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Relating Word Embedding Gender Biases to Gender Gaps: A Cross-Cultural Analysis
Scott Friedman, Sonja Schmer-Galunder, Anthony Chen, Jeffrey Rye
TL;DR
The paper asks whether word-embedding gender biases can reveal statistical gender gaps in the cultures that produced the training text. It quantifies biases in culture-specific Twitter embeddings and correlates them with gender-gap measures, finding selective, thematically meaningful associations across countries and U.S. regions. The authors also identify adjective-bias patterns linked to education and economic gaps, while noting important coverage limitations from English-only Twitter data.
Problem
Embedding biases can harm NLP models, but they may also reflect cultural gender disparities; the paper addresses limited evidence connecting the two.
Method
The paper trains culture-specific Twitter word embeddings, computes gender-bias measures, and correlates them with 18 international and 5 U.S.-based gender-gap statistics.
Results
Thematically grouped word-set biases selectively correlate with gender gaps of similar topics, while random word sets do not; adjective biases also differ in valence and dominance across education and economic gaps.
Takeaways & Limitations
Word-embedding biases can characterize cultural gender gaps by their strength, direction, and thematic dimension.
Takeaways & Limitations
English-only Twitter data omit native-language expression and people who lack technology access, have limited English knowledge, or do not use Twitter.
Abstract
from arXiv · showhide
Modern models for common NLP tasks often employ machine learning techniques and train on journalistic, social media, or other culturally-derived text. These have recently been scrutinized for racial and gender biases, rooting from inherent bias in their training text. These biases are often sub-optimal and recent work poses methods to rectify them; however, these biases may shed light on actual racial or gender gaps in the culture(s) that produced the training text, thereby helping us understand cultural context through big data. This paper presents an approach for quantifying gender bias in word embeddings, and then using them to characterize statistical gender gaps in education, politics, economics, and health. We validate these metrics on 2018 Twitter data spanning 51 U.S. regions and 99 countries. We correlate state and country word embedding biases with 18 international and 5 U.S.-based statistical gender gaps, characterizing regularities and predictive strength.
1 Introduction
The paper frames embedding biases as both harmful model artifacts and potential signals of cultural gender disparities. It proposes comparing culture-specific embedding biases with statistical gender gaps across U.S. states and countries.
- Embedding biases can produce systematic NLP mistakes, motivating efforts to debias models and remove stereotypes.
- Prior studies linked language-model biases to cultural disparities using surveys, vector analogies, and models trained on texts from different decades.
- The paper trains separate Twitter-based embeddings for 51 U.S. regions and 99 countries, then correlates them with 5 U.S. and 18 international gender-gap statistics.
- The authors claim that language biases associate with gender gaps, can be characterized by correlation strength and direction, and vary across themed word sets.
- The paper combines computational analysis with cultural research on asymmetrical gender valuations and organizes the study around gender gaps, training data, experiments, and discussion.
2 Gender Gaps and Statistics
Gender gaps are presented as disparities in opportunities and status associated with culturally asymmetric valuations of public and private domains. The study therefore uses diverse statistics to measure these disparities across cultures.
- Public-sphere traits and roles are often associated with men, while domestic and relationship domains are traditionally associated with women.
- Gender gaps are discrepancies in opportunities and status across genders.
- These asymmetric valuations are linked to men’s typical over-representation and higher salaries relative to women.
- The international analysis uses 18 gender-gap metrics from the World Economic Forum’s 2018 Global Gender Gap Report.
3 Training Data
The study builds culture-specific English Twitter corpora from 2018 location-tagged tweets, preprocessing them and separating U.S. state from country datasets. Word2Vec supplies the primary embeddings, with comparisons to other algorithms.
- The training data contain public English tweets from U.S. and international users collected over 100 days throughout 2018.
- Tweets with fewer than three words were removed, and URLs, usernames, hashtags, images, and emojis were replaced with tokens.
- Processed tweets were divided into U.S. state and country datasets to validate the approach at multiple granularities.
- The international dataset covers 99 countries, while the U.S. dataset covers 51 regions, with tweet counts varying substantially across locations.
- Cultures exceeding 10 million tweets were sampled down to 10 million, and Word2Vec was compared with other embedding algorithms.
4 Experiments
Across international and U.S. analyses, themed word-embedding biases were correlated with statistical gender gaps, with patterns varying by thematic set and domain. The experiments also compared bias metrics and embedding algorithms and examined adjective valence and dominance.
- The international and U.S. analyses used identical experimental setups, varying only the word embeddings and gender-gap statistics.
- The primary gender-bias measure was the average projection of a neutral word set onto the male–female axis.
- International Analysis: Women’s political influence and power increased relative to men as political language became more female-biased across 99 countries.
- International Analysis: Thematic word sets showed domain-specific correlations: political terms aligned with political empowerment, while intellectual and workplace terms aligned with economic statistics.
- U.S. State Analysis: In U.S. regions, threat words indirectly correlated with pay equality at R2 = 0.51, while other thematic sets aligned with exercise, legislators, and STEM-degree gaps.
- Algorithm Comparison: Word2Vec with axis projection produced the highest determination across tested direct and indirect correlations, so it was used in subsequent experiments.
5 Conclusions
Across 99 countries and 51 U.S. regions, thematically grouped word-set biases correlated selectively and meaningfully with related statistical gender gaps. The study also identified cross-cutting adjective patterns, while emphasizing that English-only data and correlational analyses limit interpretation.
- The study analyzed Twitter-derived word-embedding gender biases across 99 countries and 51 U.S. regions against 18 international and 5 U.S.-based gender-gap statistics.
- Thematically grouped word sets correlated with gender gaps of similar topics in meaningful positive or negative directions, while random and unrelated topical sets generally did not.These selective correlations support using themed word sets to capture different dimensions of gender bias and gender gaps.
- Adjectives associated with increased gender gaps had statistically significantly lower valence and dominance than adjectives associated with decreased gaps in education and economics.The authors interpret this pattern as evidence of a cross-cutting attitude toward gender for future study.
- The results align with social theory linking implicit gender valuation to differences in gender opportunities, status, rewards, and evaluations.
- English-only tweets exclude people lacking technology access, English proficiency, or Twitter use, creating cultural blind spots and motivating analysis of additional languages and cultural texts.
- The observed relationships are correlational rather than causal and have limited interpretive power without additional data and causal models.