Detecting and Preventing Confused Labels in Crowdsourced Data

Crowdsourcing is a challenging activity for many reasons, from task design to workers' training, identification of low-quality annotators, and many more. A particularly subtle form of error is due to confusion of observations, that is, crowd workers (including diligent ones) that confuse items of a class i with items of a class j, either because they are similar or because the task description has failed to explain the differences. In this paper we show that confusion of observations can be a frequent occurrence in many tasks, and that such confusions cause a significant loss in accuracy. As a consequence, confusion detection is of primary importance for crowdsourced data labeling and classification. To address this problem we introduce an algorithm for confusion detection that leverages an inference procedure based on Markov Chain Monte Carlo (MCMC) sampling. We evaluate the algorithm via both synthetic datasets and crowdsourcing experiments and show that it has high accuracy in confusion detection (up to 99%). We experimentally show that quality is significantly improved without sacrificing efficiency. Finally, we show that detecting confusion is important as it can alert task designers early in the crowdsourcing process and lead designers to modify the task or add specific training and information to reduce the occurrence of workers' confusion. We show that even simple modifications, such as alerting workers of the risk of confusion, can improve performance significantly.