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arXiv · 1411.6562

Evaluating the Crowd with Confidence

Manas Joglekar·Hector Garcia-Molina·Aditya Parameswaran
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Abstract

Worker quality control is a crucial aspect of crowdsourcing systems; typically occupying a large fraction of the time and money invested on crowdsourcing. In this work, we devise techniques to generate confidence intervals for worker error rate estimates, thereby enabling a better evaluation of worker quality. We show that our techniques generate correct confidence intervals on a range of real-world datasets, and demonstrate wide applicability by using them to evict poorly performing workers, and provide confidence intervals on the accuracy of the answers.

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Manas Joglekar, Hector Garcia-Molina, Aditya Parameswaran. 2014-11-12. Evaluating the Crowd with Confidence. https://arxiv.org/abs/1411.6562

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