  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. We report a field experiment in which tasks that
underwent prototype task iteration produced higher-quality work results than
the original task designs. With this research, we suggest that a simple and
rapid iteration cycle can improve crowd work, and we provide empirical evidence
that requester "quality" directly impacts result quality.
