Crowd Guilds: Worker-led Reputation and Feedback on Crowdsourcing Platforms

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...

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Bibliographic Details
Published in:arXiv.org 2017-02
Main Authors: Whiting, Mark E, Gamage, Dilrukshi, Gaikwad, Snehalkumar S, Gilbee, Aaron, Goyal, Shirish, Ballav, Alipta, Majeti, Dinesh, Chhibber, Nalin, Richmond-Fuller, Angela, Vargus, Freddie, Tejas Seshadri Sarma, Chandrakanthan, Varshine, Moura, Teogenes, Mohamed Hashim Salih, Gabriel Bayomi Tinoco Kalejaiye, Ginzberg, Adam, Mullings, Catherine A, Dayan, Yoni, Milland, Kristy, Orefice, Henrique, Regino, Jeff, Parsi, Sayna, Kunz Mainali, Sehgal, Vibhor, Matin, Sekandar, Sinha, Akshansh, Vaish, Rajan, Bernstein, Michael S
Format: Article
Language:English
Subjects:
Crowdsourcing
Platforms
Quality assessment
Reputations
Signal quality
Silk
Workers
Online Access:Get full text
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Summary: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.
ISSN:2331-8422
DOI:10.48550/arxiv.1611.01572