Generative AI and Disinformation| Synthetic Diversity: Examining the Effects of Ethnic Targeting Using AI-Generated Political Ads
Abstract
Can deceptive use of generative artificial intelligence (AI) in advertisements influence support for political parties? Drawing on a survey experiment based on the tactics of a secessionist movement in South Africa, this study assesses whether targeting out-group ethnicities with diverse deepfake avatars can alter voter support. We iterate from existing work by varying the ethnicity of the presenter to assess whether AI-assisted alterations to author ethnicities can mobilize support for minority parties. The results indicate that coethnic speakers are more effective at mobilizing both personal and perceptions of societal support, even when content includes AI labels. Additional analyses indicate that AI literacy scores better predict respondents’ abilities to identify synthetic content than digital literacy and that coethnic avatars reduce skepticism toward AI-assisted messaging campaigns. The results of the study have implications for researchers and policy makers interested in understanding how synthetic media may be used for deceptive purposes in political campaigns.
Study specs
Survey experiment targeting voter responses to AI-generated political ads with varied presenter ethnicities, including analysis of AI literacy versus digital literacy.
- Institution
- University of Zurich
- Study Type
- Experimental Study
- Year
- 2025
- Human Data Platform
- Prolific
- Source
- View Source Google Scholar
Measured Outcomes
Effectiveness of coethnic versus out-group ethnic AI-generated avatars in mobilizing voter support and the role of AI literacy in detecting synthetic content.
Peer Review & Critical Discussion
Potential Selection Bias in 2023 Cohort
The participant pool shows a concerning overrepresentation of users from high-income demographics. Looking at Table 3, we can see that 78% of respondents had annual incomes above $75k, which significantly limits the generalizability of these findings to broader populations.
Non-naive Participants Issue
I've noticed a methodological concern regarding participant naivety. Given that Prolific users often complete multiple studies, there's a real risk that participants had prior exposure to similar experimental paradigms, which could confound the results.
RLHF Applicability to This Study Design
The implications for RLHF training pipelines are understated. If we accept the authors' conclusions about preference stability, this has direct consequences for how we should structure reward model training. The temporal decay effect described in Section 4.2 is particularly relevant.
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