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Crowdsourcing Team Formation With Worker-Centered Modeling

Authors :
Federica Lucia Vinella
Jiayuan Hu
Ioanna Lykourentzou
Judith Masthoff
Source :
Frontiers in Artificial Intelligence, Vol 5 (2022)
Publication Year :
2022
Publisher :
Frontiers Media S.A., 2022.

Abstract

Modern crowdsourcing offers the potential to produce solutions for increasingly complex tasks requiring teamwork and collective labor. However, the vast scale of the crowd makes forming project teams an intractable problem to coordinate manually. To date, most crowdsourcing collaborative platforms rely on algorithms to automate team formation based on worker profiling data and task objectives. As a top-down strategy, algorithmic crowd team formation tends to alienate workers causing poor collaboration, interpersonal clashes, and dissatisfaction. In this paper, we investigate different ways that crowd teams can be formed through three team formation models namely bottom-up, top-down, and hybrid. By simulating an open collaboration scenario such as a hackathon, we observe that the bottom-up model forms the most competitive teams with the highest teamwork quality. Furthermore, we note that bottom-up approaches are particularly suitable for populations with high-risk appetites (most workers being lenient toward exploring new team configurations) and high degrees of homophily (most workers preferring to work with similar teammates). Our study highlights the importance of integrating worker agency in algorithm-mediated team formation systems, especially in collaborative/competitive settings, and bears practical implications for large-scale crowdsourcing platforms.

Details

Language :
English
ISSN :
26248212
Volume :
5
Database :
Directory of Open Access Journals
Journal :
Frontiers in Artificial Intelligence
Publication Type :
Academic Journal
Accession number :
edsdoj.fc6b9caadfc042619a7f9becea62271d
Document Type :
article
Full Text :
https://doi.org/10.3389/frai.2022.818562