Analysis of Gender Bias in Social Perception and Judgement Using C hinese Word Embeddings (2022)

Abstract: Gender is a construction in line with social perception and judgment. An important means of this construction is through languages. When natural language processing tools, such as word embeddings, associate gender with the relevant categories of social perception and judgment, it is likely to cause bias and harm to those groups that do not conform to the mainstream social perception and judgment. Using 12,251 Chinese word embeddings as intermedium, this paper studies the relationship between social perception and judgment categories and gender. The results reveal that these grammatical gender-neutral Chinese word embeddings show a certain gender bias, which is consistent with the mainstream society’s perception and judgment of gender. Men are judged by their actions and perceived as bad, easily-disgusted, bad-tempered and rational roles while women are judged by their appearances and perceived as perfect, either happy or sad, and emotional roles.

## introduction one of the main ways to construct gender in society is through languages. people's languages towards infants of different genders can well illustrate the gender construction of languages as a medium. when people believe that infants are female, they talk to them more gently. when people believe that infants are male, they handle infants more playfully. through these differential treatments, boys and girls finally learn to be different @xcite . as the boys and girls grow up, they start to perform the "correct" gender manners to be consistent with the gender judgment and perception of mainstream society. in other words, gender possesses performativity @xcite . as a result, in the process † equal contribution. ‡ corresponding authors. as an analytic language, chinese does have referential gender and lexical gender, such as "她" means "she" in referential gender and "爸 爸" means "father" in lexical gender. however, chinese lacks grammatical gender, comparing to french, spanish and some of the fusional languages (cao and @xcite . as a result, it is difficult to find explicit and quantitative clues between gender and categories in social perception and judgement in chinese. word embedding is powerful and efficient in natural language processing (nlp). therefore, using word embeddings to find the implicit gender bias in chinese can be an appropriate tool to analyze the associations between gender and categories in social perception and judgement. to make it clear, we define four categories of social perception and judgment and the linguistic features that can measure their gender bias, as shown in in this paper, we first gave our definition of gender bias. then, by using semantic similarity, the implicit gender bias was measured in 12,251 chinese word embeddings. examples articulate that this measurement can capture the gendered word embeddings in a language without grammatical gender. then, part-of-speech, sentiment polarity, emotion category, and semantic category were labeled to each word. we analyzed the relationships between gendered word embeddings and linguistic features to find the associations between gender and different categories in social perception and judgement. results showed that we perceive and judge men and women with different social categories. men are judged by their actions and perceived as bad, easily-disgusted, bad-tempered ## bias statement in this paper, we study stereotypical associations between gender and different categories in social perception and judgment through chinese word embeddings. most of the chinese words are grammatical gender-neutral. however, if the chinese word embeddings show gender differences in different categories of part-of-speech, sentiment polarity, emotion category and semantic category, it may show that these gender-neutral word embeddings represent our stereotypes towards different genders. for example, we always judge a woman by her appearance but judge a man by his action. although these stereotypical generalizations may not be negative, once these stereotypical representations are used in downstream nlp applications, the system may ignore, or even do harms to those people who are not consistent with the mainstream social perception and judgement of gender. hence, this stereotypical association can be regarded as bias which may cause representational harms @xcite . in other words, the uniqueness between person and person is erased, and the system only retains gender differences. the ideal state is that people will not be treated unfairly because of their genders, especially to those are not consistent with the mainstream social perception and judgement of gender, and the system should not emphasize certain characteristics of a person according to one's gender. ## dataset the chinese word embeddings foot_0 we selected were pre-trained with baidu encyclopedia corpus, using word2vec model and the method of skip-gram with negative sampling (sgns). the size of baidu encyclopedia corpus is 4.1gb and the corpus contains 745m tokens @xcite . baidu encyclopedia is an open online encyclopedia like wikipedia, with entries covering almost all areas of chinese knowledge. the encyclopedia characteristic of baidu encyclopedia determines that the language it uses is more objective and genderneutral. the total amount of the word embeddings is 636,013 and each word embedding contains 300 dimensions. after labelling part-of-speech, sentiment polarity, emotion category, and semantic category, only 12,376 words contain all the information we need. then, we calculated odds ratio (or) values of each word and only selected those within three standard deviations from the mean. at last, we kept 12,251 word embeddings as our dataset. almost all the words are gender-neutral as chinese is a language without grammatical gender. different token numbers of chinese word embeddings in part-of-speech, sentiment polarity, emotion category, and semantic category are shown in part-of-speech. the part-of-speech labels were selected from affective lexicon ontology foot_1 @xcite . as we all know, the part-of-speech of many chinese words may change in different contexts. however, the chinese word embedding we chose is not contextualized. among the 12,251 words in our dataset, only 37 words are multicategory words. we thought that the number is small and would not affect the results and analysis. therefore, we chose one of the tags in affective lexicon ontology as its part-of-speech label for analysis. there are 7 labels of the part-of-speech. to balance the amount for analysis, we only chose the words labeled "noun", "verb" and "adjective" to compute and analyze. here, we assume that nouns and adjectives are related to the appearance of what people perceive and judge, while verbs are related to action. ## experiments in this section, we will illustrate the methodology to analyze the gendered word embeddings and how they are associated to different categories in our social perception and judgement. we first used semantic similarity and odds ratio to evaluate each word embedding. then, independent-samples t test, one-factor analysis of variance (anova) and 3 https://github.com/xls1994/cilin kruskal-wallis test were used respectively to analyze the relationships between gender and categories in social perception and judgement. ## results gendered word embeddings. we selected the top 5 masculine and feminine word embeddings of grammatical gender-neutral words according to the or g value showed in ## related works it was studied that word embeddings contain all kinds of biases in human society, including gen-der bias. these biases come from the biased data in the corpus which reflect the biased languages we use daily and from the bias of the annotators when they annotate the datasets @xcite . nlp algorithms may amplify the biases contained in the datasets @xcite . some word embeddings of neutral words such as "nurse", "social" were proved to have closer similarities with gender words (e.g. "male", "boy", "female", and "girl") @xcite @xcite @xcite @xcite . the latest contextualized word embeddings also have gender bias but the degree of the bias may not as much as that of traditional word embeddings @xcite @xcite . in addition, multilingual embeddings contain gender bias @xcite and the bias is related to the types of different languages @xcite . word embedding association test (weat) can be used to measure gender bias in word embeddings @xcite @xcite and this method can also be expanded to sentence level as sentence encoder association test (seat) @xcite . another method to detect and measure the gender bias in word embeddings is to analyze gender subspace in embeddings @xcite . but this method may not show the whole gender bias in word embeddings. some of the implicit gender bias cannot be measured and caught @xcite . ## conclusion in this paper, we used word embeddings to detect and measure the implicit gender bias in a language without grammatical gender. relationships between gender and four categories in social perception and judgement are also shown according to our measurement values. word embeddings show that we judge a woman by her appearance and perceive her as a "perfect", either happy or sad, and emotional role while we judge a man by his action and perceive him as a "bad", easily-disgusted, bad-tempered, and rational role. it may cause gender bias. this systematic bias intensifies gender differences, solidifies stereotypes about men and women, erases the uniqueness of differences between person and person, and harms those do not conform to mainstream social perception and judg-ment and those who do not fit in the gender dichotomy. in the future, we can choose more dimensions rather than man/woman for investigation, such as in-group/inter-group, animate/inanimate, collectivism/individualism, etc.