Budget-Optimal Task Allocation for Reliable Crowdsourcing Systems

TL;DR

This paper introduces a cost-effective algorithm for task allocation in crowdsourcing that guarantees target reliability, outperforming traditional methods and showing non-adaptive strategies are nearly as efficient as adaptive ones.

Contribution

The paper presents a novel belief propagation-inspired algorithm for optimal task assignment and answer inference, demonstrating its superiority and order-optimality in crowdsourcing reliability.

Findings

The proposed algorithm outperforms majority voting in accuracy and cost.

Adaptive and non-adaptive task assignment costs scale similarly, showing non-adaptive methods are nearly optimal.

Building a reliable worker reputation system is crucial for maximizing adaptive design benefits.

Abstract

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.