Attention-based Gender-Stereotype Detection

Authors

Manuela Nayantara, Technological University DublinFollow
Sarah Jane Delany, Technological University DublinFollow

Files

Publisher

Technological University Dublin

Description

Gender stereotypes are perceptions about the typical physical, emotional, and social characteristics of individuals. Unlike gender bias which can result in the systematic and unfair treatment of individuals based on their gender, gender stereotypes do not always perpetuate a negative impact. Hence, there is no solid definition that frames what is considered a gender-stereotype in text. In addition, there is also a lack of labelled gender-stereotype datasets. This has led to most of the work in literature being about gender bias and not gender stereotypes. Therefore, in our research, we frame a clear definition of what constitutes a gender-stereotype in text. And using fairness benchmark datasets that were not originally intended for gender-stereotype studies, we propose an end-to-end approach to automatically identify language features that influence a gender-stereotypical sentence or text. We do this using the learned attention weights of a model. Our evaluations on the features that the model was using to predict gender-stereotypes aligned with our definition of gender-stereotypes.

Publication Date

2023

Keywords

Attention, gender-stereotypes, explainable ai

Disciplines

Computer Sciences

Conference

First Annual Teaching and Research Showcase 2023

DOI

https://doi.org/10.21427/WGD8-YC57

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 4.0 International License.

Recommended Citation

Jeyaraj, M., & Delany, S. J. (2023). Attention-based Gender-Stereotype Detection. Technological University Dublin. DOI: 10.21427/WGD8-YC57