Americans' Support for AI Development-Measured Daily with Open Data and Methods

Abstract

The rapid development of artificial intelligence should be accompanied by measurement of public sentiment at high temporal resolution. Accordingly, here I present analysis of daily repeated surveys beginning April 18, 2024 (total N=4067). The results indicate that in the population of American adults, support for further development of artificial intelligence was modestly positive and increased a statistically reliable amount over the past year. Female and low-trust respondents reported less support, however, both also displayed growing support over time. Republicans increased support at a faster rate than Democrats, pointing to potential polarization. These findings underscore the need for continuous, high-frequency surveys to accurately track shifts in public opinion on transformative technologies like AI.

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Citations
Research
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Study specs

Authors
JJ Jones
Year
2024
Human Data Platform
Prolific

Peer Review & Critical Discussion

3 threads

Potential Selection Bias in 2023 Cohort

DSJDr. Sarah J.
Verified PhD Candidate
12 replies

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.

2 hours ago

Non-naive Participants Issue

MCM. Chen (OpenAI)
Data Scientist
8 replies

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.

5 hours ago

RLHF Applicability to This Study Design

PRWProf. R. Williams
Verified Researcher
15 replies

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.

1 day ago

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