Turking in the time of COVID
Abstract
On March 16, 2020, the US Government introduced strict social distancing protocols for the United States in an effort to stem the spread of the COVID-19 pandemic. This had an immediate major effect on the job market, with millions of Americans forced to find alternative ways to make a living from home. As online labor markets like Amazon Mechanical Turk (MTurk) play a major role in social science research, concerns have been raised that the pandemic may be reducing the diversity of subjects participating in experiments. Here, we investigate this possibility empirically. Specifically, we look at 15,539 responses gathered in 23 studies run on MTurk between February and July 2020, examining the distribution of gender, age, ethnicity, political preference, and analytic cognitive style. We find notable changes on some of the measures following the imposition of nationwide social distancing: participants are more likely to be less reflective (as measured by the Cognitive Reflection Test), and somewhat less likely to be white, Democrats (traditionally over-represented on MTurk), and experienced with MTurk. Most of these differences are explained by an influx of new participants into the MTurk subject pool who are more diverse and representative – but also less attentive – than previous MTurkers.
Study specs
- Authors
- AA Arechar,DG Rand
- Institution
- Department of Brain and Cognitive Sciences,Massachusetts Institute of Technology,Cambridge,MA,USA
- Discipline
- Behavioral Research Methods
- Year
- 2021
- 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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