Skip to main content

Research Repository

Advanced Search

Towards Equal Gender Representation in the Annotations of Toxic Language Detection

Excell, Elizabeth; Al Moubayed, Noura

Towards Equal Gender Representation in the Annotations of Toxic Language Detection Thumbnail


Authors

Elizabeth Excell



Abstract

Classifiers tend to propagate biases present in the data on which they are trained. Hence, it is important to understand how the demographic identities of the annotators of comments affect the fairness of the resulting model. In this paper, we focus on the differences in the ways men and women annotate comments for toxicity, investigating how these differences result in models that amplify the opinions of male annotators. We find that the BERT model associates toxic comments containing offensive words with male annotators, causing the model to predict 67.7% of toxic comments as having been annotated by men. We show that this disparity between gender predictions can be mitigated by removing offensive words and highly toxic comments from the training data. 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%.

Citation

Excell, E., & Al Moubayed, N. (2021). Towards Equal Gender Representation in the Annotations of Toxic Language Detection. . https://doi.org/10.18653/v1/2021.gebnlp-1.7

Conference Name 3rd Workshop on Gender Bias in Natural Language Processing (GeBNLP2021), International Joint Conference on Natural Language Processing (INCNLP2021)
Conference Location Bangkok, Thailand
Start Date Aug 1, 2021
End Date Aug 6, 2021
Acceptance Date Jun 23, 2021
Online Publication Date Aug 3, 2021
Publication Date 2021
Deposit Date Jul 20, 2021
Publicly Available Date Aug 7, 2021
Pages 55-65
DOI https://doi.org/10.18653/v1/2021.gebnlp-1.7
Related Public URLs https://arxiv.org/pdf/2106.02183.pdf

Files







You might also like



Downloadable Citations