  Social stereotypes negatively impact individuals' judgements about different
groups and may have a critical role in how people understand language directed
toward minority social groups. Here, we assess the role of social stereotypes
in the automated detection of hateful language by examining the relation
between individual annotator biases and erroneous classification of texts by
hate speech classifiers. Specifically, in Study 1 we investigate the impact of
novice annotators' stereotypes on their hate-speech-annotation behavior. In
Study 2 we examine the effect of language-embedded stereotypes on expert
annotators' aggregated judgements in a large annotated corpus. Finally, in
Study 3 we demonstrate how language-embedded stereotypes are associated with
systematic prediction errors in a neural-network hate speech classifier. Our
results demonstrate that hate speech classifiers learn human-like biases which
can further perpetuate social inequalities when propagated at scale. This
framework, combining social psychological and computational linguistic methods,
provides insights into additional sources of bias in hate speech moderation,
informing ongoing debates regarding fairness in machine learning.
