Gender Bias in Text: Labeled Datasets and Lexicons

• URL: http://arxiv.org/abs/2201.08675v1
• Date: Fri, 21 Jan 2022 12:44:51 GMT
• Title: Gender Bias in Text: Labeled Datasets and Lexicons
• Authors: Jad Doughman, Wael Khreich
• Abstract summary: There is a lack of gender bias datasets and lexicons for automating the detection of gender bias. We provide labeled datasets and exhaustive lexicons by collecting, annotating, and augmenting relevant sentences. The released datasets and lexicons span multiple bias subtypes including: Generic He, Generic She, Explicit Marking of Sex, and Gendered Neologisms.
• Score: 0.30458514384586394
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Language has a profound impact on our thoughts, perceptions, and conceptions of gender roles. Gender-inclusive language is, therefore, a key tool to promote social inclusion and contribute to achieving gender equality. Consequently, detecting and mitigating gender bias in texts is instrumental in halting its propagation and societal implications. However, there is a lack of gender bias datasets and lexicons for automating the detection of gender bias using supervised and unsupervised machine learning (ML) and natural language processing (NLP) techniques. Therefore, the main contribution of this work is to publicly provide labeled datasets and exhaustive lexicons by collecting, annotating, and augmenting relevant sentences to facilitate the detection of gender bias in English text. Towards this end, we present an updated version of our previously proposed taxonomy by re-formalizing its structure, adding a new bias type, and mapping each bias subtype to an appropriate detection methodology. The released datasets and lexicons span multiple bias subtypes including: Generic He, Generic She, Explicit Marking of Sex, and Gendered Neologisms. We leveraged the use of word embedding models to further augment the collected lexicons. The underlying motivation of our work is to enable the technical community to combat gender bias in text and halt its propagation using ML and NLP techniques.

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