Examining Covert Gender Bias: A Case Study in T urkish and E nglish Machine Translation Models (2021)

Abstract: As Machine Translation (MT) has become increasingly more powerful, accessible, and widespread, the potential for the perpetuation of bias has grown alongside its advances. While overt indicators of bias have been studied in machine translation, we argue that covert biases expose a problem that is further entrenched. Through the use of the gender-neutral language Turkish and the gendered language English, we examine cases of both overt and covert gender bias in MT models. Specifically, we introduce a method to investigate asymmetrical gender markings. We also assess bias in the attribution of personhood and examine occupational and personality stereotypes through overt bias indicators in MT models. Our work explores a deeper layer of bias in MT models and demonstrates the continued need for language-specific, interdisciplinary methodology in MT model development.

## introduction various forms of biases are encoded in the way that people use language @xcite . similar to other natural language processing (nlp) tasks, learned models used in mt systems include social biases as they learn correlations from their training data that have encoded stereotypes. specifically, several studies @xcite @xcite have shown that translations from a gender-neutral language to a language with gendered pronouns are biased in the selection of pronouns in the target language. however, this is not the only way bias can manifest in mt. for example, figure 1 demonstrates marked gender in the female case of the same sentence while remaining neutral in the male case. since the translations are both accurate, unless the * * equal contribution. figure 1: using google translate, "my sister is a soccer player" accurately translates to "my female sibling is a soccer player" while "my brother is a soccer player" is translated to "my sibling is a soccer player". gender is overtly marked only when the subject is female. while previous mitigation efforts have focused on debiasing training data @xcite @xcite , the issue of covert bias has not been adequately addressed, and goes far beyond the perpetuation of outdated stereotypes. in order to ensure that the true meaning of the source is accurately represented during the translation process, understanding the linguistic and social context of the utterance is necessary. in this paper, we examine both overt and covert gender biases in commercially-used mt models through the use of a gender-neutral language, turkish, and a gendered language, english. our study investigates explicit stereotype bias through the assignment of pronouns according to stereotypes regarding occupation and personality. we also investigate how additional qualifiers to job descriptions affect results: for example, are "good doctors" more likely to be men than "bad" ones? similarly, we measure how a reference to personhood changes pronoun results. lastly, we shed light on the presence of asymmetrical gender in mt models by analyzing explicit gender markings in turkish translations of gender-specific english sentences. we not only ask if gender markings occur more for female subjects, but also if gender markings are more likely when the stereotype of the predicate does not align with the gender of the subject. to this end, we created a parallel corpus of 1,617 turkish and english job titles. we also compiled a list of descriptive adjectives based on turkish stereotypes and formed appropriate turkish sentences with and without a reference to personhood. lastly, we formed a dataset of english sentences by pairing a gendered english subject word (that has no gendered translation in turkish) with a genderstereotyped action or description. our code and data can be found in our github repository. foot_0 2 related work previous works on bias in embeddings and models @xcite @xcite , as well as corpora @xcite , have demonstrated that gender bias exists in the core of mt models. additionally, @xcite introduced a challenge set in measuring bias from english to languages with morphological gender. one common approach in bias evaluation is to translate from a gender-neutral language to a gendered language and examine the pronouns selected for occupations and adjectives @xcite @xcite . we used a modified version of these methods by ensuring that the occupation exists in the target language as well as the source language and that the adjectives used are actual stereotypes in turkey @xcite foot_1 . our remaining experiments are inspired by socio-linguistics research in turkish. first, @xcite discusses how neutral turkish words describing people, such as insan ("human"), tend to be biased towards male interpretations. in nlp, @xcite examines a related bias in english named-entity recognition where fewer female names are recognized as "person" entities than male ones. our work will similarly examine gender and personhood bias but in mt models. second, @xcite describes asymmetrical gender markings in the turkish language, concluding that male gender remains unmarked regardless of context, whereas female gender tends to be overly expressed. for example, female children are more likely to be referred to with marked gender (kız c ¸ocugu "girl child" instead of c ¸ocuk "child") than male children. the exception to this pattern is when the subject is exceptionally stereotyped as feminine (e.g. hizmetc ¸i "househelper"). we will extend the study of this phenomenon to mt. ## experiments we used four commercially available mt models in our experiments: google translate, amazon translate, microsoft translator, and systran. for reproducibility purposes, all translations were executed in april of 2021. all datasets can be found on our github 1 . ## results in this section, we evaluate 8 aggregate results across all experiments. ## conclusion we have examined gender bias exhibited by commercially used mt models in the case of turkish and english translations. we have shown evidence of overt gender bias through occupation and adjective stereotypes, and covert gender bias through asymmetrical gender and personhood bias. furthermore, our experiments show consistent evidence of male bias in a neutral context. male gender was assumed in reference to gender-equal occupations and stereotype-neutral adjectives, and the same phenomenon extends to the manifestation of overt gender markings where male subjects were more likely to be assigned the neutral case. however, when the context was not neutral, stereotype bias routinely affected results across all experiments. previous bias mitigation discussions have focused on fair pronoun assignments @xcite @xcite . additionally, google translate has recently implemented a gender-specific translation feature (kuczmarski, 2018; johnson, 2020). while pronoun assignment is a salient and ongoing concern, our study demonstrates how the problem of gender bias can be far more complex. our experiments show that domain and cultural knowledge are required and these techniques are not necessarily transferable across languages. we advocate for the inclusion of language-specific differences and the design of mitigation models that are linguistically aware and socially grounded. we hope that our work will bring more attention to such interdisciplinary work, prompt continued research in how gender bias is expressed in nlp, and assist with mitigation efforts.