Some Myths About Bias: A Queer Studies Reading Of Gender Bias In NLP

This paper critiques common assumptions about gender bias in NLP, focusing primarily on word vector-based methods for detecting and mitigating bias.It argues that these methods assume a kind of "binary thinking" that goes beyond the gender binary toward a conceptual model that structures and limits the effectiveness of these techniques.Drawing its critique from the Humanities field of Queer Studies, this paper demonstrates that binary thinking drives two "myths" in gender bias research: first, that bias is categorical, measuring bias in terms of presence/absence, and second, that it is zero-sum, where the relations between genders are idealized as symmetrical.Due to their use of binary thinking, each of these myths flattens bias into a measure that cannot distinguish between the types of bias and their effects in language.The paper concludes by briefly pointing to methods that resist binary thinking, such as those that diversify and amplify gender expressions. Bias StatementThis paper adopts a framework from Nemani et al. ( 2023) that organizes bias into the categories of "denigration", "underrepresentation", and "stereotype", within the larger category of "representational harms," further elaborated in Section 3. It assumes that bias is inherent to language systems, and it demonstrates how some methods that attempt to excise bias from language focus on a binary structure of thought that miss the opportunity to imagine alternative mitigation strategies.