Budget-optimal task allocation for reliable crowdsourcing systems

David R. Karger, Sewoong Oh, Devavrat Shah

Research output: Contribution to journalArticlepeer-review

Abstract

Crowdsourcing systems, in which numerous tasks are electronically distributed to numerous "information pieceworkers," have emerged as an effective paradigm for human-powered solving of large-scale problems in domains such as image classification, data entry, optical character recognition, recommendation, and proofreading. Because these low-paid workers can be unreliable, nearly all such systems must devise schemes to increase confidence in their answers, typically by assigning each task multiple times and combining the answers in an appropriate manner, e.g., majority voting. In this paper, we consider a general model of such crowdsourcing tasks and pose the problem of minimizing the total price (i.e., number of task assignments) that must be paid to achieve a target overall reliability. We give a new algorithm for deciding which tasks to assign to which workers and for inferring correct answers from the workers' answers. We show that our algorithm, inspired by belief propagation and low-rank matrix approximation, significantly outperforms majority voting and, in fact, is optimal through comparison to an oracle that knows the reliability of every worker. Further, we compare our approach with a more general class of algorithms that can dynamically assign tasks. By adaptively deciding which questions to ask to the next set of arriving workers, one might hope to reduce uncertainty more efficiently. We show that, perhaps surprisingly, the minimum price necessary to achieve a target reliability scales in the same manner under both adaptive and nonadaptive scenarios. Hence, our nonadaptive approach is order optimal under both scenarios. This strongly relies on the fact that workers are fleeting and cannot be exploited. Therefore, architecturally, our results suggest that building a reliable worker-reputation system is essential to fully harnessing the potential of adaptive designs.

Original languageEnglish (US)
Pages (from-to)1-24
Number of pages24
JournalOperations Research
Volume62
Issue number1
DOIs
StatePublished - Jan 2014

Keywords

  • Estimation
  • Expert systems
  • Information systems
  • Networks/graphs
  • Statistics
  • Stochastic

ASJC Scopus subject areas

  • Computer Science Applications
  • Management Science and Operations Research

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