Proceedings of the CSCW 2021 Workshop -- Investigating and Mitigating Biases in Crowdsourced Data
This addresses biases in data for researchers and practitioners using crowdsourcing, but it is incremental as it focuses on workshop discussions rather than new findings.
The workshop tackled the problem of biases in crowdsourced data by exploring how workflows, worker attributes, and practices contribute to these biases, and it discussed research directions for mitigation and implications for workers, without providing specific numerical results.
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.