mlsanityData to 14 Oct 2026

Representation Bias in Data: A Survey on Identification and Resolution Techniques

arXiv 2203.11852 ↗First posted 22 Mar 2022cs.DB cs.LGcited by 37

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Abstract (arXiv)

Data-driven algorithms are only as good as the data they work with, while data sets, especially social data, often fail to represent minorities adequately. Representation Bias in data can happen due to various reasons ranging from historical discrimination to selection and sampling biases in the data acquisition and preparation methods. Given that "bias in, bias out", one cannot expect AI-based solutions to have equitable outcomes for societal applications, without addressing issues such as representation bias. While there has been extensive study of fairness in machine learning models, including several review papers, bias in the data has been less studied. This paper reviews the literature on identifying and resolving representation bias as a feature of a data set, independent of how consumed later. The scope of this survey is bounded to structured (tabular) and unstructured (e.g., image, text, graph) data. It presents taxonomies to categorize the studied techniques based on multiple design dimensions and provides a side-by-side comparison of their properties. There is still a long way to fully address representation bias issues in data. The authors hope that this survey motivates researchers to approach these challenges in the future by observing existing work within their respective domains.

Key facts. Cited by 37 papers in the map (data through 14 Oct 2026). Builds on 74 of its 183 references that are in the map. First posted on arXiv 22 Mar 2022.

Builds on

74 of 183 references.

References · 74

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  2. 2016Equality of Opportunity in Supervised Learningcited by 2,007
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  4. 2019A Survey on Bias and Fairness in Machine Learningcited by 1,917
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  11. 2018Gender Bias in Coreference Resolution: Evaluation and Debiasing Methodscited by 659
  12. 2018A Reductions Approach to Fair Classificationcited by 587
38 more
  1. 2016Inherent Trade-Offs in the Fair Determination of Risk Scorescited by 586
  2. 2017On Fairness and Calibrationcited by 406
  3. 2017Algorithmic decision making and the cost of fairnesscited by 403
  4. 2020Fairness in Machine Learning: A Surveycited by 370
  5. 2019Bias in Bios: A Case Study of Semantic Representation Bias in a High-Stakes Settingcited by 349
  6. 2019Mitigating Gender Bias in Natural Language Processing: Literature Reviewcited by 287
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  13. 2019REPAIR: Removing Representation Bias by Dataset Resamplingcited by 157
  14. 2018Reducing Gender Bias in Abusive Language Detectioncited by 152
  15. 2019Black is to Criminal as Caucasian is to Police: Detecting and Removing Multiclass Bias in Word Embeddingscited by 147
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  18. 2020REVISE: A Tool for Measuring and Mitigating Bias in Visual Datasetscited by 136
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  20. 2019Compositional Fairness Constraints for Graph Embeddingscited by 126
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Citing papers per year: 2022: 1; 2023: 5; 2024: 17; 2025: 8; 2026: 6.

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  1. 2024Black-Box Access is Insufficient for Rigorous AI Auditscited by 126
  2. 2023Sociodemographic Bias in Language Models: A Survey and Forward Pathcited by 28
  3. 2024Data Readiness for AI: A 360-Degree Surveycited by 15
  4. 2024FairAIED: Navigating Fairness, Bias, and Ethics in Educational AI Applicationscited by 15
  5. 2024Visual-TCAV: Concept-based Attribution and Saliency Maps for Post-hoc Explainability in Image Classificationcited by 12
  6. 2023Through the Fairness Lens: Experimental Analysis and Evaluation of Entity Matchingcited by 9
  7. 2023Pitfalls in Language Models for Code Intelligence: A Taxonomy and Surveycited by 9
  8. 2022Consistent Range Approximation for Fair Predictive Modelingcited by 7
  9. 2024Chameleon: Foundation Models for Fairness-aware Multi-modal Data Augmentation to Enhance Coverage of Minoritiescited by 5
  10. 2024Explanatory Debiasing: Involving Domain Experts in the Data Generation Process to Mitigate Representation Bias in AI Systemscited by 5
  11. 2023A Survey on Image-text Multimodal Modelscited by 4
  12. 2023Survey on AI Ethics: A Socio-technical Perspectivecited by 4
25 more
  1. 2025BIASINSPECTOR: Detecting Bias in Structured Data through LLM Agentscited by 4
  2. 2024Ethical Concern Identification in NLP: A Corpus of ACL Anthology Ethics Statementscited by 3
  3. 2024Best Practices for Responsible Machine Learning in Credit Scoringcited by 2
  4. 2025ColFigPhotoAttnNet: Reliable Finger Photo Presentation Attack Detection Leveraging Window-Attention on Color Spacescited by 2
  5. 2026A Unified Framework to Quantify Cultural Intelligence of AIcited by 2
  6. 2024FairEM360: A Suite for Responsible Entity Matchingcited by 1
  7. 2024Overcoming Representation Bias in Fairness-Aware data Repair using Optimal Transportcited by 1
  8. 2024Mining the Minoria: Unknown, Under-represented, and Under-performing Minority Groupscited by 1
  9. 2025Prompting Away Stereotypes? Evaluating Bias in Text-to-Image Models for Occupationscited by 1
  10. 2025Mitigation of Gender and Ethnicity Bias in AI-Generated Stories through Model Explanationscited by 1
  11. 2025Unbiased Binning for Fairness-aware Attribute Representationcited by 1
  12. 2024Justice in Healthcare Artificial Intelligence in Africacited by 0
  13. 2024Dataset Representativeness and Downstream Task Fairnesscited by 0
  14. 2024Leveraging Ontologies to Document Bias in Datacited by 0
  15. 2024Adaptive Recruitment Resource Allocation to Improve Cohort Representativeness in Participatory Biomedical Datasetscited by 0
  16. 2024Responsible AI in NLP: GUS-Net Span-Level Bias Detection Dataset and Benchmark for Generalizations, Unfairness, and Stereotypescited by 0
  17. 2024Data Acquisition for Improving Model Fairness using Reinforcement Learningcited by 0
  18. 2025TEDI: Trustworthy and Ethical Dataset Indicators to Analyze and Compare Dataset Documentationcited by 0
  19. 2025On the Reproducibility of "FairCLIP: Harnessing Fairness in Vision-Language Learning''cited by 0
  20. 2025Temporal Image Forensics: A Review and Critical Evaluationcited by 0
  21. 2026BRIDGE the Gap: Mitigating Bias Amplification in Automated Scoring of English Language Learners via Inter-group Data Augmentationcited by 0
  22. 2026Data Bias Mitigation under Coverage Constraints & The Price of Fairnesscited by 0
  23. 2026Wait, am I Being Fair? Characterizing Deductive Stereotyping and Mitigating It with Fair-GCGcited by 0
  24. 2026A Framework for Identifying, Categorizing, and Explaining Bias in AI-Generated Codecited by 0
  25. 2026Beyond Model Ranking: Regime Diagnosis for Distributional-Statistical Misspecification in Industrial Time-Series Forecastingcited by 0