Annotators with Attitudes: How Annotator Beliefs And Identities Bias Toxic Language Detection

@article{Sap2021AnnotatorsWA,
  title={Annotators with Attitudes: How Annotator Beliefs And Identities Bias Toxic Language Detection},
  author={Maarten Sap and Swabha Swayamdipta and Laura Vianna and Xuhui Zhou and Yejin Choi and Noah A. Smith},
  journal={ArXiv},
  year={2021},
  volume={abs/2111.07997}
}
The perceived toxicity of language can vary based on someone’s identity and beliefs, but this variation is often ignored when collecting toxic language datasets, resulting in dataset and model biases. We seek to understand the *who*, *why*, and *what* behind biases in toxicity annotations. In two online studies with demographically and politically diverse participants, we investigate the effect of annotator identities (*who*) and beliefs (*why*), drawing from social psychology research about… 

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