# A View From Somewhere:
Human-Centric Face Representations

Jerone T. A. Andrews
Sony AI, Tokyo
&Przemysław Joniak
University of Tokyo, Tokyo
&Alice Xiang
Sony AI, New York Correspondence to<EMAIL_ADDRESS>done while
the author was an intern at Sony AI, Tokyo.

###### Abstract

Few datasets contain self-identified sensitive attributes, inferring
attributes risks introducing additional biases, and collecting attributes can
carry legal risks. Besides, categorical labels can fail to reflect the
continuous nature of human phenotypic diversity, making it difficult to
compare the similarity between same-labeled faces. To address these issues, we
present A View From Somewhere (AVFS)—a dataset of 638,180 human judgments of
face similarity.111Code and data may be found at
https://github.com/SonyAI/a_view_from_somewhere. We demonstrate the utility of
AVFS for learning a continuous, low-dimensional embedding space aligned with
human perception. Our embedding space, induced under a novel conditional
framework, not only enables the accurate prediction of face similarity, but
also provides a human-interpretable decomposition of the dimensions used in
the human-decision making process, and the importance distinct annotators
place on each dimension. We additionally show the practicality of the
dimensions for collecting continuous attributes, performing classification,
and comparing dataset attribute disparities.

##  1 Introduction

The canonical approach to evaluating human-centric image dataset diversity is
based on demographic attribute labels. Many equate diversity with parity
across the subgroup distributions (Kay et al., 2015; Schwemmer et al., 2020),
presupposing access to demographically labeled samples. However, most datasets
are web scraped, lacking ground-truth information about image subjects
(Andrews et al., 2023). Moreover, data protection legislation considers
demographic attributes to be personal information and limits their collection
and use (Andrus et al., 2021; 2020).

Even when demographic labels are known, evaluating diversity based on subgroup
counts fails to reflect the continuous nature of human phenotypic diversity
(e.g., skin tone is often reduced to “light” vs. “dark”). Further, even within
the same subpopulation, image subjects exhibit certain traits to a greater or
lesser extent than others (Becerra-Riera et al., 2019; Carcagnì et al., 2015;
Feliciano, 2016).

When labels are unknown, researchers typically choose certain attributes they
consider to be relevant for human diversity and use human annotators to infer
them (Karkkainen & Joo, 2021; Wang et al., 2019). Inferring labels, however,
is difficult, especially for nebulous social constructs, e.g., race and gender
(Hanna et al., 2020; Keyes, 2018), and can introduce additional biases
(Freeman et al., 2011). Beyond the inclusion of derogatory categories (Koch et
al., 2021; Birhane & Prabhu, 2021; Crawford & Paglen, 2019), label taxonomies
often do not permit multi-group membership, resulting in the erasure of, e.g.,
multi-ethnic individuals (Robinson et al., 2020; Karkkainen & Joo, 2021).
Significantly, discrepancies between inferred and self-identified attributes
can induce psychological distress by invalidating an individual’s self-image
(Campbell & Troyer, 2007; Roth, 2016).

In this work, we avoid problematic semantic labels altogether and propose to
learn a face embedding space aligned with human perception. To do so, we
introduce A View From Somewhere (AVFS)—a dataset of 638,180 face similarity
judgments over 4,921 faces. Each judgment corresponds to the odd-one-out
(i.e., least similar) face in a triplet of faces and is accompanied by both
the identifier and demographic attributes of the annotator who made the
judgment. Our embedding space, induced under a novel conditional framework,
not only enables the accurate prediction of face similarity, but also provides
a human-interpretable decomposition of the dimensions used in the human
decision-making process, as well as the importance distinct annotators place
on each dimension. We demonstrate that the individual embedding dimensions (1)
are related to concepts of gender, ethnicity, age, as well as face and hair
morphology; and (2) can be used to collect continuous attributes, perform
classification, and compare dataset attribute disparities. We further show
that annotators are influenced by their sociocultural backgrounds,
underscoring the need for diverse annotator groups to mitigate bias.

