Nichelle and Nancy: The Influence of Demographic Attributes and Tokenization Length on First Name Biases
Abstract
Through the use of first name substitution experiments, prior research has demonstrated the tendency of social commonsense reasoning models to systematically exhibit social biases along the dimensions of race, ethnicity, and gender (An et al., 2023). Demographic attributes of first names, however, are strongly correlated with corpus frequency and tokenization length, which may influence model behavior independent of or in addition to demographic factors. In this paper, we conduct a new series of first name substitution experiments that measures the influence of these factors while controlling for the others. We find that demographic attributes of a name (race, ethnicity, and gender) and name tokenization length are both factors that systematically affect the behavior of social commonsense reasoning models.
BibTeX
@inproceedings{an-rudinger-2023-nichelle,
title = "Nichelle and Nancy: The Influence of Demographic Attributes and Tokenization Length on First Name Biases",
author = "An, Haozhe and
Rudinger, Rachel",
editor = "Rogers, Anna and
Boyd-Graber, Jordan and
Okazaki, Naoaki",
booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.acl-short.34/",
doi = "10.18653/v1/2023.acl-short.34",
pages = "388--401"
}