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Racial Bias in Hate Speech and Abusive Language Detection Datasets

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arxiv 1905.12516 v1 pith:OMFPE7JO submitted 2019-05-29 cs.CL cs.LG

Thomas Davidson , Debasmita Bhattacharya , Ingmar Weber This is my paper · ORCID
classification cs.CLcs.LG
keywords abusivelanguageafrican-americanbiasclassifiersdatasetsdetectionenglish
verification ladder T0 review T1 audit T2 compute T3 formal
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Technologies for abusive language detection are being developed and applied with little consideration of their potential biases. We examine racial bias in five different sets of Twitter data annotated for hate speech and abusive language. We train classifiers on these datasets and compare the predictions of these classifiers on tweets written in African-American English with those written in Standard American English. The results show evidence of systematic racial bias in all datasets, as classifiers trained on them tend to predict that tweets written in African-American English are abusive at substantially higher rates. If these abusive language detection systems are used in the field they will therefore have a disproportionate negative impact on African-American social media users. Consequently, these systems may discriminate against the groups who are often the targets of the abuse we are trying to detect.

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Cited by 4 Pith papers

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    Microsoft's Tay chatbot failed after learning offensive Twitter content within 16 hours, and the paper draws deployment lessons from that incident.

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