mlsanityData to 7 Oct 2026

REVISE: A Tool for Measuring and Mitigating Bias in Visual Datasets

arXiv 2004.07999 ↗First posted 16 Apr 2020cs.CVcited by 136

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Abstract (arXiv)

Machine learning models are known to perpetuate and even amplify the biases present in the data. However, these data biases frequently do not become apparent until after the models are deployed. Our work tackles this issue and enables the preemptive analysis of large-scale datasets. REVISE (REvealing VIsual biaSEs) is a tool that assists in the investigation of a visual dataset, surfacing potential biases along three dimensions: (1) object-based, (2) person-based, and (3) geography-based. Object-based biases relate to the size, context, or diversity of the depicted objects. Person-based metrics focus on analyzing the portrayal of people within the dataset. Geography-based analyses consider the representation of different geographic locations. These three dimensions are deeply intertwined in how they interact to bias a dataset, and REVISE sheds light on this; the responsibility then lies with the user to consider the cultural and historical context, and to determine which of the revealed biases may be problematic. The tool further assists the user by suggesting actionable steps that may be taken to mitigate the revealed biases. Overall, the key aim of our work is to tackle the machine learning bias problem early in the pipeline. REVISE is available at https://github.com/princetonvisualai/revise-tool

Key facts. Cited by 136 papers in the map (data through 7 Oct 2026). First posted on arXiv 16 Apr 2020.

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Citing papers per year: 2021: 18; 2022: 23; 2023: 39; 2024: 32; 2025: 19; 2026: 5.

136 papers.

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  1. 2022Safe Latent Diffusion: Mitigating Inappropriate Degeneration in Diffusion Modelscited by 501
  2. 2023Poisoning Web-Scale Training Datasets is Practicalcited by 276
  3. 2023Sociotechnical Safety Evaluation of Generative AI Systemscited by 154
  4. 2022Can Machines Help Us Answering Question 16 in Datasheets, and In Turn Reflecting on Inappropriate Content?cited by 150
  5. 2021Understanding and Evaluating Racial Biases in Image Captioningcited by 108
  6. 2021EnD: Entangling and Disentangling deep representations for bias correctioncited by 105
  7. 2022A Prompt Array Keeps the Bias Away: Debiasing Vision-Language Models with Adversarial Learningcited by 104
  8. 2021A Survey on Bias in Visual Datasetscited by 84
  9. 2023DeAR: Debiasing Vision-Language Models with Additive Residualscited by 70
  10. 2024The Neglected Tails in Vision-Language Modelscited by 63
  11. 2023FACET: Fairness in Computer Vision Evaluation Benchmarkcited by 58
  12. 2024Visual Haystacks: A Vision-Centric Needle-In-A-Haystack Benchmarkcited by 53
38 more
  1. 2021PASS: An ImageNet replacement for self-supervised pretraining without humanscited by 51
  2. 2023Uncurated Image-Text Datasets: Shedding Light on Demographic Biascited by 51
  3. 2023TIBET: Identifying and Evaluating Biases in Text-to-Image Generative Modelscited by 51
  4. 2023Describing Differences in Image Sets with Natural Languagecited by 51
  5. 2023GeoDE: a Geographically Diverse Evaluation Dataset for Object Recognitioncited by 50
  6. 2021Mitigating Dataset Harms Requires Stewardship: Lessons from 1000 Paperscited by 49
  7. 2024A Decade's Battle on Dataset Bias: Are We There Yet?cited by 46
  8. 2021GitTables: A Large-Scale Corpus of Relational Tablescited by 44
  9. 2021Unsupervised Learning of Debiased Representations with Pseudo-Attributescited by 44
  10. 2023Inspecting the Geographical Representativeness of Images from Text-to-Image Modelscited by 44
  11. 2024LLM as Dataset Analyst: Subpopulation Structure Discovery with Large Language Modelcited by 43
  12. 2023T2IAT: Measuring Valence and Stereotypical Biases in Text-to-Image Generationcited by 39
  13. 2021Finding and Fixing Spurious Patterns with Explanationscited by 38
  14. 2022Gender Artifacts in Visual Datasetscited by 38
  15. 2022Representation Bias in Data: A Survey on Identification and Resolution Techniquescited by 37
  16. 2022Quantifying Societal Bias Amplification in Image Captioningcited by 37
  17. 2022Measuring Representational Harms in Image Captioningcited by 36
  18. 2023Overwriting Pretrained Bias with Finetuning Datacited by 32
  19. 2025Generate, but Verify: Reducing Hallucination in Vision-Language Models with Retrospective Resamplingcited by 32
  20. 2024Interactive Medical Image Segmentation: A Benchmark Dataset and Baselinecited by 30
  21. 2023ToViLaG: Your Visual-Language Generative Model is Also An Evildoercited by 27
  22. 2024Position: Measure Dataset Diversity, Don't Just Claim Itcited by 26
  23. 2022Incidents1M: a large-scale dataset of images with natural disasters, damage, and incidentscited by 25
  24. 2021UDIS: Unsupervised Discovery of Bias in Deep Visual Recognition Modelscited by 23
  25. 2023Robust Visual Question Answering: Datasets, Methods, and Future Challengescited by 23
  26. 2023Consensus and Subjectivity of Skin Tone Annotation for ML Fairnesscited by 22
  27. 2022Men Also Do Laundry: Multi-Attribute Bias Amplificationcited by 21
  28. 2023Multimodal Composite Association Score: Measuring Gender Bias in Generative Multimodal Modelscited by 21
  29. 2021A Near-Optimal Algorithm for Debiasing Trained Machine Learning Modelscited by 19
  30. 2022Symphony: Composing Interactive Interfaces for Machine Learningcited by 18
  31. 2023Ethical Considerations for Responsible Data Curationcited by 17
  32. 2023Model-Agnostic Gender Debiased Image Captioningcited by 17
  33. 2023Quantifying Bias in Text-to-Image Generative Modelscited by 16
  34. 2022Measuring Compositional Consistency for Video Question Answeringcited by 15
  35. 2024Public Domain 12M: A Highly Aesthetic Image-Text Dataset with Novel Governance Mechanismscited by 15
  36. 2024Understanding Bias in Large-Scale Visual Datasetscited by 15
  37. 2023Metrics for Dataset Demographic Bias: A Case Study on Facial Expression Recognitioncited by 12
  38. 2024Fairness and Bias Mitigation in Computer Vision: A Surveycited by 12

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