Gender Bias in Text: Origin, Taxonomy, and Implications (2021)

Abstract: Gender inequality represents a considerable loss of human potential and perpetuates a culture of violence, higher gender wage gaps, and a lack of representation of women in higher and leadership positions. Applications powered by Artificial Intelligence (AI) are increasingly being used in the real world to provide critical decisions about who is going to be hired, granted a loan, admitted to college, etc. However, the main pillars of AI, Natural Language Processing (NLP) and Machine Learning (ML) have been shown to reflect and even amplify gender biases and stereotypes, which are mainly inherited from historical training data. In an effort to facilitate the identification and mitigation of gender bias in English text, we develop a comprehensive taxonomy that relies on the following gender bias types: Generic Pronouns, Sexism, Occupational Bias, Exclusionary Bias, and Semantics. We also provide a bottom-up overview of gender bias, from its societal origin to its spillover onto language. Finally, we link the societal implications of gender bias to their corresponding type(s) in the proposed taxonomy. The underlying motivation of our work is to help enable the technical community to identify and mitigate relevant biases from training corpora for improved fairness in NLP systems.

## introduction bias is prevalent in every aspect of our lives. we are hardwired to compartmentalize things we experience to form a plausible perception of the world around us. the process of forming these perceptions typically breeds prejudices, which allows for flagrant inequalities to shape across different demographics. the prevalence of certain biases in society, such as gender bias, can be attributed to social roles formed as a function of this compartmentalization process. according to the social role theory, the societal origin of gender stereotypes revolves around gender-typical social roles that mirror the sexual division of labor and gender hierarchy of the society @xcite . the prevalence of gender bias in society is also spilled over onto language through the patriarchal worldview predominant among linguists prior to the prescriptive grammar movement in english. @xcite found that the generic use of he is derived from an androcentric worldview prevalent among 18th-century grammarians: "human beings were to be considered male unless proven otherwise" @xcite . the perpetuation of bias onto language entails a negative feedback loop due to the direct impact of language on a person's perceptions @xcite . linguistic determinism, a hypothesis taken from the analytic branch of philosophy, posits that language "limits and determines human thought patterns and knowledge" @xcite . hence, the recurring usage of bias in language consequently leads to a more biased perception which is fed back into our lexical (word) choice. this is even more amplified by the increased adoption of automated system based on ai, which exponentially expedites this feedback loop (as detailed in section 2.4). the linguistic spillover of gender bias has various direct and indirect implications on our society. the presence of gender bias in the language used by parents and in school text books causes children to develop sexist perceptions and behaviors towards other children of opposite gender and deepens the problematic outcomes of gender inequalities in society @xcite . additionally, sex-biased wording affects a person's perception of a career's attractiveness @xcite . consequently, countries that adopt a gendered language tend to have disproportionate labor force participation @xcite . we also discuss the direct implication of hostile sexism on a person's physiological wellbeing, such as increased stress levels, anger, and elevated cardiovascular reactivity @xcite . finally, we examine the indirect implication of benevolent sexism in embedding gender inequality and intensifying its influence in the society by portraying the advantageous aspects of being a woman (deserving special treatment, care, protection, and love) @xcite . gender bias in nlp presents itself in many stages along the design and development process. it can be found in the training data, the pre-trained models, and the algorithms themselves. the propagation of bias from text to features and algorithms leads to real-world consequences when integrated into ai systems and are used in critical decision-making applications. in particular, discriminatory decisions occur when these systems assist humans in critical decisions @xcite . these prejudiced decisions could entail allocational or representational harms @xcite . as mentioned previously, discriminating algorithms accelerate the unavoidable feedback loop, which increases the degree and volume of bias against females and other gender minority groups, especially in online media content. automated nlp-based decision-making algorithms will re-consume this increasingly growing biased content to update their models, and so on. this feedback loop contributes to an increased gender bias and further discrimination. several works in nlp revolving around bias focused on the projection of word embedding vectors on a gender direction (he -she) to detect and mitigate bias in a pre-trained model, without a clear link to the implications on society and their underlying applications @xcite . there has been previous attempts that address bias at the sentence level and provide an initial categorization of gender bias types @xcite . we build on their work and provide a more comprehensive understanding of the various forms of gender bias while linking to several real world implications on society. in this paper, we develop a comprehensive taxonomy to identify various types of gender bias. we also provide a bottom-up overview of gender bias, from its societal origin to its spillover onto language. we then link between the psycho-social implications of gender bias and the corresponding type(s) in the proposed taxonomy. our underlying motivation is to enable the the technical community working on gender bias in nlp to focus on the identification and mitigation of relevant biases for improved fairness in nlp systems. we also hope that by addressing and linking the sources and implications of gender bias in text, we encourage the community to further push the research in this direction and raise more awareness on bias and discrimination in nlp systems. ## taxonomy the first step of detecting biased language is to categorize the various forms of that bias while carefully maintaining a clear segregation between the resultant groups. the below sections develop a comprehensive taxonomy that includes a wide range of gender bias types and their subsequent subtypes. each subsection includes the definition of a bias subtype and a couple of examples that illustrate its usage in a sentence. ## conclusion in this paper, we propose a comprehensive gender bias taxonomy that distinguishes between the various forms of gender biases in english text. the taxonomy includes various exclusionary, implicitly prejudicial, and generalized forms of biased gender representations in text. our work also provides a bottom-up understanding of gender bias, highlighting the social role theory and its impact on gender stereotypes in society. we also explain how societal gender bias spilled over onto language while being fed back into our perceptions as stated in the sapir-whorf hypothesis. we hope that our comprehensive taxonomy of gender bias enables the technical community working on gender bias in nlp to focus on the identification and mitigation of relevant biases in text for improved fairness in nlp systems. we also hope that by addressing and connecting the sources and implications of gender bias in text from a linguistic, sociological, and real-life perspective, we would encourage the community to further push the research in this direction and raise more awareness on bias and discrimination in nlp systems. in future work, we will work on expanding the taxonomy to include other languages and address other forms of bias such as racial and ethnic biases.