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Demoting Racial Bias in Hate Speech Detection
[article]
2020
arXiv
pre-print
In current hate speech datasets, there exists a high correlation between annotators' perceptions of toxicity and signals of African American English (AAE). This bias in annotated training data and the tendency of machine learning models to amplify it cause AAE text to often be mislabeled as abusive/offensive/hate speech with a high false positive rate by current hate speech classifiers. In this paper, we use adversarial training to mitigate this bias, introducing a hate speech classifier that
arXiv:2005.12246v1
fatcat:klt4rbqn3fbtrnlpyjkzuw5nyu