Labeling images with a computer game
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Soylent: a word processor with a crowd inside
UIST '10 Proceedings of the 23nd annual ACM symposium on User interface software and technology
Real-time crowd control of existing interfaces
Proceedings of the 24th annual ACM symposium on User interface software and technology
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Much of the current work in crowdsourcing is focused on increasing the quality of responses. Quality issues are most often due to a small subset of low quality workers. The ability to distinguish between high and low quality workers would allow a wide range of error correction to be performed for such tasks. However, differentiating between these types is difficult when no measure of individual success is available. We propose it is possible to use higher quality workers to compensate for lower quality ones, without explicitly identifying them, by allowing them to observe and react to the input of the collective. In this paper, we present initial work on eliciting this behavior and discuss how it may be possible to leverage self-correction in the crowd for better performance on continuous real-time tasks.