Labeling messages as AI-generated does not reduce their persuasive effects
Isabel O. Gallegos; Chen Shani; Weiyan Shi; Federico Bianchi; Izzy Gainsburg; Dan Jurafsky; Robb Willer · 2026 · PNAS Nexus
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
Abstract
Abstract As generative AI enables the creation and dissemination of information at massive scale and speed, it is increasingly important to understand how people perceive AI-generated content. One prominent policy proposal requires explicitly labeling AI-generated content to increase transparency and encourage critical thinking about the information, but prior research has not yet tested the effects of such labels. To address this gap, we conducted a survey experiment (N=1,601) on a diverse sample of Americans, presenting participants with an AI-generated message about several public policies
Abstract by Isabel O. Gallegos; Chen Shani; Weiyan Shi; Federico Bianchi; Izzy Gainsburg; Dan Jurafsky; Robb Willer, PNAS Nexus (2026) — licensed CC BY 4.0.
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Metadata source: OpenAlex · DOI 10.1093/pnasnexus/pgag008