TY - GEN
T1 - The Challenges of Crowd Workers in Rural and Urban America
AU - Flores-Saviaga, Claudia
AU - Li, Yuwen
AU - Hanrahan, Benjamin V.
AU - Bigham, Jeffrey
AU - Savage, Saiph
N1 - Publisher Copyright:
© 2020, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85117857435&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85117857435&partnerID=8YFLogxK
U2 - 10.1609/hcomp.v8i1.7475
DO - 10.1609/hcomp.v8i1.7475
M3 - Conference contribution
AN - SCOPUS:85117857435
SN - 9781577358480
T3 - Proceedings of the AAAI Conference on Human Computation and Crowdsourcing
SP - 159
EP - 162
BT - HCOMP 2020 - Proceedings of the 8th AAAI Conference on Human Computation and Crowdsourcing
A2 - Aroyo, Lora
A2 - Simperl, Elena
PB - Association for the Advancement of Artificial Intelligence
T2 - 8th AAAI Conference on Human Computation and Crowdsourcing, HCOMP 2020
Y2 - 25 October 2020 through 29 October 2020
ER -