizing AI use to mitigate unintended public skepticism.