Reducing annotator bias by belief elicitation

TL;DR

This paper introduces a simple belief elicitation method to reduce annotator bias in crowdsourced data, improving AI fairness by asking annotators about others' judgments rather than their own.

Contribution

The study proposes a novel, resource-efficient approach for mitigating annotator bias by eliciting beliefs about others' judgments, demonstrated through controlled experiments.

Findings

Bias was consistently reduced when asking for beliefs instead of judgments.

The method requires fewer annotations and no large number of annotators per instance.

Results suggest improved fairness and generalizability in AI systems.

Abstract

Crowdsourced annotations of data play a substantial role in the development of Artificial Intelligence (AI). It is broadly recognised that annotations of text data can contain annotator bias, where systematic disagreement in annotations can be traced back to differences in the annotators' backgrounds. Being unaware of such annotator bias can lead to representational bias against minority group perspectives and therefore several methods have been proposed for recognising bias or preserving perspectives. These methods typically require either a substantial number of annotators or annotations per data instance. In this study, we propose a simple method for handling bias in annotations without requirements on the number of annotators or instances. Instead, we ask annotators about their beliefs of other annotators' judgements of an instance, under the hypothesis that these beliefs may provide more representative and less biased labels than judgements. The method was examined in two controlled, survey-based experiments involving Democrats and Republicans (n=1,590) asked to judge statements as arguments and then report beliefs about others' judgements. The results indicate that bias, defined as systematic differences between the two groups of annotators, is consistently reduced when asking for beliefs instead of judgements. Our proposed method therefore has the potential to reduce the risk of annotator bias, thereby improving the generalisability of AI systems and preventing harm to unrepresented socio-demographic groups, and we highlight the need for further studies of this potential in other tasks and downstream applications.