Proceedings of the CSCW 2021 Workshop -- Investigating and Mitigating Biases in Crowdsourced Data
Danula Hettiachchi, Mark Sanderson, Jorge Goncalves, Simo Hosio,, Gabriella Kazai, Matthew Lease, Mike Schaekermann, Emine Yilmaz

TL;DR
This workshop paper discusses how crowdsourcing workflows and worker attributes contribute to biases in data and explores research directions to mitigate these biases, emphasizing their impact on both data quality and workers.
Contribution
It provides a comprehensive overview of biases in crowdsourced data and proposes research avenues for bias mitigation strategies in crowdsourcing workflows.
Findings
Identification of key sources of bias in crowdsourcing
Discussion of methods to mitigate labeling biases
Implications for worker fairness and data quality
Abstract
This volume contains the position papers presented at CSCW 2021 Workshop - Investigating and Mitigating Biases in Crowdsourced Data, held online on 23rd October 2021, at the 24th ACM Conference on Computer-Supported Cooperative Work and Social Computing (CSCW 2021). The workshop explored how specific crowdsourcing workflows, worker attributes, and work practices contribute to biases in data. The workshop also included discussions on research directions to mitigate labelling biases, particularly in a crowdsourced context, and the implications of such methods for the workers.
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Taxonomy
TopicsMobile Crowdsensing and Crowdsourcing · Personal Information Management and User Behavior · Data Quality and Management
