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

TL;DR

This paper investigates how explicit AI labeling of online content influences public assessments and related attitudes. Using a nationally representative UK probability sample ($n=3{,}861$) and a $2\times2\times2$ randomized design, it manipulates AI labeling, salience, and framing and measures perceived accuracy, policy interest, policy support, and misinformation concern. The main finding is that AI labeling reduces perceived accuracy and interest in the policy discussed, but has limited effects on policy support and general misinformation concerns; salience can modestly mitigate the accuracy penalty, while framing yields little moderation. The study suggests transparency policies may aid trust without broadly diminishing policy message effectiveness, though contextualizing AI use could mitigate public skepticism.

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.

Figures (9)

• Figure 1: Baseline Awareness of Generative AI in the Sample.
• Figure 2: Effects of AI labeling.
• Figure 3: Effects of Salience Enhancement.
• Figure 4: AI Labeling × Salience Enhancement Interaction Effects.
• Figure 5: Embedded image for the policy article with one-sided framing.