Artificial intelligence can persuade humans on political issues
Abstract
The emergence of large language models (LLMs) that leverage deep learning and web-scale corpora has made it possible for artificial intelligence (AI) to tackle many higher-order cognitive tasks, with critical implications for industry, government, and labor markets in the US and globally. Here, we investigate whether existing, openly-available LLMs are capable of influencing humans’ political attitudes, an ability recently regarded as the unique purview of other humans. Across three preregistered experiments featuring diverse samples of Americans (total N=4,836), we find consistent evidence that messages generated by LLMs (OpenAI’s GPT 3 and 3.5 models) are able to persuade humans across a number of policy issues, including highly polarized issues, such as an assault weapon ban, a carbon tax, and a paid parental-leave program. Overall, LLM-generated messages were as persuasive as messages crafted by lay humans. Our results show LLMs can persuade humans, even on highly polarized policy issues. As the capacity of LLMs is expected to improve substantially in the near future, these results suggest that LLMs may change political discourse, calling for immediate attention for the identification and regulation of potential misuse of LLMs.
Study specs
- Authors
- H Bai,J Voelkel,J Eichstaedt,R Willer
- Institution
- Stanford University,London Business School,Dartmouth College,Stanford Graduate School of Business
- Discipline
- Political Science,Social Science
- Year
- 2023
- Human Data Platform
- Prolific
- Source
- View Source Google Scholar
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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