mlsanityData to 7 Oct 2026

A Survey on Bias and Fairness in Natural Language Processing

arXiv 2204.09591 ↗First posted 6 Mar 2022cs.CL cs.AIcited by 9

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Abstract (arXiv)

As NLP models become more integrated with the everyday lives of people, it becomes important to examine the social effect that the usage of these systems has. While these models understand language and have increased accuracy on difficult downstream tasks, there is evidence that these models amplify gender, racial and cultural stereotypes and lead to a vicious cycle in many settings. In this survey, we analyze the origins of biases, the definitions of fairness, and how different subfields of NLP mitigate bias. We finally discuss how future studies can work towards eradicating pernicious biases from NLP algorithms.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 17 of its 22 references that are in the map. First posted on arXiv 6 Mar 2022.

Builds on

17 of 22 references.

References · 17

  1. 2016Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddingscited by 1,304
  2. 2016Semantics derived automatically from language corpora contain human-like biasescited by 894
  3. 2018Mitigating Unwanted Biases with Adversarial Learningcited by 663
  4. 2018Gender Bias in Coreference Resolution: Evaluation and Debiasing Methodscited by 657
  5. 2018Gender Bias in Coreference Resolutioncited by 438
  6. 2017Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraintscited by 420
  7. 2019On Measuring Social Biases in Sentence Encoderscited by 397
  8. 2019Mitigating Gender Bias in Natural Language Processing: Literature Reviewcited by 287
  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 251
  11. 2019Identifying and Reducing Gender Bias in Word-Level Language Modelscited by 214
  12. 2020Towards Debiasing Sentence Representationscited by 170
5 more
  1. 2018Learning Gender-Neutral Word Embeddingscited by 169
  2. 2019Predictive Biases in Natural Language Processing Models: A Conceptual Framework and Overviewcited by 137
  3. 2019Getting Gender Right in Neural Machine Translationcited by 98
  4. 2018Understanding the Origins of Bias in Word Embeddingscited by 90
  5. 2019Equalizing Gender Biases in Neural Machine Translation with Word Embeddings Techniquescited by 65

Built on by

Cited by year

Citing papers per year: 2022: 2; 2023: 2; 2024: 1; 2025: 4.

9 papers.

  1. 2023A Survey on Fairness in Large Language Modelscited by 91
  2. 2024Fairness in Large Language Models: A Taxonomic Surveycited by 57
  3. 2023T2IAT: Measuring Valence and Stereotypical Biases in Text-to-Image Generationcited by 39
  4. 2022Unified Detoxifying and Debiasing in Language Generation via Inference-time Adaptive Optimizationcited by 32
  5. 2022Debiasing Methods for Fairer Neural Models in Vision and Language Research: A Surveycited by 22
  6. 2025Deconstructing Bias: A Multifaceted Framework for Diagnosing Cultural and Compositional Inequities in Text-to-Image Generative Modelscited by 4
  7. 2025Simulating a Bias Mitigation Scenario in Large Language Modelscited by 4
  8. 2025Enhancements for Developing a Comprehensive AI Fairness Assessment Standardcited by 3
  9. 2025Fairness Metric Design Exploration in Multi-Domain Moral Sentiment Classification using Transformer-Based Modelscited by 2