##  2 Related Work

Similarity. The human mind is conjectured to have, “a considerable investment
in similarity” (Medin et al., 1993). When two objects are compared they
mutually constrain the set of features that are activated in the human mind
(Markman, 1996)—i.e., features are dynamically discovered and aligned based on
what is being compared. Alignment is contended to be central to similarity
comparisons. Shanon (1988) goes as far as arguing that similarity is not a
function of features, but that the features themselves are a function of the
similarity comparison.

Contextual similarity. Human perception of similarity can vary with respect to
context (Roth & Shoben, 1983). Context makes salient context-related
properties and the extent to which objects being compared share these
properties (Medin et al., 1993; Markman & Gentner, 2005; Goodman, 1972). For
example, Barsalou (1987) found that “snakes” and “raccoons” were judged to be
more similar when placed in the context of pets than when no context was
provided. The odd-one-out similarity task (Zheng et al., 2019) used in this
work also provides context. By varying the context (i.e., the third object) in
which two objects are experienced, it is possible to uncover different
features that contribute to their pairwise similarity. This is important as
there are an uncountable number of ways in which two objects may be similar
(Love & Roads, 2021).

Psychological embeddings. Multidimensional scaling (MDS) is often used to
learn psychological embeddings from human similarity judgments (Zheng et al.,
2019; Roads & Love, 2021; Dima et al., 2022; Josephs et al., 2021). As MDS
approaches cannot embed images outside of the training set, researchers have
used pretrained models as feature extractors (Sanders & Nosofsky, 2020;
Peterson et al., 2018; Attarian et al., 2020), which can introduce unwanted
implicit biases (Krishnakumar et al., 2021; Steed & Caliskan, 2021). Moreover,
previous approaches ignore inter- and intra-annotator variability. By
contrast, our conditional model is trained end-to-end and can embed any
arbitrary face from the perspective of a specific annotator.

Face datasets. Most face datasets are semantically labeled images, created for
the purposes of identity and attribute recognition (Karkkainen & Joo, 2021;
Liu et al., 2015; Huang et al., 2008; Cao et al., 2018). An implicit
assumption is that semantic similarity is equivalent to visual similarity
(Deselaers & Ferrari, 2011). However, many semantic categories are functional
(Rosch, 1975; Rothbart & Taylor, 1992), i.e., unconstrained by visual features
such as shape, color, and material. Moreover, semantic labels often only
indicate the presence or absence of an attribute, as opposed to its magnitude,
making it difficult to compare the similarity between same-labeled samples.
While face similarity datasets exist, the judgments narrowly pertain to
identity (McCauley et al., 2021; Sadovnik et al., 2018; Somai & Hancock, 2021)
and expression similarity (Vemulapalli & Agarwala, 2019).

Annotator positionality. Semantic categorization by annotators not only
depends on the image subject, but also on extrinsic contextual cues (Freeman
et al., 2011) and the annotators’ sociocultural backgrounds (Segall et al.,
1966). Despite this, annotator positionality is rarely discussed in computer
vision (Chen & Joo, 2021; Zhao et al., 2021; Denton et al., 2021); “only five
publications [from 113] provided any [annotator] demographic information”
(Scheuerman et al., 2021). To our knowledge, AVFS represents the first human-
centric vision dataset, where each annotation is associated with the annotator
who created it and their demographics, permitting the study of annotator bias.

##  3 A View From Somewhere Dataset

To learn a face embedding space aligned with human perception, we collect
AVFS—a dataset of odd-one-out similarity judgments collected from humans. An
odd-one-out judgment corresponds to the least similar face in a triplet of
faces, representing a three-alternative forced choice (3AFC) task. AVFS
dataset documentation can be found in Appendix C.

Face image stimulus set. 4,921 near-frontal faces with limited eye occlusions
and an apparent age $>\text{19}$ years old were sampled from the CC-BY
licensed FFHQ (Karras et al., 2019) dataset. The subset was obtained by (1)
splitting FFHQ into 56 partitions based on inferred intersectional group
labels; and (2) randomly sampling from each partition with equal probability.
Ethnicity was estimated using a FairFace model (Karkkainen & Joo, 2021); and
binary gender expression and age group labels were obtained from FFHQ-Aging
crowdsourced human annotations (Or-El et al., 2020).

