Comparing Biases and the Impact of Multilingual Training across Multiple Languages (2023)

Abstract: Studies in bias and fairness in natural language processing have primarily examined social biases within a single language and/or across few attributes (e.g. gender, race). However, biases can manifest differently across various languages for individual attributes. As a result, it is critical to examine biases within each language and attribute. Of equal importance is to study how these biases compare across languages and how the biases are affected when training a model on multilingual data versus monolingual data. We present a bias analysis across Italian, Chinese, English, Hebrew, and Spanish on the downstream sentiment analysis task to observe whether specific demographics are viewed more positively. We study bias similarities and differences across these languages and investigate the impact of multilingual vs. monolingual training data. We adapt existing sentiment bias templates in English to Italian, Chinese, Hebrew, and Spanish for four attributes: race, religion, nationality, and gender. Our results reveal similarities in bias expression such as favoritism of groups that are dominant in each language’s culture (e.g. majority religions and nationalities). Additionally, we find an increased variation in predictions across protected groups, indicating bias amplification, after multilingual finetuning in comparison to multilingual pretraining.

## introduction the growth of interest in natural language processing (nlp) has led to investigations of the various social biases learned by models. while researchers are actively studying bias and fairness in nlp models, they typically focus on a single language, primarily english @xcite @xcite . however, biases can manifest differently across languages (e.g., though biases may vary across different languages and attributes, these may also be affected by the data the models are trained on. previous studies have shown the impact of multilingual versus monolingual training data on a model's task performance @xcite . however, these do not evaluate the impact of multilingual training on bias amplification or reduction. in this paper, we present an analysis of four demographic attributes (race, religion, nationality, gender) across five languages: italian, chinese, english, hebrew, and spanish. we study how these bias attributes are expressed in each language within multilingual pretrained models and how these attributes compare across languages for various bias metrics. we focus our study on the sentiment analysis task. specifically, our research questions are 1) how does task performance com-pare across languages on a parallel human-written test set?, 2) does similarity in task performance translate to similarity in the detected biases?, and 3) does multilingual data reduce/amplify biases? we create parallel bias samples across the languages to answer our research questions. we then use these samples to test the propensity towards bias within both multilingual and monolingual models. our contributions are: ## related work research in bias and fairness has primarily focused on individual languages, with most studies in english @xcite @xcite @xcite . nadeem et al. (2021) measures stereotypical bias across gender, profession, race, and religion attributes in various transformerbased models. czarnowska et al. (2021) evaluates age, disability, nationality, gender, race, religion, and sexual orientation attributes across various bias metrics on the sentiment analysis and named entity recognition downstream tasks. sap et al. (2020) introduces social bias frames, a framework to categorize and explain how statements may project biases or offensive assumptions onto various demographic groups. nangia et al. (2020) proposes crows-pairs, a dataset used to contrastively evaluate biases between demographic groups across nine types of bias. while bias studies in english cover a wide range of tasks and demographic groups, the resulting findings of these studies are only applicable to english-based models and cannot be extended to other languages. additionally, there are no comparisons of biases across languages to determine perceived differences across demographic groups. while english is the primary language examined in bias research, there are several non-english bias studies. @xcite extends crows-pairs to investigate various biases in french. @xcite discusses the disparity between western and indian fairness values and lists several types of bias relevant to india. meanwhile, @xcite analyzes biases in hindi and focuses on a subgroup of the proposed biases, including caste and religion bias. @xcite proposes evaluation metrics and mitigation methods for gender bias in grammatically gendered languages, with experiments on french and spanish text. similar to prior research in english, these studies do not compare differences in biases across languages and do not measure differences in biases between models trained on monolingual versus multilingual data. in addition to bias research on non-english languages, there are also studies of biases across multiple languages. @xcite analyze ethnicity bias across six languages and attempt to mitigate biases seen in monolingual models through the use of multilingual models. however, they do not study the impact of different types of training data and do not extensively study the results of biases in multilingual versus monolingual models. @xcite evaluates gender bias in masked language models across nine languages. @xcite proposes multilingual fairness metrics in multimodal vision-language models. @xcite analyzes gender, race, and ethnicity bias across english, spanish, and arabic for the sentiment analysis task. however, identity terms are not explicitly mentioned in the text and are instead implied through names representing the attributes. cabello piqueras and søgaard (2022) creates parallel cloze test sets across english, spanish, german, and french, but the samples are not created with the intent of studying disparities across demographics. while there is extensive research examining biases across various languages, previous work has not evaluated biases on downstream tasks across h: phase 1 e compare luated on a odel for task set across on task 13 multilingual model english italian chinese training data finetune … high-performing languages low-performing languages figure 1: phase 1, task performance. a multilingual model is finetuned on multilingual data and evaluated on a parallel sentiment analysis test set. ch: phase 2 quivalent to robing for esponding lates for task terms r languages d multilingual pare across sk 14 multilingual model language 1 language 3 language 2 bias samples compare language 4 language 5 compare high-performing low-performing several attributes and languages of linguistic diversity. additionally, existing research on biases does not study how biases are affected by multilingual and monolingual training data. ## approach we outline the various phases of our study below. these are also visualized in figures 1 , 2 , 3, and 4. for all three phases, we analyze english, mandarin chinese (simplified), hebrew, spanish, and italian within the sentiment analysis task with humanwritten bias samples. definitions we first define related terms from @xcite that are used throughout the paper. an attribute is used to describe a userbased sensitive category (e.g. religion). within an attribute, there are several groups that are each used to describe a protected group (e.g. buddhism). for each group, there are one or more identity terms that are used to express that group (e.g. buddhist and buddhism). we list the attributes and corresponding groups in our analysis in ## data we describe the datasets used below. additional details are provided in appendix a.2 fine-tuning data as there are no large-scale binary sentiment analysis datasets covering our five languages, we utilize datasets from different domains for sentiment analysis fine-tuning: multilingual amazon review corpus (marc) @xcite for english, chinese, and spanish, @xcite for hebrew, and sentipolc @xcite and absita @xcite for italian. to provide a fairer evaluation of task performance, we downsample the collected datasets so that the number of positive and negative samples is equal across all languages. our data statistics are shown in parallel test data to evaluate and compare task performance across our languages, we need to collect a sentiment analysis test set with parallel samples from the same domain. we utilize xed (öhman et al., 2020) as our initial dataset, which con- ## conclusion in this paper, we analyze various bias attributes across five languages on the downstream sentiment analysis task. our results show that 1) bias is exhibited differently across different languages and models do not exhibit consistently low biases in a specific language and 2) models favor groups that are dominant within each language's culture. together, these results provide evidence for the need to ground mitigation methods to specific languages/cultures instead of the findings from a single language. further results show that multilingual finetuning data has more effect on bias amplification and changes in sentiment probabilities in comparison to multilingual pretraining data. we hope our findings encourage further diversity and expansion to additional languages in future bias studies. additional future work can analyze culture-specific attributes (e.g. caste) and mitigate language-specific biases based on these results.