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The rise of concern around Natural Language Processing (NLP) technologies containing and perpetuating social biases has led to a rich and rapidly growing area of research. Gender bias is one of the central biases being analyzed, but to date there is no comprehensive analysis of how “gender” is theorized in the field. We survey nearly 200 articles concerning gender bias in NLP to discover how the field conceptualizes gender both explicitly (e.g. through definitions of terms) and implicitly (e.g. through how gender is operationalized in practice). In order to get a better idea of emerging trajectories of thought, we split these articles into two sections by time.

We find that the majority of the articles do not make their theo- rization of gender explicit, even if they clearly define “bias.” Almost none use a model of gender that is intersectional or inclusive of non- binary genders; and many conflate sex characteristics, social gender, and linguistic gender in ways that disregard the existence and expe- rience of trans, nonbinary, and intersex people. There is an increase between the two time-sections in statements acknowledging that gender is a complicated reality, however, very few articles manage to put this acknowledgment into practice. In addition to analyzing these findings, we provide specific recommendations to facilitate interdisciplinary work, and to incorporate theory and methodol- ogy from Gender Studies. Our hope is that this will produce more inclusive gender bias research in NLP.

Gender bias in natural language processing (NLP) tools, deriving from implicit human bias embedded in language data, is an important and complicated problem on the road to fair algorithms. We leverage topic modeling to retrieve documents associated with particular gendered categories, and discuss how exploring these documents can inform our understanding of the corpora we may use to train NLP tools. This is a starting point for challenging the systemic power structures and producing a justice-focused approach to NLP.

Gender bias has been identified in many models for Natural Language Processing, stemming from implicit biases in the text corpora used to train the models. Such corpora are too large to closely analyze for biased or stereotypical content. Thus, we argue for a combination of quantitative and qualitative methods, where the quantitative part produces a view of the data of a size suitable for qualitative analysis. We investigate the usefulness of semi-supervised topic modeling for the detection and analysis of gender bias in three corpora (mainstream news articles in English and Swedish, and LGBTQ+ web content in English). We compare differences in topic models for three gender categories (masculine, feminine, and nonbinary or neutral) in each corpus. We find that in all corpora, genders are treated differently and that these differences tend to correspond to hegemonic ideas of gender.