TITLE: Gender Bias in Text: Labeled Datasets and Lexicons 

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.
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.
Gender Bias in Text: Labeled Datasets and Lexicons
http://arxiv.org/abs/2201.08675v2