Beyond Binary Gender Labels: Revealing Gender Bias in LLMs through Gender-Neutral Name Predictions
Zhiwen You, HaeJin Lee, Shubhanshu Mishra, Sullam Jeoung, Apratim Mishra, Jinseok Kim, Jana Diesner
Abstract
Name-based gender prediction has traditionally categorized individuals as either female or male based on their names, using a binary classification system. That binary approach can be problematic in the cases of gender-neutral names that do not align with any one gender, among other reasons. Relying solely on binary gender categories without recognizing gender-neutral names can reduce the inclusiveness of gender prediction tasks. We introduce an additional gender category, i.e., “neutral”, to study and address potential gender biases in Large Language Models (LLMs). We evaluate the performance of several foundational and large language models in predicting gender based on first names only. Additionally, we investigate the impact of adding birth years to enhance the accuracy of gender prediction, accounting for shifting associations between names and genders over time. Our findings indicate that most LLMs identify male and female names with high accuracy (over 80%) but struggle with gender-neutral names (under 40%), and the accuracy of gender prediction is higher for English-based first names than non-English names. The experimental results show that incorporating the birth year does not improve the overall accuracy of gender prediction, especially for names with evolving gender associations. We recommend using caution when applying LLMs for gender identification in downstream tasks, particularly when dealing with non-binary gender labels.- Anthology ID:
- 2024.gebnlp-1.16
- Volume:
- Proceedings of the 5th Workshop on Gender Bias in Natural Language Processing (GeBNLP)
- Month:
- August
- Year:
- 2024
- Address:
- Bangkok, Thailand
- Editors:
- Agnieszka Faleńska, Christine Basta, Marta Costa-jussà, Seraphina Goldfarb-Tarrant, Debora Nozza
- Venues:
- GeBNLP | WS
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 255–268
- Language:
- URL:
- https://aclanthology.org/2024.gebnlp-1.16
- DOI:
- Cite (ACL):
- Zhiwen You, HaeJin Lee, Shubhanshu Mishra, Sullam Jeoung, Apratim Mishra, Jinseok Kim, and Jana Diesner. 2024. Beyond Binary Gender Labels: Revealing Gender Bias in LLMs through Gender-Neutral Name Predictions. In Proceedings of the 5th Workshop on Gender Bias in Natural Language Processing (GeBNLP), pages 255–268, Bangkok, Thailand. Association for Computational Linguistics.
- Cite (Informal):
- Beyond Binary Gender Labels: Revealing Gender Bias in LLMs through Gender-Neutral Name Predictions (You et al., GeBNLP-WS 2024)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2024.gebnlp-1.16.pdf