Low-quality results have been a long-standing problem on microtask crowdsourcing platforms, driving away requesters and justifying low wages for workers. To date, workers have been blamed for low-quality results: they are said to make as little effort as possible, do not pay attention to detail, and lack expertise. In this paper, we hypothesize that requesters may also be responsible for low-quality work: they launch unclear task designs that confuse even earnest workers, under-specify edge cases, and neglect to include examples. We introduce prototype tasks, a crowdsourcing strategy requiring all new task designs to launch a small number of sample tasks. Workers attempt these tasks and leave feedback, enabling the re-quester to iterate on the design before publishing it.
Recommended citation: Gaikwad, S., Chhibber, N., Sehgal et al. (2017). Prototype Tasks: Improving Crowdsourcing Results through Rapid, Iterative Task Design. arXiv preprint arXiv:1707.05645.