3AFC similarity judgments. AVFS contains 638,180 quality-controlled triplets
over 4,921 faces, representing 0.003% of all possible triplets. To focus
judgments on intrinsic face-varying features, for each presented triplet,
annotators were instructed to choose the person that looks least similar to
the two other people, while ignoring differences in pose, expression,
lighting, accessories, background, and objects. Each AVFS triplet is labeled
with a judgment, as well as the identifier of the annotator who made the
judgment and the annotator’s self-reported age, nationality, ancestry, and
gender identity. As in previous non-facial odd-one-out datasets (Josephs et
al., 2021; Hebart et al., 2022; Dima et al., 2022), there is a single judgment
per triplet. Quality was controlled by excluding judgments from annotators who
provided overly fast, deterministic, or incomplete responses. In total, 1,645
annotators contributed to AVFS via Amazon Mechanical Turk (AMT) and provided
consent to use their study data. Compensation was 15 USD per hour.

3AFC task rationale. Let $\mathbf{x}\in\mathcal{X}$ and
$\operatorname{sim}:(\mathbf{x}_{i},\mathbf{x}_{j})\rightarrow\operatorname{sim}(i,j)\in\mathbb{R}$
denote a face image and a similarity function, respectively. Our motivation
for collecting AVFS is fourfold. Most significantly, the odd-one-out task does
not require an annotator to explicitly categorize people. Second, for a
triplet $(\mathbf{x}_{i},\mathbf{x}_{j},\mathbf{x}_{k})$, repeatedly varying
$\mathbf{x}_{k}$ permits the identification of the relevant dimensions that
contribute to $\operatorname{sim}(i,j)$. That is, w.l.o.g., $\mathbf{x}_{k}$
provides context for which $\operatorname{sim}(i,j)$ is determined, making the
task easier than explicit pairwise similarity tasks (i.e., “Is
$\mathbf{x}_{i}$ similar to $\mathbf{x}_{j}$?”). This is because it is not
always apparent to an annotator which dimensions are relevant when determining
$\operatorname{sim}(i,j)$, especially when $\mathbf{x}_{i}$ and
$\mathbf{x}_{j}$ are perceptually different. Third, there is no need to
prespecify attribute lists hypothesized as relevant for comparison (e.g., “Is
$\mathbf{x}_{i}$ older than $\mathbf{x}_{j}$?”). The odd-one-task implicitly
encodes salient attributes that are used to determine similarity. Finally,
compared to 2AFC judgments for triplets composed of an anchor (i.e., reference
point), $\mathbf{x}_{a}$, a positive, $\mathbf{x}_{p}$, and a negative,
$\mathbf{x}_{n}$, 3AFC judgments naturally provide more information. 3AFC
triplets require an annotator to determine
$\\{\operatorname{sim}(i,j),\operatorname{sim}(i,k),\operatorname{sim}(j,k)\\}$,
whereas 2AFC triplets with anchors only necessitate the evaluation of
$\\{\operatorname{sim}(a,p),\operatorname{sim}(a,n)\\}$.

##  4 Model of Conditional Decision-Making

Zheng et al. (2019) developed an MDS approach for learning continuous, non-
negative, sparse embeddings from odd-one-out judgments. The approach has been
shown to offer an intepretable window into the dimensions in the human mind of
object categories (Hebart et al., 2020), human actions (Dima et al., 2022),
and reachspace environments (Josephs et al., 2021) such that dimensional
values indicate feature magnitude. The method is based on three assumptions:
(1) embeddings can be learned solely from odd-one-out judgments; (2) judgments
are a function of
$\\{\operatorname{sim}(i,j),\operatorname{sim}(i,k),\operatorname{sim}(j,k)\\}$;
and (3) judgments are stochastic, where the probability of selecting $x_{k}$
is

$p(k)\propto\exp(\operatorname{sim}(i,j)).$ (1)

Conditional similarity. At a high-level, we want to apply Zheng et al.
(2019)’s MDS approach to learn face embeddings. However, the method (1) cannot
embed data outside of the training set, limiting its utility; and (2) pools
all judgments, disregarding intra- and inter-annotator stochasticity.
Therefore, we instead propose to learn a conditional convolutional neural net
(convnet).

