An Analysis of the Use of Qualifications on the Amazon Mechanical Turk Online Labor Market

Abstract

Several human computation systems use crowdsourcing labor markets to recruit workers. However, it is still a challenge to guarantee that the results produced by workers have a high enough quality. This is particularly difficult in markets based on micro-tasks, where the assessment of the quality of the results needs to be done automatically. Pre-selection of suitable workers is a mechanism that can improve the quality of the results achieved. This can be done by considering worker’s personal information, worker’s historical behavior in the system, or through the use of customized qualification tasks. However, little is known about how requesters use these mechanisms in practice. This study advances present knowledge in worker pre-selection by analyzing data collected from the Amazon Mechanical Turk platform, regarding the way requesters use qualifications to this end. Furthermore, the influence of using customized qualification tasks in the quality of the results produced by workers is investigated. Results show that most jobs (93.6%) use some mechanism for the pre-selection of workers. While most workers use standard qualifications provided by the system, the few requesters that submit most of the jobs prefer to use customized ones. Regarding worker behavior, we identified a positive and significant correlation between the propensity of the worker to possess a particular qualification, and both the number of tasks that require this qualification, and the reward offered for the tasks that require the qualification, although this correlation is weak. To assess the impact that the use of customized qualifications has in the quality of the results produced, we have executed experiments with three different types of tasks using both unqualified and qualified workers. The results showed that, generally, qualified workers provide more accurate answers, when compared to unqualified ones.