SlowGuess/NeurIPS_2023_OCR huggingface dataset:
Factors most associated with misclassifications differ considerably across regions and incomes. We find for the high income bucket, objects marked as smaller are most associated with mistakes, appearing  $+2.8\mathrm{x}$  more among mistakes. On the other hand, texture which is not among the top five factors among mistakes in the high income bucket is associated with mistakes in the medium and low income buckets. Texture is  $+0.6\mathrm{x}$  and  $+1.7\mathrm{x}$  more likely to appear among mistakes in the medium and low income buckets respectively. We also find in the low income bucket, factors such as occlusion and darker lighting to be associated with model mistakes, appearing  $+1.2\mathrm{x}$  and  $+0.9\mathrm{x}$  more so among mistakes in the low income bucket. This suggests specific factors such as texture, occlusion, and darker lighting are associated with the disparity in performance we observe across incomes.
Further discussion of actors associated with mistakes across regions. We also measured the factors associated with model mistakes across regions in Figure 5 in Section 4.3 of the main text. In Asia we observe the factors most associated with mistakes are similar to those associated with mistakes overall. However, we find distinctive factors are associated with mistakes across each of the other regions. In the Americas, we find smaller objects (+1.2x more likely to appear among mistakes), followed by images with multiple objects (+0.3x). Similarly in Europe, smaller objects and multiple objects are most associated with mistakes appearing +2.8x and +0.7x more so among mistakes respectively. In Africa however, we find instead texture (+1.6x) most associated with mistakes, followed by occlusion (+0.9x) and darker lighting (+0.8x). This suggests the disparity
due to lower performance in regions such as Africa are associated with distinct factors related to texture, occlusion, and darker lighting.
Figure 11: Shows the full error ratios for each factor per income bucket (left) and region (right). An error ratio higher than zero indicates the factor is more associated with model mistakes; less than zero indicates the factor is less likely to appear among a model's mistakes.
Statistical significance of error ratios for top factors. To confirm the top factors associated with model mistakes measured by our error ratio are statistically significant. We conduct a Chi-Squared test comparing the overall distribution of counts of the top factors to their distribution of counts among misclassifications. We find a statistically significant difference with a Chi-Squared statistic of 21.7 (p-value =0.0002).
Class	Income	Factors associated with mistakes
sofas	low	pattern (+0.5x), background (+0.3x), pose (+0.2x)
toilet paper	low	texture (+3.3x), shape (+2.7x), color (+0.8x)
living rooms	low	background (+0.8x), pose (+0.0x), color (-0.1x)
kitchen sinks	low	color (+0.5x), background (+0.3x), pose (+0.2x)
showers	low	background (+0.9x), pose (+0.3x), pattern (-0.5x)
Table 13: Class-specific vulnerabilities surfaced by our factor labels. We show vulnerabilities for the classes with lowest income performance. The values in parenthesis indicate how much more likely a factor is to appear for misclassified samples.
Factors most associated with largest discrepancies for classes across income buckets. We show the three factors most associated with model mistakes for the classes across income buckets with largest performance gap in Table 13. Trends are similar to those shown in the main paper for the largest disparity per region.
Additional analysis of vulnerabilities by country In Table 14 we show the most vulnerable factor by country along with its error ratio for CLIP with a ViT encoder.
Additional analysis on the effect of architecture and training procedure We extend our evaluation of CLIP models to include a ResNet50 encoder, to enable more consistency between the architectures of our CLIP and supervised models. Results per income are given in 15.
A.5 Texture debiasing experimental details
To measure the effect of reducing texture bias from Geirhos et al., we create a mapping from Dollar Street classes to ImageNet-1k similar to Rojas et al. [2022]. We initialize the mapping by matching the embedding similarity of each class name to its nearest neighbors from ImageNet-1k using a pre-trained Spacy language model eng-large https://spacy.io/usage/linguistic-features#vectors-similarity. We then manually correct any
Country	Most vulnerable factor	Error Ratio
Bangladesh	shape	1.77
Bolivia	color	0.48
Brazil	multiple_objects	3.25
Burkina Faso	texture	1.56
Burundi	color	0.42
Cambodia	texture	0.7
Cameroon	background	0.23
China	smaller	3.52
Colombia	color	0.34
Egypt	pattern	0.47
France	pose	0.88
Haiti	shape	0.26
India	texture	1.2
Indonesia	smaller	2.15
Cote d’Ivoire	texture	2.17
Jordan	background	0.29
Kazakhstan	background	0.82
Kenya	background	0.51
South Korea	shape	1.71
Latvia	smaller	7.88
Lebanon	background	0.82
Liberia	color	0.49
Malawi	texture	2.27
South Africa	color	0.73
Mexico	pose	0.18
Myanmar	texture	1.84
Nepal	texture	1.65
Netherlands	pose	0.94
Nigeria	texture	1.29
Pakistan	background	1.05
Palestine	background	0.49
Papua New Guinea	background	0.29
Peru	pose	0.91
Philippines	texture	1.98
Romania	background	0.43
Russia	pose	0.42
Rwanda	texture	3.72
Somalia	background	0.58
Sri Lanka	background	2.65
Sweden	pose	0.28
Thailand	subcategory	0.16
Tunisia	background	0.4
United States	smaller	3.49
Ukraine	pose	0.38
United Kingdom	pose	0.39
Vietnam	background	0.3
Zimbabwe	background	0.55
Table 14: The most vulnerable factor for a CLIP ViT per country.
issues in this mapping to produce ImageNet-1k mappings for approximately half of the Dollar Street classes. Note for all other analysis we use the ImageNet-21k mapping from Goyal et al. [2022].