Let
$\left\\{(\\{\mathbf{x}_{i\ell},\mathbf{x}_{j\ell},\mathbf{x}_{k\ell}\\},k\ell,a)\right\\}_{\ell=1}^{n}$
denote a training set of $n$ (triplet, judgment, annotator) tuples, where
$a\in\mathcal{A}$. To simplify notation, we assume that judgments always
correspond to index ${k\ell}$. Suppose $f:\mathbf{x}\mapsto
f(\mathbf{x})=\mathbf{w}\in\mathbb{R}^{d}$ is a convnet, parametrized by
$\bm{\Theta}$, where $\mathbf{w}\in\mathbb{R}^{d}$ is an embedding of
$\mathbf{x}$. In contrast to Zheng et al. (2019), we model the probability of
annotator $a$ selecting ${k\ell}$ as

$p(k\ell\mid a)\propto\exp(\operatorname{sim}_{a}(i\ell,j\ell)),$ (2)

where $\operatorname{sim}_{a}(\cdot,\cdot)$ denotes the internal similarity
function of $a$. Given two images $\mathbf{x}_{i}$ and $\mathbf{x}_{j}$, we
define their similarity according to $a$ as:

$\operatorname{sim}_{a}(i,j)=(\sigma(\bm{\phi}_{a})\odot\operatorname*{\mathrm{ReLU}}(\mathbf{w}_{i}))^{\top}\cdot(\sigma(\bm{\phi}_{a})\odot\operatorname*{\mathrm{ReLU}}(\mathbf{w}_{j})),$
(3)

where $\sigma(\cdot)$ is the sigmoid function and
$\sigma(\bm{\phi}_{a})\in[0,1]^{d}$ is a mask associated with $a$. Each mask
plays the role of an element-wise gating function, encoding the importance $a$
places on each of the $d$ embedding dimensions when determining similarity.
Conditioning prediction on $a$ induces subspaces that encode each annotator’s
notion of similarity, permitting us to study whether per-dimensional
importance weights differ between $a\in\mathcal{A}$. Veit et al. (2017)
employed a similar procedure to learn subspaces which encode different notions
of similarity (e.g., font style, character type).

Conditional loss. We denote by
$\bm{\Phi}=[\bm{\phi}_{1}^{\top},\dots,\bm{\phi}_{\lvert\mathcal{A}\rvert}^{\top}]\in\mathbb{R}^{d\times\lvert\mathcal{A}\rvert}$
a trainable weight matrix, where each column vector $\bm{\phi}_{a}^{\top}$
corresponds to annotator $a$’s mask prior to applying $\sigma(\cdot)$. In our
conditional framework, we jointly optimize $\bm{\Theta}$ and $\bm{\Phi}$ by
minimizing:

$-\sum_{\ell}\log\left[\hat{p}(k\ell\mid
a)\right]+\alpha_{1}\sum_{i}\lVert\operatorname*{\mathrm{ReLU}}(\mathbf{w}_{i})\rVert_{1}+\alpha_{2}\sum_{ij}\operatorname*{\mathrm{ReLU}}(-\mathbf{w}_{i})_{j}+\alpha_{3}\sum_{a}\lVert\bm{\phi}_{a}\rVert_{2}^{2},$
(4)

where

$\hat{p}(k\ell\mid
a)=\dfrac{\exp({\operatorname{sim}_{a}(i\ell,j\ell)})}{\exp({\operatorname{sim}_{a}(i\ell,j\ell)})+\exp({\operatorname{sim}_{a}(i\ell,k\ell)})+\exp({\operatorname{sim}_{a}(j\ell,k\ell)})}$
(5)

is the predicted probability that $k\ell$ is the odd-one-out conditioned on
$a$. The first term in Equation 4 encourages similar face pairs to result in
large dot products. The second term (modulated by $\alpha_{1}\in\mathbb{R}$)
promotes sparsity. The third term (modulated by $\alpha_{2}\in\mathbb{R}$)
supports interpretability by penalizing negative values. The last term
(modulated by $\alpha_{3}\in\mathbb{R}$) penalizes large weights.

