Gender Bias in Word Embeddings: A Comprehensive Analysis of Frequency, Syntax, and Semantics

• URL: http://arxiv.org/abs/2206.03390v1
• Date: Tue, 7 Jun 2022 15:35:10 GMT
• Title: Gender Bias in Word Embeddings: A Comprehensive Analysis of Frequency, Syntax, and Semantics
• Authors: Aylin Caliskan, Pimparkar Parth Ajay, Tessa Charlesworth, Robert Wolfe, Mahzarin R. Banaji
• Abstract summary: We provide a comprehensive analysis of group-based biases in widely-used static English word embeddings trained on internet corpora. Using the Single-Category Word Embedding Association Test, we demonstrate the widespread prevalence of gender biases. We find that, of the 1,000 most frequent words in the vocabulary, 77% are more associated with men than women.
• Score: 3.4048739113355215
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The statistical regularities in language corpora encode well-known social biases into word embeddings. Here, we focus on gender to provide a comprehensive analysis of group-based biases in widely-used static English word embeddings trained on internet corpora (GloVe 2014, fastText 2017). Using the Single-Category Word Embedding Association Test, we demonstrate the widespread prevalence of gender biases that also show differences in: (1) frequencies of words associated with men versus women; (b) part-of-speech tags in gender-associated words; (c) semantic categories in gender-associated words; and (d) valence, arousal, and dominance in gender-associated words. First, in terms of word frequency: we find that, of the 1,000 most frequent words in the vocabulary, 77% are more associated with men than women, providing direct evidence of a masculine default in the everyday language of the English-speaking world. Second, turning to parts-of-speech: the top male-associated words are typically verbs (e.g., fight, overpower) while the top female-associated words are typically adjectives and adverbs (e.g., giving, emotionally). Gender biases in embeddings also permeate parts-of-speech. Third, for semantic categories: bottom-up, cluster analyses of the top 1,000 words associated with each gender. The top male-associated concepts include roles and domains of big tech, engineering, religion, sports, and violence; in contrast, the top female-associated concepts are less focused on roles, including, instead, female-specific slurs and sexual content, as well as appearance and kitchen terms. Fourth, using human ratings of word valence, arousal, and dominance from a ~20,000 word lexicon, we find that male-associated words are higher on arousal and dominance, while female-associated words are higher on valence.

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