Charlesworth, T. E. S., et al. (2024).
PNAS Nexus, 3(3).
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.
Significance Statement
Stereotypes at the intersections of social groups (e.g. poor man) may induce unique beliefs not visible in parent categories alone (e.g. poor or men). Despite increased public and research awareness of intersectionality, empirical evidence on intersectionality remains understudied. Using large corpora of naturalistic English text, the Flexible Intersectional Stereotype Extraction procedure is introduced, validated, and applied to Internet text to reveal stereotypes (in occupations and personality traits) at the intersection of gender, race, and social class. The results show the dominance (frequency) and halo effects (positivity) of powerful groups (White, Men, and Rich), amplified at group intersections. Such findings and methods illustrate the societal significance of how language embodies, propagates, and even intensifies stereotypes of intersectional social categories.
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Here is a summary:
This article presents a novel method, the Flexible Intersectional Stereotype Extraction (FISE) procedure, for systematically identifying and validating intersectional stereotypes from language models.
Intersectional stereotypes, which capture the unique biases associated with the intersection of multiple social identities (e.g. race and gender), are a critical area of study for understanding and addressing prejudice and discrimination.
The ability to reliably extract and validate intersectional stereotypes from large language datasets can provide clinical psychologists with valuable insights into the cognitive biases and social perceptions that may influence clinical assessment, diagnosis, and treatment.
Understanding the prevalence and nature of intersectional stereotypes can help clinical psychologists develop more culturally-sensitive and inclusive practices, as well as inform interventions aimed at reducing bias and promoting equity in mental healthcare.
The FISE method demonstrated in this research can be applied to a variety of clinical and psychological datasets, allowing for the systematic study of intersectional biases across different domains relevant to clinical psychology.
In summary, this research on extracting and validating intersectional stereotypes is highly relevant for clinical psychologists, as it provides a rigorous approach to identifying and addressing the complex biases that can impact the assessment, diagnosis, and treatment of diverse patient populations.
