Labeling messages as AI-generated does not reduce their persuasive effects
Stanford Medicine · Stanford University · +1 more institution
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 (e.g. allowing colleges to pay student-athletes), randomly assigning whether participants were told the message was…
Citation impact
- FWCI
- 90.91
- Percentile
- 100%
- References
- 8
Authors
7Topics & keywords
- Transparency (behavior)
- Variety (cybernetics)
- Affect (linguistics)
- Sample (material)
- Motivated reasoning
- Persuasion
- Scale (ratio)