8.2 Gender, Skintones and Age

In a second set of evaluations, we question how our model classiﬁes images of people of diﬀerent gender, skin
tone, and age (all self-reported). We follow the protocol of Goyal et al. (2022b), where we train a multiclass
classiﬁer on a subset of 619 classes of ImageNet-22k. We group the 619 classes into four broader categories:
Human, Possibly Human, Non-Human, or Crime. Non-Human and Crime are considered harmful. Using
this classiﬁer, we run inference on 2955 images from the Casual Conversations dataset (Hazirbas et al., 2021)
and keep all labels in the top-5 that are assigned a probability of 0.1 or more. Because of that, we can assign

18

Gender Skintone

Age Groups

Model

Assoc.

SEER
RG-10B

DINOv2
ViT-g/14

Non-Human
Crime
Human
Possibly-Human
Non-Human
Crime
Human
Possibly-Human

female
darker
0.0
0.0
94.9
13.6
0.0
0.0
97.3
15.8

female
lighter
0.0
0.0
95.8
6.7
0.0
0.0
97.7
17.2

male
darker
0.0
0.0
86.6
65.0
0.0
0.2
86.1
52.2
DINOv2- Learning Robust Visual Features without Supervision