Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

5-2022

Abstract

Requesters on crowdsourcing platforms like Amazon Mechanical Turk (AMT) compensate workers inadequately. One potential reason for the underpayment is that the AMT’s requester interface provides limited information about estimated wages, preventing requesters from knowing if they are offering a fair piece-rate reward. To assess if presenting wage information affects requesters’ reward setting behaviors, we conducted a controlled study with 63 participants. We had three levels for a between-subjects factor in a mixed design study, where we provided participants with: no wage information, wage point estimate, and wage distribution. Each participant had three stages of adjusting the reward and controlling the estimated wage. Our analysis with Bayesian growth curve modeling suggests that the estimated wage derived from the participant-set reward increased from $2.56/h to $2.69/h and $2.33/h to $2.74/h when we provided point estimate and distribution information respectively. The wage decreased from $2.06/h to $1.99/h in the control condition.

Keywords

Human-centered computing, Human computer interaction (HCI)

Discipline

Graphics and Human Computer Interfaces | Software Engineering

Research Areas

Software and Cyber-Physical Systems

Publication

CHI EA '22: Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems, New Orleans, April 29 - May 5

First Page

1

Last Page

6

ISBN

9781450391566

Identifier

10.1145/3491101.3519660

Publisher

ACM

City or Country

New Orleans, LA, USA

Additional URL

https://doi.org/10.1145/3491101.3519660

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