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• W2949678053 abstract "We investigate how annotators’ insensitivity to differences in dialect can lead to racial bias in automatic hate speech detection models, potentially amplifying harm against minority populations. We first uncover unexpected correlations between surface markers of African American English (AAE) and ratings of toxicity in several widely-used hate speech datasets. Then, we show that models trained on these corpora acquire and propagate these biases, such that AAE tweets and tweets by self-identified African Americans are up to two times more likely to be labelled as offensive compared to others. Finally, we propose *dialect* and *race priming* as ways to reduce the racial bias in annotation, showing that when annotators are made explicitly aware of an AAE tweet’s dialect they are significantly less likely to label the tweet as offensive." @default.
• W2949678053 created "2019-06-27" @default.
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• W2949678053 date "2019-01-01" @default.
• W2949678053 modified "2023-10-16" @default.
• W2949678053 title "The Risk of Racial Bias in Hate Speech Detection" @default.
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• W2949678053 doi "https://doi.org/10.18653/v1/p19-1163" @default.
• W2949678053 hasPublicationYear "2019" @default.
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