Paid crowdsourcing platforms suffer from low-quality work and unfair rejections, but paradoxically, most workers and requesters have high reputation scores. These inflated scores, which make high-quality work and workers difficult to find, stem from social pressure to avoid giving negative feedback. We introduce Boomerang, a reputation system for crowdsourcing platforms that elicits more accurate feedback by rebounding the consequences of feedback directly back onto the person who gave it. With Boomerang, requesters find that their highly-rated workers gain earliest access to their future tasks, and workers find tasks from their highly-rated requesters at the top of their task feed.
Gaikwad, S. N. S., Morina, D., Ginzberg, A., Mullings, C., Goyal, S., Gamage, D., … & Ziulkoski, K. (2016, October). Boomerang: Rebounding the consequences of reputation feedback on crowdsourcing platforms. In Proceedings of the 29th Annual Symposium on User Interface Software and Technology (pp. 625-637). ACM.