Theory-Grounded Measurement of U.S. Social Stereotypes in English Language Models
Yang Trista Cao, Anna Sotnikova, Hal Daumé III, Rachel Rudinger, Linda Zou
Abstract
NLP models trained on text have been shown to reproduce human stereotypes, which can magnify harms to marginalized groups when systems are deployed at scale. We adapt the Agency-Belief-Communion (ABC) stereotype model of Koch et al. (2016) from social psychology as a framework for the systematic study and discovery of stereotypic group-trait associations in language models (LMs). We introduce the sensitivity test (SeT) for measuring stereotypical associations from language models. To evaluate SeT and other measures using the ABC model, we collect group-trait judgments from U.S.-based subjects to compare with English LM stereotypes. Finally, we extend this framework to measure LM stereotyping of intersectional identities.
BibTeX
@inproceedings{cao-etal-2022-theory,
title = "Theory-Grounded Measurement of {U}.{S}. Social Stereotypes in {E}nglish Language Models",
author = "Cao, Yang Trista and
Sotnikova, Anna and
Daum{\'e} III, Hal and
Rudinger, Rachel and
Zou, Linda",
editor = "Carpuat, Marine and
de Marneffe, Marie-Catherine and
Meza Ruiz, Ivan Vladimir",
booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.naacl-main.92/",
doi = "10.18653/v1/2022.naacl-main.92",
pages = "1276--1295"
}