Crowd Guilds: Worker-led Reputation and Feedback on Crowdsourcing Platforms
This addresses the issue of unreliable reputation systems for crowd workers and requesters on platforms, though it is an incremental improvement based on historical guild concepts.
The paper tackles the problem of inflated and uninformative reputation scores in decentralized crowdsourcing by introducing crowd guilds, where centralized groups of workers use double-blind peer assessment to certify quality. In a two-week field experiment, crowd guilds produced reputation signals more strongly correlated with ground-truth worker quality than traditional decentralized models.
Crowd workers are distributed and decentralized. While decentralization is designed to utilize independent judgment to promote high-quality results, it paradoxically undercuts behaviors and institutions that are critical to high-quality work. Reputation is one central example: crowdsourcing systems depend on reputation scores from decentralized workers and requesters, but these scores are notoriously inflated and uninformative. In this paper, we draw inspiration from historical worker guilds (e.g., in the silk trade) to design and implement crowd guilds: centralized groups of crowd workers who collectively certify each other's quality through double-blind peer assessment. A two-week field experiment compared crowd guilds to a traditional decentralized crowd work model. Crowd guilds produced reputation signals more strongly correlated with ground-truth worker quality than signals available on current crowd working platforms, and more accurate than in the traditional model.