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International Journal of Computer Vision
Erschienen in: International Journal of Computer Vision 7/2022

23.05.2022

REVISE: A Tool for Measuring and Mitigating Bias in Visual Datasets

verfasst von: Angelina Wang, Alexander Liu, Ryan Zhang, Anat Kleiman, Leslie Kim, Dora Zhao, Iroha Shirai, Arvind Narayanan, Olga Russakovsky

Erschienen in: International Journal of Computer Vision | Ausgabe 7/2022

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Abstract

Machine learning models are known to perpetuate and even amplify the biases present in the data. However, these data biases frequently do not become apparent until after the models are deployed. Our work tackles this issue and enables the preemptive analysis of large-scale datasets. REvealing VIsual biaSEs (REVISE) is a tool that assists in the investigation of a visual dataset, surfacing potential biases along three dimensions: (1) object-based, (2) person-based, and (3) geography-based. Object-based biases relate to the size, context, or diversity of the depicted objects. Person-based metrics focus on analyzing the portrayal of people within the dataset. Geography-based analyses consider the representation of different geographic locations. These three dimensions are deeply intertwined in how they interact to bias a dataset, and REVISE sheds light on this; the responsibility then lies with the user to consider the cultural and historical context, and to determine which of the revealed biases may be problematic. The tool further assists the user by suggesting actionable steps that may be taken to mitigate the revealed biases. Overall, the key aim of our work is to tackle the machine learning bias problem early in the pipeline. REVISE is available at https://​github.​com/​princetonvisuala​i/​revise-tool.
Vorheriger Artikel Nonblind Image Deconvolution via Leveraging Model Uncertainty in An Untrained Deep Neural Network
Nächster Artikel Investigating the Role of Image Retrieval for Visual Localization

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Fußnoten
1
GeoJSON is a JSON-based standard for encoding boundary and region information through GPS data. GeoJSON files for many geographic regions are easily downloadable online, and can be readily converted from shapefiles, another type of geographic boundary file.
 
2
Because top-1 accuracy for even the best model on all 365 scenes is 55.19%, but top-5 accuracy is 85.07%, we use the less granular scene categorization at the second tier of the defined scene hierarchy here. For example, aquarium, church indoor, and music studio fall into the scene group of indoor cultural.
 
3
We use different subsets of the YFCC100m dataset depending on the particular annotations required by each metric.
 
4
We consider the subset of the BDD100K dataset with images in New York City, which is a majority of the dataset.
 
5
Random subset of size 100,000.
 
6
We also looked into using reverse image searches to recover the query, but the “best guess labels” returned from these searches were not particularly useful, erring on both the side of being much too vague, such as returning “sea” for any scene with water, or too specific, with the exact name and brand of one of the objects.
 
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Metadaten
Titel
REVISE: A Tool for Measuring and Mitigating Bias in Visual Datasets
verfasst von
Angelina Wang
Alexander Liu
Ryan Zhang
Anat Kleiman
Leslie Kim
Dora Zhao
Iroha Shirai
Arvind Narayanan
Olga Russakovsky
Publikationsdatum
23.05.2022
Verlag
Springer US
Erschienen in
International Journal of Computer Vision / Ausgabe 7/2022
Print ISSN: 0920-5691
Elektronische ISSN: 1573-1405
DOI
https://doi.org/10.1007/s11263-022-01625-5

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