Crowdsourcing with unsure option

Crowdsourcing with unsure option One of the fundamental issues in crowdsourcing is the trade-off between the number of workers needed for high-accuracy aggregation and the budget to pay. To save cost, it is important to ensure high quality of the crowd-sourced labels, hence the total cost on label collection will be reduced. Since the confidence of the workers often has a close relationship with their abilities, a possible way for quality control is to request the workers to return the labels only when they feel confident, by means of providing them with an ‘unsure’ option. On the other hand, allowing workers to choose the unsure option can potentially waste part of the budget. In this work, we conduct an analysis towards understanding when providing the unsure option indeed leads to significant cost reduction, as well as how the confidence threshold might be set. We also propose an online mechanism, which is an alternative for threshold selection when the estimation of the crowd ability distribution is difficult. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Machine Learning Springer Journals

Crowdsourcing with unsure option

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Publisher
Springer US
Copyright
Copyright © 2017 by The Author(s)
Subject
Computer Science; Artificial Intelligence (incl. Robotics); Control, Robotics, Mechatronics; Computing Methodologies; Simulation and Modeling; Language Translation and Linguistics
ISSN
0885-6125
eISSN
1573-0565
D.O.I.
10.1007/s10994-017-5677-x
Publisher site
See Article on Publisher Site

Abstract

One of the fundamental issues in crowdsourcing is the trade-off between the number of workers needed for high-accuracy aggregation and the budget to pay. To save cost, it is important to ensure high quality of the crowd-sourced labels, hence the total cost on label collection will be reduced. Since the confidence of the workers often has a close relationship with their abilities, a possible way for quality control is to request the workers to return the labels only when they feel confident, by means of providing them with an ‘unsure’ option. On the other hand, allowing workers to choose the unsure option can potentially waste part of the budget. In this work, we conduct an analysis towards understanding when providing the unsure option indeed leads to significant cost reduction, as well as how the confidence threshold might be set. We also propose an online mechanism, which is an alternative for threshold selection when the estimation of the crowd ability distribution is difficult.

Journal

Machine LearningSpringer Journals

Published: Oct 26, 2017

References

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