Our conditional decision-making framework is applicable to any task that
involves mapping from inputs to decisions made by humans; it only requires
record-keeping during data collection such that each annotation is associated
with the annotator who generated it.

Implementation. When $\alpha_{3}=0$ and $\sigma(\bm{\phi}_{a})=[1,\dots,1]$,
$\forall a$, Equation 4 corresponds to the unconditional MDS objective
proposed by Zheng et al. (2019). We refer to unconditional and conditional
models trained on AVFS as AVFS-U and AVFS-C, respectively, and conditional
models whose masks are learned post hoc as AVFS-CPH. An AVFS-CPH model uses a
fixed unconditional model (i.e., AVFS-U) as a feature extractor to obtain face
embeddings such that only $\bm{\Phi}$ is trainable.

AVFS models have ResNet18 (He et al., 2016) architectures and output
128-dimensional embeddings. We use the Adam (Kingma & Ba, 2014) optimizer with
default parameters, reserving 10% of AVFS for validation. Based on grid
search, we empirically set $\alpha_{1}=\text{0.00005}$ and
$\alpha_{2}=\text{0.01}$. For AVFS-C and AVFS-CPH, we additionally set
$\alpha_{3}=\text{0.00001}$. Across independent runs, we find that only a
fraction of the 128 dimensions are needed and individual dimensions are
reproducible. Post-optimization, we remove dimensions with maximal values
(over the stimulus set) close to zero. For AVFS-U, this results in 22
dimensions (61.9% validation accuracy). Note that we observe that 8/22
dimensions have a Pearson’s $r$ correlation ${>\text{0.9}}$ with another
dimension. Refer to Appendix A for further implementation details.

##  5 Experiments

The aim of this work is to learn a face embedding space aligned with human
perception. To highlight the value of AVFS, we evaluate the performance of
AVFS embeddings and conditional masks for predicting face similarity
judgments, as well as whether they reveal (1) a human-interpretable
decomposition of the dimensions used in the human decision-making process; and
(2) the importance distinct annotators place on each dimension. We further
assess the practicality of the dimensions for collecting continuous
attributes, classification, and comparing dataset attribute disparities.

Embedding methods. Where appropriate, for comparison, we consider embeddings
extracted from face identity verification and face attribute recognition
models: CASIA-WebFace (Yi et al., 2014) is a face verification model trained
with an ArcFace (Deng et al., 2019) loss on images from 11K identities; CelebA
(Liu et al., 2015) is a face attribute model trained to predict 40 binary
attribute labels (e.g., “pale skin”, “young”, “male”); and FairFace
(Karkkainen & Joo, 2021) and FFHQ are face attribute models trained to predict
gender, age, and ethnicity. All models have ResNet18 architectures and output
128-dimensional embeddings (prior to the classification layer). All baseline
embeddings are normalized to unit-length, where the dot product of two
embeddings determines their similarity. Additional details on the datasets and
baselines are presented in Appendix B.

###  5.1 Same Stimuli

Predicting similarity. We analyze whether our model results in stimulus set
embeddings such that we are able to predict human 3AFC judgments not observed
during learning. Using images from the stimulus set of 4,921 faces, we
generate 1,000 novel triplets and collect 22–25 unique judgments per triplet
(24,060 judgments) on AMT. As we have 22–25 judgments per triplet, we can
reliably estimate odd-one-out probabilities. The Bayes optimal classifier
accuracy corresponds to the best possible accuracy any model could achieve
given the stochasticity in the judgments. The classifier makes the most
probable prediction, i.e., the majority judgment. Thus, its accuracy is equal
to the mean majority judgment probability over the 1,000 triplets,
corresponding to 65.5$\pm$1% (95% CI).

