About

Project

This package was developed as part of a masters thesis made on the MSc of Cognitive Science on Aarhus University. With this package we hope to contribute to the development of methods for quantifying gender bias in Danish language models.

Our main contribution is the refining and wrapping of existing methods into a coherent test suite. Additionally, we have added our own evaluation and visualization functions in order to present the results of the different metrics in a coherent way.

Contact

If you have any questions regarding the project itself or the code implementation, feel free to contact either Thea Rolskov Sloth or Astrid Sletten Rybner via e-mail.

GenDa Lens is licensed under MIT and available on GitHub.

Acknowledgements

This project uses code from three already implemented frameworks for quantifying gender bias in Danish. While all code written by others is properly attributed at the top of the scripts in the repository, we would also like to present aknowledgement here to the authors of the work we draw on:

• The original ABC Framework: González, A. V., Barrett, M., Hvingelby, R., Webster, K., & Søgaard, A. (2020). Type B reflexivization as an unambiguous testbed for multilingual multi-task gender bias.

• The original Augmented DaNe Framework: Lassen, I. M., Almasi, M., Enevoldsen, K., & Kristensen-mclachlan, R. (2023, May). Detecting intersectionality in NER models: A data-driven approach.

• The original WinoBias Framework: Zhao, J., Wang, T., Yatskar, M., Ordonez, V., & Chang, K. W. (2018). Gender bias in coreference resolution: Evaluation and debiasing methods.

• The Danish translation of the WinoBias Framework, DaWinoBias: Signe Kirk and Kiri Koppelgaard