mlsanityData to 14 Oct 2026

Mapping the Multilingual Margins: Intersectional Biases of Sentiment Analysis Systems in English, Spanish, and Arabic

arXiv 2204.03558 ↗First posted 7 Apr 2022cs.CLcited by 13

Not on the map. Why?

Abstract (arXiv)

As natural language processing systems become more widespread, it is necessary to address fairness issues in their implementation and deployment to ensure that their negative impacts on society are understood and minimized. However, there is limited work that studies fairness using a multilingual and intersectional framework or on downstream tasks. In this paper, we introduce four multilingual Equity Evaluation Corpora, supplementary test sets designed to measure social biases, and a novel statistical framework for studying unisectional and intersectional social biases in natural language processing. We use these tools to measure gender, racial, ethnic, and intersectional social biases across five models trained on emotion regression tasks in English, Spanish, and Arabic. We find that many systems demonstrate statistically significant unisectional and intersectional social biases.

Key facts. Cited by 13 papers in the map (data through 14 Oct 2026). Builds on 19 of its 36 references that are in the map. First posted on arXiv 7 Apr 2022.

Builds on

19 of 36 references.

References · 19

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
  3. 2019Unsupervised Cross-lingual Representation Learning at Scalecited by 3,935
  4. 2016Enriching Word Vectors with Subword Informationcited by 1,583
  5. 2016Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddingscited by 1,312
  6. 2016Semantics derived automatically from language corpora contain human-like biasescited by 901
  7. 2019On Measuring Social Biases in Sentence Encoderscited by 398
  8. 2019Measuring Bias in Contextualized Word Representationscited by 276
  9. 2019Lipstick on a Pig: Debiasing Methods Cover up Systematic Gender Biases in Word Embeddings But do not Remove Themcited by 268
  10. 2017Word Embeddings Quantify 100 Years of Gender and Ethnic Stereotypescited by 253
  11. 2018Examining Gender and Race Bias in Two Hundred Sentiment Analysis Systemscited by 234
  12. 2019Gender Bias in Contextualized Word Embeddingscited by 203
7 more
  1. 2020Detecting Emergent Intersectional Biases: Contextualized Word Embeddings Contain a Distribution of Human-like Biasescited by 167
  2. 2019Black is to Criminal as Caucasian is to Police: Detecting and Removing Multiclass Bias in Word Embeddingscited by 147
  3. 2019Assessing Social and Intersectional Biases in Contextualized Word Representationscited by 135
  4. 2020KUISAIL at SemEval-2020 Task 12: BERT-CNN for Offensive Speech Identification in Social Mediacited by 84
  5. 2020Gender Bias in Multilingual Embeddings and Cross-Lingual Transfercited by 55
  6. 2019Examining Gender Bias in Languages with Grammatical Gendercited by 40
  7. 2021Responsible AI: Gender bias assessment in emotion recognitioncited by 22

Built on by

Cited by year

Citing papers per year: 2023: 7; 2024: 3; 2025: 3.

13 papers.

Sort:

  1. 2023A Survey on Intersectional Fairness in Machine Learning: Notions, Mitigation, and Challengescited by 41
  2. 2023Comparing Biases and the Impact of Multilingual Training across Multiple Languagescited by 36
  3. 2024LLMs are Biased Teachers: Evaluating LLM Bias in Personalized Educationcited by 28
  4. 2023Factoring the Matrix of Domination: A Critical Review and Reimagination of Intersectionality in AI Fairnesscited by 26
  5. 2023Fairness in Language Models Beyond English: Gaps and Challengescited by 16
  6. 2024Gender Bias in Decision-Making with Large Language Models: A Study of Relationship Conflictscited by 15
  7. 2023Multilingual large language models leak human stereotypes across language boundariescited by 12
  8. 2024Fairness Definitions in Language Models Explainedcited by 11
  9. 2025Colombian Waitresses y Jueces canadienses: Gender and Country Biases in Occupation Recommendations from LLMscited by 7
  10. 2025Social Bias in Multilingual Language Models: A Surveycited by 2
  11. 2023Politeness Stereotypes and Attack Vectors: Gender Stereotypes in Japanese and Korean Language Modelscited by 1
  12. 2025Bias Beyond English: Evaluating Social Bias and Debiasing Methods in a Low-Resource Settingcited by 1
1 more
  1. 2023Model and Evaluation: Towards Fairness in Multilingual Text Classificationcited by 0