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Crowds, not Drones: Modeling Human Factors in Interactive Crowdsourcing

Abstract : In this vision paper, we propose SmartCrowd, an intelligent and adaptive crowdsourcing framework. Contrary to existing crowdsourcing systems, where the process of hiring workers (crowd), learning their skills, and evaluating the accuracy of tasks they perform are fragmented, siloed, and often ad-hoc, SmartCrowd foresees a paradigm shift in that process, considering unpredictability of human nature, namely human factors. SmartCrowd offers opportunities in making crowdsourcing intelligent through iterative interaction with the workers, and adaptively learning and improving the underlying processes. Both existing (majority of which do not require longer engagement from volatile and mostly non-recurrent workers) and next generation crowdsourcing applications (which require longer engagement from the crowd) stand to benefit from SmartCrowd. We outline the opportunities in SmartCrowd, and discuss the challenges and directions, that can potentially revolutionize the existing crowdsourcing landscape.
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Submitted on : Monday, January 6, 2014 - 4:23:37 PM
Last modification on : Friday, November 18, 2022 - 9:25:33 AM
Long-term archiving on: : Thursday, April 10, 2014 - 3:46:41 PM


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  • HAL Id : hal-00923542, version 1


Senjuti Basu Roy, Ioanna Lykourentzou, Saravanan Thirumuruganathan, Sihem Amer-Yahia, Gautam Das. Crowds, not Drones: Modeling Human Factors in Interactive Crowdsourcing. DBCrowd 2013 - VLDB Workshop on Databases and Crowdsourcing, Aug 2013, Riva del Garda, Trento, Italy. pp.39-42. ⟨hal-00923542⟩



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