mlsanityData to 7 Oct 2026

Fairness-aware Class Imbalanced Learning

arXiv 2109.10444 ↗First posted 21 Sep 2021cs.CLcited by 19

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Abstract (arXiv)

Class imbalance is a common challenge in many NLP tasks, and has clear connections to bias, in that bias in training data often leads to higher accuracy for majority groups at the expense of minority groups. However there has traditionally been a disconnect between research on class-imbalanced learning and mitigating bias, and only recently have the two been looked at through a common lens. In this work we evaluate long-tail learning methods for tweet sentiment and occupation classification, and extend a margin-loss based approach with methods to enforce fairness. We empirically show through controlled experiments that the proposed approaches help mitigate both class imbalance and demographic biases.

Key facts. Cited by 19 papers in the map (data through 7 Oct 2026). Builds on 25 of its 39 references that are in the map. First posted on arXiv 21 Sep 2021.

Builds on

25 of 39 references.

References · 25

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2014Unsupervised Domain Adaptation by Backpropagationcited by 1,906
  3. 2016Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddingscited by 1,304
  4. 2019Class-Balanced Loss Based on Effective Number of Samplescited by 1,239
  5. 2019Learning Imbalanced Datasets with Label-Distribution-Aware Margin Losscited by 987
  6. 2016Semantics derived automatically from language corpora contain human-like biasescited by 894
  7. 2019EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Taskscited by 800
  8. 2020CrowS-Pairs: A Challenge Dataset for Measuring Social Biases in Masked Language Modelscited by 703
  9. 2014Deep Domain Confusion: Maximizing for Domain Invariancecited by 597
  10. 2017A systematic study of the class imbalance problem in convolutional neural networkscited by 510
  11. 2020Null It Out: Guarding Protected Attributes by Iterative Nullspace Projectioncited by 442
  12. 2019On Measuring Social Biases in Sentence Encoderscited by 397
13 more
  1. 2019Nuanced Metrics for Measuring Unintended Bias with Real Data for Text Classificationcited by 396
  2. 2019Bias in Bios: A Case Study of Semantic Representation Bias in a High-Stakes Settingcited by 346
  3. 2015The Variational Fair Autoencodercited by 223
  4. 2016Demographic Dialectal Variation in Social Media: A Case Study of African-American Englishcited by 194
  5. 2019Dice Loss for Data-imbalanced NLP Taskscited by 180
  6. 2018Adversarial Removal of Demographic Attributes from Text Datacited by 166
  7. 2020Contextualizing Hate Speech Classifiers with Post-hoc Explanationcited by 84
  8. 2018Towards Robust and Privacy-preserving Text Representationscited by 75
  9. 2019Perturbation Sensitivity Analysis to Detect Unintended Model Biasescited by 74
  10. 2021Diverse Adversaries for Mitigating Bias in Trainingcited by 48
  11. 2020Fairness Metrics: A Comparative Analysiscited by 45
  12. 2020Stereotypical Bias Removal for Hate Speech Detection Task using Knowledge-based Generalizationscited by 40
  13. 2020A little goes a long way: Improving toxic language classification despite data scarcitycited by 12

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Citing papers per year: 2022: 7; 2023: 3; 2024: 8; 2025: 1.

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  1. 2022On Learning Fairness and Accuracy on Multiple Subgroupscited by 31
  2. 2022Improved Multi-label Classification under Temporal Concept Drift: Rethinking Group-Robust Algorithms in a Label-Wise Settingcited by 23
  3. 2022Optimising Equal Opportunity Fairness in Model Trainingcited by 22
  4. 2022A Survey of Methods for Addressing Class Imbalance in Deep-Learning Based Natural Language Processingcited by 20
  5. 2024Infusing Hierarchical Guidance into Prompt Tuning: A Parameter-Efficient Framework for Multi-level Implicit Discourse Relation Recognitioncited by 11
  6. 2022Towards A Holistic View of Bias in Machine Learning: Bridging Algorithmic Fairness and Imbalanced Learningcited by 10
  7. 2023iLab at SemEval-2023 Task 11 Le-Wi-Di: Modelling Disagreement or Modelling Perspectives?cited by 10
  8. 2025CoPL: Collaborative Preference Learning for Personalizing LLMscited by 8
  9. 2022Fairness Increases Adversarial Vulnerabilitycited by 5
  10. 2024On the Inductive Biases of Demographic Parity-based Fair Learning Algorithmscited by 5
  11. 2022Systematic Evaluation of Predictive Fairnesscited by 3
  12. 2024FairDD: Fair Dataset Distillationcited by 3
7 more
  1. 2024Ethics Whitepaper: Whitepaper on Ethical Research into Large Language Modelscited by 1
  2. 2024LTCXNet: Advancing Chest X-Ray Analysis with Solutions for Long-Tailed Multi-Label Classification and Fairness Challengescited by 1
  3. 2023Blacks is to Anger as Whites is to Joy? Understanding Latent Affective Bias in Large Pre-trained Neural Language Modelscited by 0
  4. 2023Beyond Fairness: Age-Harmless Parkinson's Detection via Voicecited by 0
  5. 2024fairBERTs: Erasing Sensitive Information Through Semantic and Fairness-aware Perturbationscited by 0
  6. 2024Balancing the Scales: Reinforcement Learning for Fair Classificationcited by 0
  7. 2024textitGeoHard: Towards Measuring Class-wise Hardness through Modelling Class Semanticscited by 0