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

arXiv · 2111.14322

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

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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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Danula Hettiachchi, Mark Sanderson, Jorge Goncalves, Simo Hosio, Gabriella Kazai, Matthew Lease, Mike Schaekermann, Emine Yilmaz. 2021-11-29. Proceedings of the CSCW 2021 Workshop -- Investigating and Mitigating Biases in Crowdsourced Data. https://arxiv.org/abs/2111.14322

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