mlsanityData to 7 Oct 2026

Stereotyping and Bias in the Flickr30K Dataset

arXiv 1605.06083 ↗First posted 19 May 2016cs.CL cs.CVcited by 25

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Abstract (arXiv)

An untested assumption behind the crowdsourced descriptions of the images in the Flickr30K dataset (Young et al., 2014) is that they "focus only on the information that can be obtained from the image alone" (Hodosh et al., 2013, p. 859). This paper presents some evidence against this assumption, and provides a list of biases and unwarranted inferences that can be found in the Flickr30K dataset. Finally, it considers methods to find examples of these, and discusses how we should deal with stereotype-driven descriptions in future applications.

Key facts. Cited by 25 papers in the map (data through 7 Oct 2026). First posted on arXiv 19 May 2016.

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Citing papers per year: 2021: 5; 2022: 4; 2023: 7; 2024: 6; 2025: 2; 2026: 1.

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  1. 2021Understanding and Evaluating Racial Biases in Image Captioningcited by 108
  2. 2021PASS: An ImageNet replacement for self-supervised pretraining without humanscited by 51
  3. 2023Uncurated Image-Text Datasets: Shedding Light on Demographic Biascited by 51
  4. 2024A Decade's Battle on Dataset Bias: Are We There Yet?cited by 46
  5. 2022Gender Artifacts in Visual Datasetscited by 38
  6. 2022Measuring Representational Harms in Image Captioningcited by 36
  7. 2021The World of an Octopus: How Reporting Bias Influences a Language Model's Perception of Colorcited by 32
  8. 2022Underspecification in Scene Description-to-Depiction Taskscited by 30
  9. 2021Dimensions of Commonsense Knowledgecited by 29
  10. 2023Balancing the Picture: Debiasing Vision-Language Datasets with Synthetic Contrast Setscited by 27
  11. 2023Survey of Social Bias in Vision-Language Modelscited by 26
  12. 2023A Friendly Face: Do Text-to-Image Systems Rely on Stereotypes when the Input is Under-Specified?cited by 25
13 more
  1. 2024Understanding Bias in Large-Scale Visual Datasetscited by 15
  2. 2021Computer Vision and Conflicting Values: Describing People with Automated Alt Textcited by 14
  3. 2024Fairness and Bias Mitigation in Computer Vision: A Surveycited by 12
  4. 2022Simplicity Bias Leads to Amplified Performance Disparitiescited by 11
  5. 2023Evaluating Bias and Fairness in Gender-Neutral Pretrained Vision-and-Language Modelscited by 6
  6. 2023Fairness in AI Systems: Mitigating gender bias from language-vision modelscited by 5
  7. 2024Quantifying the Gaps Between Translation and Native Perception in Training for Multimodal, Multilingual Retrievalcited by 5
  8. 2024Measuring Machine Learning Harms from Stereotypes Requires Understanding Who Is Harmed by Which Errors in What Wayscited by 4
  9. 2023Impressions: Understanding Visual Semiotics and Aesthetic Impactcited by 3
  10. 2025Attention IoU: Examining Biases in CelebA using Attention Mapscited by 3
  11. 2025Understanding Gender Bias in AI-Generated Product Descriptionscited by 2
  12. 2024Common-Sense Bias Modeling for Classification Taskscited by 1
  13. 2026Personalized Generative Models for Contextual Debiasingcited by 0