Crowdsourcing in form of human-based electronic services (people services) provides a powerful way of outsourcing tasks to a large crowd of remote workers over the Internet. Research has shown that multiple redundant results delivered by different workers can be aggregated in order to achieve a reliable result. However, existing implementations of this approach are rather inefficient as they multiply the effort for task execution and are not able to guarantee a certain quality level. As a starting point towards an integrated approach for quality management of people services we have developed a quality management model that combines elements of statistical quality control (SQC) with group decision theory. The contributions of the workers are tracked and weighted individually in order to minimize the quality management effort while guaranteing a well-defined level of overall result quality. A quantitative analysis of the approach based on an optical character recognition (OCR) scenario confirms the efficiency and reach of the approach.
Crowdsourcing in the form of human-based electronic services provides a powerful way of outsourcing so called human intelligence tasks (HITs) to a large workforce of people over the Internet. Because of the limited control over that workforce, it is challenging to ensure the quality of the work results. Several approaches have been proposed that can be applied to specific types of HITs. However, it is difficult to identify a suitable quality management approach for any given type of HIT. This paper aims to provide a first sketch of a decision matrix.
Crowdsourcing in the form of human-based electronic services (people services) provides a powerful way of outsourcing tasks to a large crowd of remote workers over the Internet. Research has shown that multiple redundant results delivered by different workers can be aggregated in order to achieve a reliable result. However, basic implementations of this approach are rather inefficient as they multiply the effort for task execution and are not able to guarantee a certain quality level. In this paper we are addressing these challenges by elaborating on a statistical approach for quality management of people services which we had previously proposed. The approach combines elements of statistical quality management with dynamic group decisions. We present a comprehensive statistical model that enhances our original work and makes it more transparent. We also provide an extendible toolkit that implements our model and facilitates its application to real-time experiments as well as to simulations. A quantitative analysis based on an optical character recognition (OCR) scenario confirms the efficiency and reach of our model.
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