| Challenge: | Ethnic bias is one of the most prevalent social stereotypes. |
| Approach: | They propose to use a multilingual model and contextual word alignment to mitigate ethnic bias in monolingual BERT for English, German, Spanish, Korean, Turkish, and Chinese. |
| Outcome: | The proposed methods alleviate ethnic bias in English, German, Spanish, Korean, Turkish, and Chinese using a multilingual model and contextual word alignment of two monolingual models. |
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Social Bias in Multilingual Language Models: A Survey (2025.emnlp-main)
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| Challenge: | Pretrained multilingual models exhibit the same social bias as models processing English texts. |
| Approach: | They examine the literature on bias evaluation and mitigation approaches in multilingual and non-English contexts and identify gaps in the field. |
| Outcome: | The proposed models perform well on multilingual language understanding benchmarks and are consistent with the current literature. |
Comparing Biases and the Impact of Multilingual Training across Multiple Languages (2023.emnlp-main)
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Sharon Levy, Neha John, Ling Liu, Yogarshi Vyas, Jie Ma, Yoshinari Fujinuma, Miguel Ballesteros, Vittorio Castelli, Dan Roth
| Challenge: | Currently, studies on bias and fairness in natural language processing focus on a single language and/or across few attributes (e.g. gender, race). However, biases can manifest differently across languages for individual attributes. |
| Approach: | They adapt existing sentiment bias templates in English to Italian, Chinese, Hebrew, and Spanish for race, religion, nationality, and gender. |
| Outcome: | The proposed model favors groups that are dominant in each language's culture, indicating bias amplification, after multilingual finetuning. |
Investigating Bias in Multilingual Language Models: Cross-Lingual Transfer of Debiasing Techniques (2023.emnlp-main)
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| Challenge: | Debiasing techniques that target sentence representations are being investigated in multilingual models . a growing interest in addressing bias detection and mitigation in NLP due to their societal implications. |
| Approach: | They examine the transferability of debiasing techniques across different languages within multilingual models by using a dataset from CrowS-Pairs. |
| Outcome: | The proposed techniques reduce bias in English, French, German, and Dutch by 13% . the authors also show that the techniques with additional pretraining exhibit enhanced cross-lingual effectiveness for the languages included in the analyses . |
Multilingual BERT has an accent: Evaluating English influences on fluency in multilingual models (2023.findings-eacl)
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| Challenge: | Multilingual models can improve NLP performance on low-resource languages by leveraging higher-resourced languages, but they also reduce average performance on all languages. |
| Approach: | They propose a method to evaluate multilingual models by asking if models predict languages with an 'English accent' they propose to use grammatical structure bias to determine if multilingual model is biased toward English-like setting . |
| Outcome: | The proposed method compares the fluency of multilingual models to the fluencies of monolingual Spanish and Greek models. |
Bias and Fairness in Natural Language Processing (D19-2)
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| Challenge: | a tutorial will review the history of bias and fairness studies in machine learning and language processing . |
| Approach: | This tutorial reviews the history of bias and fairness studies in machine learning and language processing . it presents recent community effort to quantify and mitigat bias in natural language processing models . |
| Outcome: | This tutorial reviews the history of bias and fairness studies in machine learning and language processing . it aims to quantify and mitigate bias in natural language processing models for a wide spectrum of tasks . |
Demographic-Aware Language Model Fine-tuning as a Bias Mitigation Technique (2022.aacl-short)
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| Challenge: | In this paper, we analyze the variations in gender and racial biases in BERT-like language models when exposed to different demographic groups. |
| Approach: | They analyze gender and racial biases in BERT-like language models when exposed to different demographic groups. |
| Outcome: | The proposed model can mitigate biases in text authored by disadvantaged demographic groups compared to advantaged groups . the proposed model is agnostic to the language of the speakers behind the language . |
Your Stereotypical Mileage May Vary: Practical Challenges of Evaluating Biases in Multiple Languages and Cultural Contexts (2024.lrec-main)
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Karen Fort, Laura Alonso Alemany, Luciana Benotti, Julien Bezançon, Claudia Borg, Marthese Borg, Yongjian Chen, Fanny Ducel, Yoann Dupont, Guido Ivetta, Zhijian Li, Margot Mieskes, Marco Naguib, Yuyan Qian, Matteo Radaelli, Wolfgang S. Schmeisser-Nieto, Emma Raimundo Schulz, Thiziri Saci, Sarah Saidi, Javier Torroba Marchante, Shilin Xie, Sergio E. Zanotto, Aurélie Névéol
| Challenge: | Recent studies have identified a gap in the availability of tools and resources to study bias in languages other than English and social contexts outside the north of America. |
| Approach: | They use stereotypes to build a corpus of sentence pairs that cover biases in seven cultural contexts. |
| Outcome: | The proposed resource covers a wide range of languages and cultural settings . it favors sentences that express stereotypes in most bias categories . |
Mitigating Gender Bias in Natural Language Processing: Literature Review (P19-1)
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Tony Sun, Andrew Gaut, Shirlyn Tang, Yuxin Huang, Mai ElSherief, Jieyu Zhao, Diba Mirza, Elizabeth Belding, Kai-Wei Chang, William Yang Wang
| Challenge: | NLP models propagate and may even amplify gender bias found in text corpora . methods to mitigate gender bias in NLP are relatively nascent . |
| Approach: | They propose to analyze gender bias based on four forms of representation bias and discuss the advantages and drawbacks of existing gender debiasing methods. |
| Outcome: | The proposed methods are based on four forms of representation bias and have advantages and drawbacks. |
On Evaluating and Mitigating Gender Biases in Multilingual Settings (2023.findings-acl)
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| Challenge: | Existing benchmarks and resources for evaluating gender biases in multilingual settings are limited. |
| Approach: | They propose to extend DisCo to different Indian languages using human annotations to evaluate gender biases in multilingual models. |
| Outcome: | The proposed benchmarks and mitigation techniques are extended beyond English to evaluate gender biases in multilingual models. |
On Measures of Biases and Harms in NLP (2022.findings-aacl)
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Sunipa Dev, Emily Sheng, Jieyu Zhao, Aubrie Amstutz, Jiao Sun, Yu Hou, Mattie Sanseverino, Jiin Kim, Akihiro Nishi, Nanyun Peng, Kai-Wei Chang
| Challenge: | Recent studies show that natural language processing (NLP) technologies propagate societal biases about demographic groups associated with attributes such as gender, race, and nationality. |
| Approach: | They propose a framework for harms and questions to help practitioners understand biases . they propose measurable measures to detect and mitigate biased groups . |
| Outcome: | The proposed framework provides a framework for harms and questions for practitioners to answer to guide the development of bias measures. |