2017/07/18 by Snehalkumar S. Gaikwad, Gaikwad, Snehalkumar "Neil" S., Nalin Chhibber +55
Computer Science · Decision Sciences · #Auction Theory and Applications #FOS: Computer and information sciences #Human-Computer Interaction (cs.HC) #Mobile Crowdsensing and Crowdsourcing #Open Source Software Innovations
paper · pdf · doi:10.48550/arxiv.1707.05645
openalex publication_date 2017/07/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.