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Quality Control in Crowdsourcing Systems Issues and Directions
| Citations: | 8 - 0 self |
BibTeX
@MISC{Workflow_qualitycontrol,
author = {Web-scale Workflow and Mohammad Allahbakhsh and Boualem Benatallah and Hamid Reza and Motahari-nezhad Hewlett-packard Laboratories and Elisa Bertino},
title = {Quality Control in Crowdsourcing Systems Issues and Directions},
year = {}
}
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Abstract
As a new distributed computing model, crowdsourcing lets people leverage the crowd’s intelligence and wisdom toward solving problems. This article proposes a framework for characterizing various dimensions of quality control in crowdsourcing systems, a critical issue. The authors briefly review existing quality-control approaches, identify open issues, and look to future research directions. Crowdsourcing has emerged as an effective way to perform tasks that are easy for humans but remain difficult for computers. 1,2 For instance, Amazon Mechanical Turk (MTurk; www.mturk.com) provides on-demand access to task forces for micro-tasks such as image recognition and language translation. Several organizations, including DARPA and various world health and relief agencies, are using platforms such as MTurk, CrowdFlower
Keyphrases
quality control system issue critical issue various world health task force let people effective way amazon mechanical turk crowd intelligence open issue several organization various dimension language translation quality-control approach relief agency on-demand access research direction image recognition
