DeepAI AI Chat
Log In Sign Up

Budget-Optimal Task Allocation for Reliable Crowdsourcing Systems

by   David R. Karger, et al.

Crowdsourcing systems, in which numerous tasks are electronically distributed to numerous "information piece-workers", 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 which can dynamically assign tasks. By adaptively deciding which questions to ask to the next arriving worker, 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 non-adaptive scenarios. Hence, our non-adaptive approach is order-optimal under both scenarios. This strongly relies on the fact that workers are fleeting and can not be exploited. Therefore, architecturally, our results suggest that building a reliable worker-reputation system is essential to fully harnessing the potential of adaptive designs.


page 1

page 2

page 3

page 4


A Worker-Task Specialization Model for Crowdsourcing: Efficient Inference and Fundamental Limits

Crowdsourcing system has emerged as an effective platform to label data ...

Achieving Budget-optimality with Adaptive Schemes in Crowdsourcing

Crowdsourcing platforms provide marketplaces where task requesters can p...

Iterative Bayesian Learning for Crowdsourced Regression

Crowdsourcing platforms emerged as popular venues for purchasing human i...

Crowdsourcing Control: Moving Beyond Multiple Choice

To ensure quality results from crowdsourced tasks, requesters often aggr...

Prospect Theory Based Crowdsourcing for Classification in the Presence of Spammers

We consider the M-ary classification problem via crowdsourcing, where cr...

Adversarial Task Allocation

The problem of allocating tasks to workers is of long standing fundament...

How Many Workers to Ask? Adaptive Exploration for Collecting High Quality Labels

Crowdsourcing has been part of the IR toolbox as a cheap and fast mechan...