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

Tessa E S Charlesworth1, Kshitish Ghate2, Aylin Caliskan3

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Summary
This summary is machine-generated.

This study introduces Flexible Intersectional Stereotype Extraction (FISE) to analyze how social identities like gender, race, and class intersect in everyday language. It reveals dominant stereotypes and associated traits, highlighting biases in large text corpora.

Keywords:
genderintersectionalityracestereotypingword embeddings

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Area of Science:

  • Computational Social Science
  • Natural Language Processing
  • Sociolinguistics

Background:

  • Social identities like gender, race, and class are not experienced in isolation but intersect.
  • Understanding how these intersections manifest in large-scale language is crucial for social science research.
  • Existing methods struggle to quantify intersectional stereotypes efficiently across vast text data.

Purpose of the Study:

  • To develop and validate a novel computational method, Flexible Intersectional Stereotype Extraction (FISE), for analyzing intersectional stereotypes in everyday language.
  • To quantify the frequency and nature of stereotypes associated with various intersections of gender, race, and class.
  • To reveal biases and patterns in language use concerning social group identities.

Main Methods:

  • Development of the Flexible Intersectional Stereotype Extraction (FISE) procedure.
  • Application of FISE to word embeddings (GloVe, BERT) trained on large English internet text corpora.
  • Validation against ground-truth occupation demographics and analysis of trait adjective associations.

Main Results:

  • FISE successfully aligned with occupation demographics, validating its approach.
  • Demonstrated significant androcentrism and ethnocentrism, with 'White + Men' associated with 59% of traits, and 'Black + Women' with 5%.
  • Intersectional groups, particularly those involving 'Rich,' were associated with more frequent, positive, warm, competent, and dominant traits.

Conclusions:

  • FISE provides a transparent and efficient tool for quantifying intersectional stereotypes in large text corpora.
  • The findings reveal pervasive biases and highlight the differential attribution of qualities based on intersecting social identities.
  • This work establishes infrastructure for future research on intersectional identities and their emergent properties in language.