Title
Boomerang: Rebounding the Consequences of Reputation Feedback on Crowdsourcing Platforms.
Abstract
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. Field experiments verify that Boomerang causes both workers and requesters to provide feedback that is more closely aligned with their private opinions. Inspired by a game-theoretic notion of incentive-compatibility, Boomerang opens opportunities for interaction design to incentivize honest reporting over strategic dishonesty.
Year
DOI
Venue
2016
10.1145/2984511.2984542
UIST
Keywords
Field
DocType
crowdsourcing platforms, human computation, game theory
Dishonesty,Internet privacy,Reputation system,Interaction design,Crowdsourcing,Computer science,Simulation,Negative feedback,Human computation,Human–computer interaction,Game theory,Reputation
Conference
ISSN
Citations 
PageRank 
Proceedings of the 29th Annual Symposium on User Interface Software and Technology, 2016
14
0.62
References 
Authors
36
37