# A Step Toward More Inclusive People Annotations for Fairness

arXiv: https://arxiv.org/abs/2105.02317 · HTML: https://mlsanity.com/paper/2105.02317/

## Key facts

- Cited by 46 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 33 references that are in the map.
- First posted on arXiv 5 May 2021.

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

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Deep Learning Face Attributes in the Wild](https://mlsanity.com/paper/1411.7766/) (2014)
- [Equality of Opportunity in Supervised Learning](https://mlsanity.com/paper/1610.02413/) (2016)
- [Model Cards for Model Reporting](https://mlsanity.com/paper/1810.03993/) (2018)
- [Age Progression/Regression by Conditional Adversarial Autoencoder](https://mlsanity.com/paper/1702.08423/) (2017)
- [Learning Adversarially Fair and Transferable Representations](https://mlsanity.com/paper/1802.06309/) (2018)
- [Towards Fairer Datasets: Filtering and Balancing the Distribution of the People Subtree in the ImageNet Hierarchy](https://mlsanity.com/paper/1912.07726/) (2019)
- [No Classification without Representation: Assessing Geodiversity Issues in Open Data Sets for the Developing World](https://mlsanity.com/paper/1711.08536/) (2017)
- [Does Object Recognition Work for Everyone?](https://mlsanity.com/paper/1906.02659/) (2019)
- [Turning a Blind Eye: Explicit Removal of Biases and Variation from Deep Neural Network Embeddings](https://mlsanity.com/paper/1809.02169/) (2018)
- [Diversity in Faces](https://mlsanity.com/paper/1901.10436/) (2019)
- [Predictive Inequity in Object Detection](https://mlsanity.com/paper/1902.11097/) (2019)
- [Putting Fairness Principles into Practice: Challenges, Metrics, and Improvements](https://mlsanity.com/paper/1901.04562/) (2019)
- [Auditing ImageNet: Towards a Model-driven Framework for Annotating Demographic Attributes of Large-Scale Image Datasets](https://mlsanity.com/paper/1905.01347/) (2019)
- [Soft Sampling for Robust Object Detection](https://mlsanity.com/paper/1806.06986/) (2018)

## Built on by (most cited first, 30 of 46)

- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [PaLI: A Jointly-Scaled Multilingual Language-Image Model](https://mlsanity.com/paper/2209.06794/) (2022)
- [VideoPoet: A Large Language Model for Zero-Shot Video Generation](https://mlsanity.com/paper/2312.14125/) (2023)
- [PaLI-X: On Scaling up a Multilingual Vision and Language Model](https://mlsanity.com/paper/2305.18565/) (2023)
- [PaLI-3 Vision Language Models: Smaller, Faster, Stronger](https://mlsanity.com/paper/2310.09199/) (2023)
- [FACET: Fairness in Computer Vision Evaluation Benchmark](https://mlsanity.com/paper/2309.00035/) (2023)
- [Adversarial attacks and defenses in explainable artificial intelligence: A survey](https://mlsanity.com/paper/2306.06123/) (2023)
- [Gender Artifacts in Visual Datasets](https://mlsanity.com/paper/2206.09191/) (2022)
- [CLIP the Bias: How Useful is Balancing Data in Multimodal Learning?](https://mlsanity.com/paper/2403.04547/) (2024)
- [Overwriting Pretrained Bias with Finetuning Data](https://mlsanity.com/paper/2303.06167/) (2023)
- [Survey of Social Bias in Vision-Language Models](https://mlsanity.com/paper/2309.14381/) (2023)
- [Fairness Indicators for Systematic Assessments of Visual Feature Extractors](https://mlsanity.com/paper/2202.07603/) (2022)
- [Auditing Gender Presentation Differences in Text-to-Image Models](https://mlsanity.com/paper/2302.03675/) (2023)
- [Consensus and Subjectivity of Skin Tone Annotation for ML Fairness](https://mlsanity.com/paper/2305.09073/) (2023)
- [Holistic Safety and Responsibility Evaluations of Advanced AI Models](https://mlsanity.com/paper/2404.14068/) (2024)
- [Ethical Considerations for Responsible Data Curation](https://mlsanity.com/paper/2302.03629/) (2023)
- [Quantifying Bias in Text-to-Image Generative Models](https://mlsanity.com/paper/2312.13053/) (2023)
- [Revealing and Reducing Gender Biases in Vision and Language Assistants (VLAs)](https://mlsanity.com/paper/2410.19314/) (2024)
- [Evaluating the Fairness of Discriminative Foundation Models in Computer Vision](https://mlsanity.com/paper/2310.11867/) (2023)
- [Robustness Disparities in Commercial Face Detection](https://mlsanity.com/paper/2108.12508/) (2021)
- [Ground Truth Or Dare: Factors Affecting The Creation Of Medical Datasets For Training AI](https://mlsanity.com/paper/2309.12327/) (2023)
- [DIG In: Evaluating Disparities in Image Generations with Indicators for Geographic Diversity](https://mlsanity.com/paper/2308.06198/) (2023)
- [Resampled Datasets Are Not Enough: Mitigating Societal Bias Beyond Single Attributes](https://mlsanity.com/paper/2407.03623/) (2024)
- [Measuring and signing fairness as performance under multiple stakeholder distributions](https://mlsanity.com/paper/2207.09960/) (2022)
- [A View From Somewhere: Human-Centric Face Representations](https://mlsanity.com/paper/2303.17176/) (2023)
- [Generalized People Diversity: Learning a Human Perception-Aligned Diversity Representation for People Images](https://mlsanity.com/paper/2401.14322/) (2024)
- [Copycats: the many lives of a publicly available medical imaging dataset](https://mlsanity.com/paper/2402.06353/) (2024)
- [Interpretable Debiasing of Vision-Language Models for Social Fairness](https://mlsanity.com/paper/2602.24014/) (2026)
- [Robustness Disparities in Face Detection](https://mlsanity.com/paper/2211.15937/) (2022)
- [Towards Reliable Assessments of Demographic Disparities in Multi-Label Image Classifiers](https://mlsanity.com/paper/2302.08572/) (2023)

## Abstract (arXiv)

The Open Images Dataset contains approximately 9 million images and is a widely accepted dataset for computer vision research. As is common practice for large datasets, the annotations are not exhaustive, with bounding boxes and attribute labels for only a subset of the classes in each image. In this paper, we present a new set of annotations on a subset of the Open Images dataset called the MIAP (More Inclusive Annotations for People) subset, containing bounding boxes and attributes for all of the people visible in those images. The attributes and labeling methodology for the MIAP subset were designed to enable research into model fairness. In addition, we analyze the original annotation methodology for the person class and its subclasses, discussing the resulting patterns in order to inform future annotation efforts. By considering both the original and exhaustive annotation sets, researchers can also now study how systematic patterns in training annotations affect modeling.