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Though majority vote among annotators is typically used for ground truthlabels in natural language processing, annotator disagreement in tasks such ashate speech detection may reflect differences in opinion across groups, notnoise. Thus, a crucial problem in hate speech detection is determining whethera statement is offensive to the demographic group that it targets, when thatgroup may constitute a small fraction of the annotator pool. We construct amodel that predicts individual annotator ratings on potentially offensive textand combines this information with the predicted target group of the text tomodel the opinions of target group members. We show gains across a range ofmetrics, including raising performance over the baseline by 22% at predictingindividual annotators' ratings and by 33% at predicting variance amongannotators, which provides a metric for model uncertainty downstream. We findthat annotator ratings can be predicted using their demographic information andopinions on online content, without the need to track identifying annotator IDsthat link each annotator to their ratings. We also find that use ofnon-invasive survey questions on annotators' online experiences helps tomaximize privacy and minimize unnecessary collection of demographic informationwhen predicting annotators' opinions.

When the Majority is Wrong: Modeling Annotator Disagreement for Subjective Tasks