A Rose by Any Other Name would not Smell as Sweet: Social Bias in Names Mistranslation (2023.emnlp-main)
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| Challenge: | Using demographics, we hypothesize that the ability of translation systems to correctly translate female-associated names is significantly lower than male-associated name. |
| Approach: | They propose a translation evaluation procedure based on round-trip translation of names that are demographically aligned and analyze the effect of name demographics on translation quality using generalized linear mixed effects models. |
| Outcome: | The proposed evaluation procedure is based on round-trip translation of names from a dataset of names that are demographically aligned and shows that the ability of translation systems to translate female-associated names is significantly lower than male-associated name. |
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Measuring and Mitigating Name Biases in Neural Machine Translation (2022.acl-long)
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| Challenge: | Neural machine translation systems exhibit problematic biases, such as stereotypical gender bias in occupation terms. |
| Approach: | They propose a method to reduce biases in person name translations by randomly switching entities during translation. |
| Outcome: | The proposed method eliminates the problem without any effect on translation quality. |
On the Influence of Gender and Race in Romantic Relationship Prediction from Large Language Models (2024.emnlp-main)
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| Challenge: | We show that models are less likely to predict romantic relationships for same-gender character pairs than different-grace character pairs. |
| Approach: | They perform name-replacement experiments to examine gender biases in large language models . they hypothesize that models mirror heteronormative biase and prejudice against interracial romantic relationships . |
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Low Frequency Names Exhibit Bias and Overfitting in Contextualizing Language Models (2021.emnlp-main)
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| Challenge: | Infrequent names are less similar to initial representations, and are more self-similar, suggesting that models rely on less context-informed representations of uncommon and minority names. |
| Approach: | They use a dataset of U.S. first names with labels based on predominant gender and racial group to examine effect of training corpus frequency on tokenization, contextualization, similarity to initial representation, and bias. |
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Global Voices, Local Biases: Socio-Cultural Prejudices across Languages (2023.emnlp-main)
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| Challenge: | Existing studies on human biases are heavily skewed towards Western and European languages . despite growing interest in language models, there are several shortcomings in the literature . |
| Approach: | They scale the Word Embedding Association Test to 24 languages and add culturally relevant information for each language. |
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Through the Looking Glass of Multilingual AI: Contrasting Language- and Name Script-Dependent Ethnic Hierarchies in GPT and DeepSeek (2026.acl-srw)
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| Challenge: | a recent study found that large language models are biased overwhelmingly Anglocentric . a stereotype perceptual map is a framework for analyzing how ethnic groups are positioned along evaluative dimensions. |
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Do Large Language Models Discriminate in Hiring Decisions on the Basis of Race, Ethnicity, and Gender? (2024.acl-short)
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| Challenge: | We study whether large language models exhibit race- and gender-based name discrimination in hiring decisions . |
| Approach: | They propose templatic prompts to LLMs to write an email to a named job applicant informing them of a hiring decision. |
| Outcome: | The proposed model generates an acceptance or rejection email based on the applicant's first name . |
Run Like a Girl! Sport-Related Gender Bias in Language and Vision (2023.findings-acl)
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| Challenge: | Existing social biases and stereotypes against certain groups are reproduced by computational models. |
| Approach: | They analyze gender bias in two Language and Vision datasets to find that they underrepresent women . they hypothesize that a bias affects human naming choices for people playing sports . |
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Cross-lingual Transfer Can Worsen Bias in Sentiment Analysis (2023.emnlp-main)
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| Challenge: | Existing evidence of demographic bias in SA systems is limited to a handful of languages, and it is costly to create supervised training data in a new language. |
| Approach: | They use counterfactual evaluation to test whether gender or racial biases are imported when using cross-lingual transfer . r&r is much more prevalent than gender biase . |
| Outcome: | The proposed model is compared with monolingual systems in five languages and shows that it is biased more than monolingual ones. |
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. |
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Unequal Representations: Analyzing Intersectional Biases in Word Embeddings Using Representational Similarity Analysis (2020.coling-main)
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| Challenge: | Specifically, we probe contextualized and non-contextualized word embeddings for evidence of intersectional biases against Black women. |
| Approach: | They propose a representational similarity analysis approach to detect human-like biases in word embeddings using representational similarities analysis. |
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