Disagreeable, Slovenly, Honest and Un-named Women? Investigating Gender Bias in Educational Resources by Extending Existing Gender Bias Taxonomies

Anonymous

Abstract: Gender bias has been extensively studied in both the educational field and the Natural Language Processing (NLP) field, the former using human coding to identify patterns associated with and causes of gender bias in text and the latter to detect, measure and mitigate gender bias in NLP output and models. This work aims to use NLP to facilitate automatic, quantitative analysis of educational text within the framework of a gender bias taxonomy. Analyses of both educational texts and a lexical resource (WordNet) reveal patterns of bias that can inform and aid educators in updating textbooks and lexical resources and in designing assessment items.

Paper Type: long

Research Area: Ethics, Bias, and Fairness

Contribution Types: Data analysis

Languages Studied: English

Preprint Status: There is no non-anonymous preprint and we do not intend to release one.

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A1 Elaboration For Yes Or No: Section 7

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A2 Elaboration For Yes Or No: Section 8

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A3 Elaboration For Yes Or No: Abstract and Section 1

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B3 Elaboration For Yes Or No: Section 3

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B4 Elaboration For Yes Or No: Section 3

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B5 Elaboration For Yes Or No: Section 3.1

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B6 Elaboration For Yes Or No: Section 3.1 (Table 2)

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C1 Elaboration For Yes Or No: Our computational experiments are statistical analysis of data, where we report the sample size, the significance test employed, statistical significance obtained and effect sizes.

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C2 Elaboration For Yes Or No: Section 4

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C3 Elaboration For Yes Or No: Section 4

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C4 Elaboration For Yes Or No: Section 3

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