Model	high	middle	low
BEiTpretrained21k	64.9	60.4	51.7
MLPMixerPretrained21k	88.3	79.9	61.9
SeerPretrained	89.6	81.7	64.3
ViTpretrained21k	88.3	79.9	61.3
ResNet50Pretrained21k	86.5	77.4	58.4
CLIP ViTB/32	92.6	83.4	66.9
CLIP ResNet101	91.2	82.9	68.2
CLIP ResNet50	95.7	88.8	73.4
Table 15: Comparison of model architectures across incomes. Overall, we find that while architecture and learning objective are important factors for fairness considerations, there are consistent and similar vulnerabilities across models.
B Data Card
We provide a data card for our annotations, following the guidance of Pushkarna et al. [2022].
DollarStreet Factor Annotations
We provide annotations for Dollar Street images with distinctive factor labels such as pose, background, and color to explain performance disparities in models.
Data is available at:
https://github.com/facebookresearch/dollarstreet_factors
Data visualizer is available at:
https://dollarstreetfactors.metademolab.com/
Overview
Publisher	Meta
Authors	Laura Gustafson, Megan Richards, Melissa Hall, Caner Hazirbas, Diane Bouchacourt, Mark Ibrahim
Contact	dollarstreet-factors@meta.com
Funding & Funding Type	Fundamental AI Research
License	CC BY-NC 4.0
Applications
Dataset Purpose	Evaluate computer vision models robustness to common factors to help pinpoint where geographical and economical performance discrepancies arise.
Key Application	Computer Vision, Robustness, Fairness
Primary Motivations	We can use the factors to identify model vulnerabilities that contribute to these discrepancies. Pinpointing the vulnerabilities will help guide research into developing fairer models.
Intended Audience	Vision researchers aiming to analyse their trained vision models.
Suitable Use Case	Evaluation of Computer Vision models and analysis as to a model's strengths and weaknesses.
Data Type
Primary Data Type	Annotations for existing DollarStreet dataset of images
Primary Annotation Type	Annotations are manually gathered from expert annotators. Annotations are booleans for each of the factors, along with single word and paragraph responses detailing the annotator's logic.
Data SnapShot	Dataset contains
· Annotations for 14k images
· Each image is annotated with 16 factors
Data Sources	Annotations were manually gathered. Annotations are for images from the existing public DollarStreet dataset. https://www.gapminder.org/dollar-street Images are licensed under CC-BY.
DollarStreet Factor Annotations
Annotation format	Each item in the annotation file will contain:1. Image information:·url: Public url of image·full_image_id: Unique ID of image·household_id: Unique ID of household who took the photo·class: Image classification class2. Group information:·region: Region where the image is from. Options are The Americas, Europe, Africa, Asia. Derived from country.·income_bucket: Income bucket of household who took the image. Options are high income, middle income, low income. Derived from income·country: String of country name where image is taken.·lat: Latitude of country·lng: Longitude of country·income: Integer of income of household. TODOMetric3. Summary:·one_word: One word describing how the image differs from the prototypical images for it's class.·justification: String description for the annotators' justification of their one word summary.·agree_right: Boolean describing whether the annotator agreed with the class label·why_disagree: If agree_right is False this will contain a string explanation as to why the annotator disagreed with the class label4. Factors (Boolean):·multiple.objects·background·color·brighter·darker·style·larger·smaller·objectblocking·personBlocking·partial_view·pattern·pose·shape·subcategory·location·texture
B.1 Interactive factor dashboard
We show screenshots of our interactive dashboard for exploring the factor labels across regions in Figures 12 and 13. The dashboard allows for interactive queries by region, income, factor label. Each query yields sample images, which you can interactively explore annotations for as shown in 13. We hope this tool will allow researchers to easily explore factor labels associated with images across axes such as regions or incomes to spur further research into reliable vision systems.
Figure 12: Interactive dashboard for Dollar Street factor annotations with an income and factor label query (for texture).
Figure 13: Interactive dashboard for Dollar Street factor annotations illustrating an example of the annotations.
B.2 Sample images
Country
The Americas
Africa
Table 17: Examples of diaper images. Our factors surfaced that images of diapers in Dollar Street between regions differed most among pose, color, partial view.
Country
Asia
Africa
Table 18: Examples of goat images. Our factors surfaced that images of goats in Dollar Street between regions differed most among pattern, color, subcategory
In Tables 18 and 17 we show example images from classes and regions that were found to have some the starkest difference in factors, as measured by JSD.