Crowds, not drones: Modeling human factors in interactive crowdsourcing

Senjuti Basu Roy, Ioanna Lykourentzou, Saravanan Thirumuruganathan, Sihem Amer-Yahia, Gautam Das

Research output: Contribution to journalConference articlepeer-review

10 Scopus citations


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 nonrecurrent 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.

Original languageEnglish (US)
Pages (from-to)39-42
Number of pages4
JournalCEUR Workshop Proceedings
StatePublished - 2013
Externally publishedYes
Event1st VLDB Workshop on Databases and Crowdsourcing, DBCrowd 2013 - Co-located with the 39th International Conference on Very Large Data Bases, VLDB 2013 - Riva del Garda, Trento, Italy
Duration: Aug 26 2013 → …

All Science Journal Classification (ASJC) codes

  • General Computer Science


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