mlsanityData to 7 Oct 2026

The Casual Conversations v2 Dataset

arXiv 2303.04838 ↗First posted 8 Mar 2023cs.CV cs.AI cs.CLcited by 4

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Abstract (arXiv)

This paper introduces a new large consent-driven dataset aimed at assisting in the evaluation of algorithmic bias and robustness of computer vision and audio speech models in regards to 11 attributes that are self-provided or labeled by trained annotators. The dataset includes 26,467 videos of 5,567 unique paid participants, with an average of almost 5 videos per person, recorded in Brazil, India, Indonesia, Mexico, Vietnam, Philippines, and the USA, representing diverse demographic characteristics. The participants agreed for their data to be used in assessing fairness of AI models and provided self-reported age, gender, language/dialect, disability status, physical adornments, physical attributes and geo-location information, while trained annotators labeled apparent skin tone using the Fitzpatrick Skin Type and Monk Skin Tone scales, and voice timbre. Annotators also labeled for different recording setups and per-second activity annotations.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 16 of its 36 references that are in the map. First posted on arXiv 8 Mar 2023.

Builds on

16 of 36 references.

References · 16

  1. 2016MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognitioncited by 621
  2. 2018The Open Images Dataset V4: Unified image classification, object detection, and visual relationship detection at scalecited by 542
  3. 2017Age Progression/Regression by Conditional Adversarial Autoencodercited by 426
  4. 2019Does Object Recognition Work for Everyone?cited by 149
  5. 2020Demographic Bias in Biometrics: A Survey on an Emerging Challengecited by 93
  6. 2021Towards Measuring Fairness in AI: the Casual Conversations Datasetcited by 80
  7. 2020Analysis of Gender Inequality In Face Recognition Accuracycited by 47
  8. 2021A Step Toward More Inclusive People Annotations for Fairnesscited by 46
  9. 2021Gendered Differences in Face Recognition Accuracy Explained by Hairstyles, Makeup, and Facial Morphologycited by 32
  10. 2022FairGRAPE: Fairness-aware GRAdient Pruning mEthod for Face Attribute Classificationcited by 30
  11. 2020Understanding Fairness of Gender Classification Algorithms Across Gender-Race Groupscited by 24
  12. 2022Fairness Indicators for Systematic Assessments of Visual Feature Extractorscited by 24
4 more
  1. 2022Face Recognition Accuracy Across Demographics: Shining a Light Into the Problemcited by 24
  2. 2019Does Face Recognition Accuracy Get Better With Age? Deep Face Matchers Say Nocited by 16
  3. 2022Casual Conversations v2: Designing a large consent-driven dataset to measure algorithmic bias and robustnesscited by 5
  4. 2021Does Face Recognition Error Echo Gender Classification Error?cited by 4

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 1; 2025: 1; 2026: 1.

4 papers.

  1. 2024Fairness and Bias in Multimodal AI: A Surveycited by 19
  2. 2023Metrics for Dataset Demographic Bias: A Case Study on Facial Expression Recognitioncited by 12
  3. 2025CASPER: A Large Scale Spontaneous Speech Datasetcited by 3
  4. 2026When Entanglement Lower-Bounds Disparity: Auditing and Repairing Demographic Fairness in Audio Understanding Modelscited by 0