Persuading voters using human-artificial intelligence dialogues
Abstract
There is great public concern about the potential use of generative artificial intelligence (AI) for political persuasion and the resulting impacts on elections and democracy1,2,3,4,5,6. We inform these concerns using pre-registered experiments to assess the ability of large language models to influence voter attitudes. In the context of the 2024 US presidential election, the 2025 Canadian federal election and the 2025 Polish presidential election, we assigned participants randomly to have a conversation with an AI model that advocated for one of the top two candidates. We observed significant treatment effects on candidate preference that are larger than typically observed from traditional video advertisements7,8,9. We also document large persuasion effects on Massachusetts residents’ support for a ballot measure legalizing psychedelics. Examining the persuasion strategies9 used by the models indicates that they persuade with relevant facts and evidence, rather than using sophisticated psychological persuasion techniques. Not all facts and evidence presented, however, were accurate; across all three countries, the AI models advocating for candidates on the political right made more inaccurate claims. Together, these findings highlight the potential for AI to influence voters and the important role it might play in future elections.
Study specs
Pre-registered experiments where participants interacted with AI advocating for one of two candidates or a ballot measure, examining persuasion strategies and effects across three elections.
- Authors
- H Lin,G Czarnek,B Lewis,JP White,AJ Berinsky
- Institution
- Massachusetts Institute of Technology
- Discipline
- Political Science,Artificial Intelligence
- Study Type
- Experimental Study
- Year
- 2025
- Human Data Platform
- Prolific
- Source
- View Source Google Scholar
Measured Outcomes
Influence of AI-generated dialogues on voter attitudes and preferences, including analysis of persuasion strategies and accuracy of presented information.
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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