Investigating and Mitigating Biases in Crowdsourced Data

Abstract

It is common practice for machine learning systems to rely on crowdsourced label data for training and evaluation. It is also well-known that biases present in the label data can induce biases in the trained models. Biases may be introduced by the mechanisms used for deciding what data should/could be labelled or by the mechanisms employed to obtain the labels. Various approaches have been proposed to detect and correct biases once the label dataset has been constructed. However, proactively reducing biases during the data labelling phase and ensuring data fairness could be more economical compared to post-processing bias mitigation approaches. In this workshop, we aim to foster discussion on ongoing research around biases in crowdsourced data and to identify future research directions to detect, quantify and mitigate biases before, during and after the labelling process such that both task requesters and crowd workers can benefit. We will explore how specific crowdsourcing workflows, worker attributes, and work practices contribute to biases in the labelled data; how to quantify and mitigate biases as part of the labelling process; and how such mitigation approaches may impact workers and the crowdsourcing ecosystem. The outcome of the workshop will include a collaborative publication of a research agenda to improve or develop novel methods relating to crowdsourcing tools, processes and work practices to address biases in crowdsourced data. We also plan to run a Crowd Bias Challenge prior to the workshop, where participants will be asked to collect labels for a given dataset while minimising potential biases.