Proceedings of the CSCW 2021 Workshop -- Investigating and Mitigating Biases in Crowdsourced Data

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.