Towards Equal Gender Representation in the Annotations of Toxic Language Detection

no code implementations • ACL (GeBNLP) 2021 • Elizabeth Excell, Noura Al Moubayed

We then apply the learned associations between gender and language to toxic language classifiers, finding that models trained exclusively on female-annotated data perform 1. 8% better than those trained solely on male-annotated data and that training models on data after removing all offensive words reduces bias in the model by 55. 5% while increasing the sensitivity by 0. 4%.

Fairness