8 Fairness and Bias Analysis

8.1 Geographical Fairness

We conduct two fairness evaluations of our models. We probe for geographical fairness and potential harmful
label associations. For both evaluations, we experiment with our largest ViT-g model.

We evaluate geographical fairness on the Dollar Street dataset introduced in De Vries et al. (2019) using
the evaluation protocol of Goyal et al. (2022b). This benchmark compares performance across countries and
income levels. It contains 16,073 images from 289 households across 54 countries. The task is to recognize
94 concepts that vary visually between households based on income or location. In Table 12, we compare
our model with SEERv2 (Goyal et al., 2022a), a model trained on a geographically diverse set of images.
Our model is slightly fairer across regions and incomes than the SEERv2 model and signiﬁcantly better than

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DINOv2- Learning Robust Visual Features without Supervision