Boomerang: Rebounding the Consequences of Reputation Feedback on Crowdsourcing Platforms
This addresses quality and fairness issues for workers and requesters on crowdsourcing platforms, offering a novel incentive mechanism.
The paper tackles the problem of inflated reputation scores on crowdsourcing platforms, which obscure low-quality work and unfair rejections, by introducing Boomerang, a system that rebounds feedback consequences onto the giver, leading to more accurate feedback aligned with private opinions in field experiments.
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 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.