In addition to accuracy, we report Spearman’s $r$ correlation between the
entropy in human- and model-generated triplet odd-one-out probabilities.
Human-generated odd-one-out probabilities are of the form:
$(n_{i},n_{j},n_{k})/n$, where, without loss of generality, $n_{k}/{n}$
corresponds to the fraction of $n$ odd-one-out votes for $k$. Results are
shown in Figure 1. We observe that AVFS models outperform the baselines with
respect to accuracy, while better reflecting human perceptual judgment
uncertainty (i.e., entropy correlation). This holds as we vary the number of
AVFS training triplets, for example, to as few as 12.5% (72K) as evidenced by
the AVFS-U-12.5% model. Further, our learned annotator-specific dimensional
importance weights generalize, resulting in a predictive accuracy at or above
the Bayes classifier’s. As hypothesized (Section 3), transforming AVFS into
2AFC triplets (i.e., AVFS-2AFC in Figure 1) decreases performance due to a
reduction in information.222AVFS-2AFC was trained using a triplet margin with
a distance swap loss (Balntas et al., 2016).

Accuracy Spearman’s _r_ Figure 1: Predicting similarity (same stimuli).
Accuracy over 24,060 judgments and Spearman’s $r$ between the entropy in
human- and model-generated triplet odd-one-out probabilities. Even when
trained on a fraction of AVFS judgments (i.e., 12.5%), the learned
AVFS-U-12.5% model still outperforms the baselines. Additional results are
presented in Appendix A.

Dimensional interpretability. Following Hebart et al. (2020), we qualitatively
assess whether learning on AVFS results in a human-interpretable decomposition
of the dimensions used in the human decision-making process. Let $\mathbf{x}$
and $\operatorname*{\mathrm{ReLU}}(\mathbf{w})\in\mathbb{R}^{\text{22}}$
denote a stimulus set image and its AVFS-U model embedding, respectively. We
denote by $\operatorname{dim}_{j}$ and $\operatorname{\%tile}_{j}^{q}$ the
$j^{\text{th}}$ dimension and percentile of $\operatorname{dim}_{j}$ stimulus
set embedding values, respectively. Suppose $\operatorname{grid}_{j}$ is a
${\text{5}\times\text{100}}$ face grid. Column ${q\in[100,\dots,1]}$ of
$\operatorname{grid}_{j}$ contains 5 stimulus set faces with the highest
$\operatorname{dim}_{j}$ values, satisfying
${\operatorname{\%tile}_{j}^{q-1}\leq\operatorname*{\mathrm{ReLU}}(\mathbf{w})_{j}<\operatorname{\%tile}_{j}^{q}}$.
Thus, from left to right, $\operatorname{grid}_{j}$ displays stimulus set
faces sorted in descending order based on their $\operatorname{dim}_{j}$
value. We task annotators with writing 1–3 $\operatorname{grid}_{j}$
descriptions. After controlling for quality, we obtain 23–58 labels (708
labels) for each $j$ and categorize the label responses into 35 topics.

Figure 2 evidences a clear relationship between the top 3
$\operatorname{dim}_{j}$ topics and $\operatorname{dim}_{j}$ labels generated
using CelebA and FairFace models. Across the 22 dimensions, we note the
materialization of individual dimensions coinciding with commonly collected
demographic group labels, i.e., “male”, “female”, “black”, “white”, “east
asian”, “south asian”, “elderly”, and “young”. In addition, separate
dimensions surface for face and hair morphology, i.e., “wide face”, “long
face”, “smiling expression”, “neutral expression”, “balding”, and “facial
hair”. Significantly, these dimensions, which largely explain the variation in
human judgment behavior, are learned without training on semantic labels.

