The Challenges of Crowd Workers in Rural and Urban America

Claudia Flores-Saviaga, Yuwen Li, Benjamin V. Hanrahan, Jeffrey Bigham, Saiph Savage

Research output: Chapter in Book/Report/Conference proceedingConference contribution

10 Scopus citations


Crowd work has the potential of helping the financial recovery of regions traditionally plagued by a lack of economic opportunities, e.g., rural areas. However, we currently have limited information about the challenges facing crowd workers from rural and super rural areas as they struggle to make a living through crowd work sites. This paper examines the challenges and advantages of rural and super rural Amazon Mechanical Turk (M Turk) crowd workers and contrasts them with those of workers from urban areas. Based on a survey of 421 crowd workers from differing geographic regions in the U.S., we identified how across regions, people struggled with being onboarded into crowd work. We uncovered that despite the inequalities and barriers, rural workers tended to be striving more in micro-tasking than their urban counterparts. We also identified cultural traits, relating to time dimension and individualism, that offer us an insight into crowd workers and the necessary qualities for them to succeed on gig platforms. We finish by providing design implications based on our findings to create more inclusive crowd work platforms and tools.

Original languageEnglish (US)
Title of host publicationHCOMP 2020 - Proceedings of the 8th AAAI Conference on Human Computation and Crowdsourcing
Editors Lora Aroyo, Elena Simperl
PublisherAssociation for the Advancement of Artificial Intelligence
Number of pages4
ISBN (Print)9781577358480
StatePublished - 2020
Event8th AAAI Conference on Human Computation and Crowdsourcing, HCOMP 2020 - Virtual, Online
Duration: Oct 25 2020Oct 29 2020

Publication series

NameProceedings of the AAAI Conference on Human Computation and Crowdsourcing
ISSN (Print)2769-1330
ISSN (Electronic)2769-1349


Conference8th AAAI Conference on Human Computation and Crowdsourcing, HCOMP 2020
CityVirtual, Online

All Science Journal Classification (ASJC) codes

  • Computational Theory and Mathematics
  • Human-Computer Interaction

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