FairFace: Face Attribute Dataset for Balanced Race, Gender, and Age for Bias Measurement and Mitigation

Kärkkäinen, Kimmo; Joo, Jungseock

doi:10.1109/wacv48630.2021.00159

Cited by 336 publications

(199 citation statements)

References 44 publications

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“…A particular example is the recent Open AI CLIP [60] model which is a large scale model pre-trained on wide variety of images with language supervision. In its broader impact section, the authors present fairness evaluations of their model on harmful label associations and disparity in gender recognition using FairFace [39] dataset. However, these evaluations did not provide systematic protocols that can be followed for any pretrained model for assessing fairness such as geodiversity.…”

Section: Related Workmentioning

confidence: 99%

Fairness Indicators for Systematic Assessments of Visual Feature Extractors

Goyal¹,

Soriano²,

Hazırbaş³

et al. 2022

Preprint

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Section: Related Workmentioning

confidence: 99%

Fairness Indicators for Systematic Assessments of Visual Feature Extractors

Goyal¹,

Soriano²,

Hazırbaş³

et al. 2022

Preprint

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“…Similarly, datasets for training or evaluating face recognition algorithms may include annotations for fairness analysis [11,26]. In other cases, these annotations were initially curated as training data for attribute classifiers (e.g., Celeb-A [18], FairFace [14], UTKFace [32]) but may also be useful for studying bias [1,23].…”

Section: Identifying and Reducing Bias In Trained Modelsmentioning

confidence: 99%

A Step Toward More Inclusive People Annotations for Fairness

Schumann

Ricco

Prabhu

et al. 2021

Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society

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The Open Images Dataset [16] 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. CCS CONCEPTS• Computing methodologies → Computer vision; Machine learning; • Social and professional topics → User characteristics.

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“…We first study FairFace [13], a collection of 100,000 face images annotated with crowd-sourced labels about the perceived age, race, and gender of each image. FairFace is notable for being approximately balanced across 7 races and 2 genders.…”

Section: Fairfacementioning

confidence: 99%

The Spotlight: A General Method for Discovering Systematic Errors in Deep Learning Models

d'Eon¹,

d’Eon²,

Wright³

et al. 2021

Preprint

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Supervised learning models often make systematic errors on rare subsets of the data. However, such systematic errors can be difficult to identify, as model performance can only be broken down across sensitive groups when these groups are known and explicitly labelled. This paper introduces a method for discovering systematic errors, which we call the spotlight. The key idea is that similar inputs tend to have similar representations in the final hidden layer of a neural network. We leverage this structure by "shining a spotlight" on this representation space to find contiguous regions where the model performs poorly. We show that the spotlight surfaces semantically meaningful areas of weakness in a wide variety of model architectures, including image classifiers, language models, and recommender systems.Preprint. Under review.

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FairFace: Face Attribute Dataset for Balanced Race, Gender, and Age for Bias Measurement and Mitigation

Cited by 336 publications

References 44 publications

Fairness Indicators for Systematic Assessments of Visual Feature Extractors

Fairness Indicators for Systematic Assessments of Visual Feature Extractors

A Step Toward More Inclusive People Annotations for Fairness

The Spotlight: A General Method for Discovering Systematic Errors in Deep Learning Models

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