The persuasive effects of political microtargeting in the age of generative artificial intelligence
Abstract
The increasing availability of microtargeted advertising and the accessibility of generative artificial intelligence (AI) tools, such as ChatGPT, have raised concerns about the potential misuse of large language models in scaling microtargeting efforts for political purposes. Recent technological advancements, involving generative AI and personality inference from consumed text, can potentially create a highly scalable “manipulation machine” that targets individuals based on their unique vulnerabilities without requiring human input. This paper presents four studies examining the effectiveness of this putative “manipulation machine.” The results demonstrate that personalized political ads tailored to individuals’ personalities are more effective than nonpersonalized ads (studies 1a and 1b). Additionally, we showcase the feasibility of automatically generating and validating these personalized ads on a large scale (studies 2a and 2b). These findings highlight the potential risks of utilizing AI and microtargeting to craft political messages that resonate with individuals based on their personality traits. This should be an area of concern to ethicists and policy makers.
Study specs
Four studies were conducted, including experiments (studies 1a and 1b) on the effectiveness of personality-tailored ads and feasibility assessments (studies 2a and 2b) of automatic generation and validation of these ads using generative AI and personality inference.
- Authors
- A Simchon,M Edwards,S Lewandowsky
- Institution
- University of Bristol
- Discipline
- Political Science,Psychology
- Study Type
- Experimental Study
- Year
- 2024
- Human Data Platform
- Prolific
- Source
- View Source Google Scholar
Measured Outcomes
Effectiveness of personality-based microtargeted political ads and the scalability of their generation using generative AI tools.
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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