Extracting intersectional stereotypes from embeddings: Developing and validating the Flexible Intersectional Stereotype Extraction procedure

Tessa E.S. Charlesworth*, Kshitish Ghate, Aylin Caliskan, Mahzarin R. Banaji

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Social group–based identities intersect. The meaning of “woman” is modulated by adding social class as in “rich woman” or “poor woman.” How does such intersectionality operate at-scale in everyday language? Which intersections dominate (are most frequent)? What qualities (positivity, competence, warmth) are ascribed to each intersection? In this study, we make it possible to address such questions by developing a stepwise procedure, Flexible Intersectional Stereotype Extraction (FISE), applied to word embeddings (GloVe; BERT) trained on billions of words of English Internet text, revealing insights into intersectional stereotypes. First, applying FISE to occupation stereotypes across intersections of gender, race, and class showed alignment with ground-truth data on occupation demographics, providing initial validation. Second, applying FISE to trait adjectives showed strong androcentrism (Men) and ethnocentrism (White) in dominating everyday English language (e.g. White + Men are associated with 59% of traits; Black + Women with 5%). Associated traits also revealed intersectional differences: advantaged intersectional groups, especially intersections involving Rich, had more common, positive, warm, competent, and dominant trait associates. Together, the empirical insights from FISE illustrate its utility for transparently and efficiently quantifying intersectional stereotypes in existing large text corpora, with potential to expand intersectionality research across unprecedented time and place. This project further sets up the infrastructure necessary to pursue new research on the emergent properties of intersectional identities.

Original languageEnglish (US)
JournalPNAS Nexus
Volume3
Issue number3
DOIs
StatePublished - Mar 1 2024

Funding

This research was supported by a Social Sciences and Humanities Research Council of Canada Postdoctoral Fellowship, and the Rand Innovation Fund from the Harvard Department of Psychology awarded to Tessa Charlesworth, and the Hodgson Innovation Fund from the Harvard Department of Psychology awarded to M.R.B. This work is supported by the National Institute of Standards and Technology (NIST) Grant 60NANB23D194. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author and do not necessarily reflect those of NIST.

Keywords

  • gender
  • intersectionality
  • race
  • stereotyping
  • word embeddings

ASJC Scopus subject areas

  • General

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