# The Casual Conversations v2 Dataset

arXiv: https://arxiv.org/abs/2303.04838 · HTML: https://mlsanity.com/paper/2303.04838/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 36 references that are in the map.
- First posted on arXiv 8 Mar 2023.

## Builds on (most cited first, 16 of 16)

- [MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition](https://mlsanity.com/paper/1607.08221/) (2016)
- [The Open Images Dataset V4: Unified image classification, object detection, and visual relationship detection at scale](https://mlsanity.com/paper/1811.00982/) (2018)
- [Age Progression/Regression by Conditional Adversarial Autoencoder](https://mlsanity.com/paper/1702.08423/) (2017)
- [Does Object Recognition Work for Everyone?](https://mlsanity.com/paper/1906.02659/) (2019)
- [Demographic Bias in Biometrics: A Survey on an Emerging Challenge](https://mlsanity.com/paper/2003.02488/) (2020)
- [Towards Measuring Fairness in AI: the Casual Conversations Dataset](https://mlsanity.com/paper/2104.02821/) (2021)
- [Analysis of Gender Inequality In Face Recognition Accuracy](https://mlsanity.com/paper/2002.00065/) (2020)
- [A Step Toward More Inclusive People Annotations for Fairness](https://mlsanity.com/paper/2105.02317/) (2021)
- [Gendered Differences in Face Recognition Accuracy Explained by Hairstyles, Makeup, and Facial Morphology](https://mlsanity.com/paper/2112.14656/) (2021)
- [FairGRAPE: Fairness-aware GRAdient Pruning mEthod for Face Attribute Classification](https://mlsanity.com/paper/2207.10888/) (2022)
- [Face Recognition Accuracy Across Demographics: Shining a Light Into the Problem](https://mlsanity.com/paper/2206.01881/) (2022)
- [Understanding Fairness of Gender Classification Algorithms Across Gender-Race Groups](https://mlsanity.com/paper/2009.11491/) (2020)
- [Fairness Indicators for Systematic Assessments of Visual Feature Extractors](https://mlsanity.com/paper/2202.07603/) (2022)
- [Does Face Recognition Accuracy Get Better With Age? Deep Face Matchers Say No](https://mlsanity.com/paper/1911.06396/) (2019)
- [Casual Conversations v2: Designing a large consent-driven dataset to measure algorithmic bias and robustness](https://mlsanity.com/paper/2211.05809/) (2022)
- [Does Face Recognition Error Echo Gender Classification Error?](https://mlsanity.com/paper/2104.13803/) (2021)

## Built on by (most cited first, 4 of 4)

- [Fairness and Bias in Multimodal AI: A Survey](https://mlsanity.com/paper/2406.19097/) (2024)
- [Metrics for Dataset Demographic Bias: A Case Study on Facial Expression Recognition](https://mlsanity.com/paper/2303.15889/) (2023)
- [CASPER: A Large Scale Spontaneous Speech Dataset](https://mlsanity.com/paper/2506.00267/) (2025)
- [When Entanglement Lower-Bounds Disparity: Auditing and Repairing Demographic Fairness in Audio Understanding Models](https://mlsanity.com/paper/2609.27382/) (2026)

## 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.