Research conducted by Saiph Savage , the J. Wayne and Kathy Richards Faculty Fellow in Engineering at West Virginia University, received honorable mention for best paper at the Association for Computing Machinery’s CHI Conference on Human Factors in Computing Systems.
A recent report by Harris and Krueger found that 600,000 workers participate in the online gig economy and the number is growing rapidly. But while crowdsourcing platforms have extended the scope of the labor market, enabling people with disabilities, at-home parents and temporarily out-of-work people to work, many, Savage said, are concerned that workers on crowdsourcing markets are treated unfairly.
“Concerns about low earnings on crowd work platforms have been voiced repeatedly,” Savage said. “Unfortunately, detailed research into crowd work earnings has been limited by an absence of adequate quantitative data.”
Working in collaboration with professors from such institutions as Carnegie Mellon University, the University of Pennsylvania and University of Oxford, the team studied the hourly wage of online workers by using Amazon Mechanical Turk, one of the one of the largest micro-crowdsourcing markets.
“We created a web plugin to track worker’s online work activity, i.e., the number of hours inputted and the salary they obtained,” Savage said. “We found that the majority of workers made below minimum wage; the median hourly wage was only $2 per hour, with only four percent of the workers earning more than $7.25 per hour.”
According to Savage, the study, which recorded the tasks of more than 2,700 workers, was the first of its kind.
Savage will use the findings in her Human Computer Interaction Laboratory as she develops tools that use machine learning to teach crowd workers how to make a better living on these digital platforms.
Held in Montreal, Canada, CHI 2018 is the premier international conference of Human-Computer Interaction. More than 2,500 submissions were received with 101 being chosen for honorable mention, ranking it among the top five percent of all submissions received.
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