Social Dynamics of AI Adoption
Abstract
Anxiety about falling behind can drive people to embrace emerging technologies with uncertain consequences. We study how social forces shape demand for AI-based learning tools early in the education pipeline. In incentivized experiments with parents—key gatekeepers for children’s AI adoption—we elicit their demand for unrestricted AI tools for teenagers’ education. Parental demand rises with the share of other teenagers using the technology, with social forces increasing willingness to pay for AI by more than 60%. Providing information about potentially adverse effects of unstructured AI use negatively shifts beliefs about the merits of AI, but does not change individual demand. Instead, this information increases parents’ preference for banning AI in schools. Follow-up experiments show that social information has little effect on beliefs about AI quality, perceived skill priorities, or support for bans, suggesting that effects operate through social pressure rather than social learning. Our evidence highlights social pressure driving individual technology adoption despite widespread support for restricting its use.
Study specs
- Authors
- L Bursztyn,A Imas,R Jiménez-Durán,A Leonard
- Institution
- University of Chicago,National Bureau of Economic Research,Booth School of Business,Bocconi University,Stanford University
- Discipline
- Behavioral Economics
- Year
- 2022
- 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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