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

2025-06-19

AI Summary

This study examines how AI-generated labels affect public perceptions of news accuracy and their spillover effects—including policy interest, policy support, and concern about online misinformation. Method: A nationally representative randomized controlled survey experiment (n = 3,861) was conducted to assess causal effects of explicit AI labeling on these outcomes. Contribution/Results: Explicit AI labeling significantly reduces perceived news accuracy and marginally diminishes policy interest, but exerts no statistically significant effect on policy support or general information anxiety. Critically, this is the first study to empirically delineate the bounded nature of AI-labeling effects using high-quality probability sampling: its adverse impacts are limited in magnitude and can be meaningfully attenuated by increasing the salience of AI usage. These findings challenge the prevailing “algorithm aversion” hypothesis and provide evidence-based guidance for platform labeling policies and risk communication strategies.

Technology Category

• Application Domains: Misinformation & Fake News
• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)
• Humans and AI: Voting

Application Category

• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

Explicit labeling of online content produced by artificial intelligence (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.

Problem

• AI labeling reduces perceived accuracy of online content
• AI labeling has limited effects on policy support
• AI labeling does not increase concerns about misinformation

Innovation

• AI labeling reduces perceived accuracy
• Survey experiment tests labeling effects
• Algorithm aversion effects are limited