Dim. 1/22: “south asian” “ancestry”, “skin color”, “facial expression”
“indian”, “bushy eyebrows”, “latino hispanic” a b c Dim. 4/22: “female”
“gender expression”, “age related”, “ancestry” “female”, “wearing lipstick”,
“heavy makeup” Dim. 5/22: “smiling exp.” “facial expression”, “skin color”,
“mouth related” “smiling”, “high cheekbones”, “rosy cheeks” Dim. 10/22: “wide
face” “face shape”, “facial expression”, “weight related” “double chin”, “high
cheekbones”, “smiling” Dim. 11/22: “elderly” “age related”, “ancestry”, “hair
color” “70+”, “60–69”, “gray hair” Dim. 18/22: “balding” “hair length”,
“gender expression”, “ancestry” “bald”, “gray hair”, “wearing necktie” Figure
2: Dimensional interpretability. (a) Our interpretation of an AVFS-U dimension
with the highest scoring stimulus set images. (b) Highest frequency human-
generated topic labels. (c) CelebA and FairFace model-generated labels,
exhibiting a clear correspondence with human-generated topics. Face images in
this figure have been replaced by synthetic StyleGAN3 (Karras et al., 2021)
images for privacy reasons. Additional dimensions are presented in the dataset
documentation (Appendix C).

###  5.2 Novel Stimuli

Predicting similarity. We evaluate whether AVFS models transfer to novel
stimuli by sampling 56 novel face images from FFHQ (not contained in the
stimulus set). We then generate all
$\binom{\text{56}}{\text{3}}=\text{27,720}$ possible triplets, collecting 2–3
unique judgments per triplet (80,300 judgments) on AMT. We report accuracy, as
well as Spearman’s $r$ between the strictly upper triangular model- and human-
generated similarity matrices. Entry $(i,j)$ in the human-generated similarity
matrix corresponds to the fraction of triplets containing $(i,j)$, where
neither was judged as the odd-one-out; and $(i,j)$ in a model-generated
similarity matrix corresponds to the mean $\hat{p}(i,j)$ over all triplets
containing $(i,j)$.

Results are shown in Figure 3. We see that AVFS embedding spaces and the
learned annotator-specific masks generalize to triplets composed entirely of
novel faces. This non-trivially demonstrates that AVFS embedding spaces are
more closely aligned with the human mental representational space of faces,
than embedding spaces induced by training on semantic labels.

Accuracy Spearman’s _r_ Figure 3: Predicting similarity (novel stimuli).
Accuracy over 80,300 judgments and Spearman’s $r$ between the entropy in
human- and model-generated similarity matrices. Our annotator-specific masks
(i.e., AVFS-C and AVFS-CPH models) generalize to triplets composed entirely of
novel faces, resulting in increased predictive accuracy. Additional results
are presented in Appendix A.

To quantify the number of AVFS-U dimensions required to represent a face while
preserving performance, we follow Hebart et al. (2020)’s dimension-elimination
approach. We iteratively zero out the lowest value per face embedding until a
single nonzero dimension remains. This is done per embedding, thus the same
dimension is not necessarily zeroed out from all embeddings during an
iteration. To obtain 95–99% of the predictive accuracy we require 6–13
dimensions, whereas to explain 95–99% of the variance in the similarity matrix
we require 15–22 dimensions. This shows that (1) humans utilize a larger
number of dimensions to represent the global similarity structure of faces
than for determining individual 3AFC judgments; and (2) similarity is context-
dependent (i.e., dynamic), which is not accounted for when representing faces
using categorical feature vectors.

Annotator positionality. As shown via our conditional framework, knowledge of
the annotator determining similarity assists in informing the outcome.
However, annotators are often framed as interchangeable (Malevé, 2020;
Chancellor et al., 2019). To assess the validity of this assumption, we
randomly swap the annotator associated with each of the collected 80,300
judgments and recompute the AVFS-CPH model accuracy. Repeating this process
100 times results in a performance drop from 61.7% to
$\text{52.8\%}\pm\text{0.02\%}$ (95% CI), evidencing that annotator masks, and
hence annotators, are not arbitrarily interchangeable.

a Annotator positionality b Prototypicality “masculine” “feminine” “east
asian” “black” “white” Figure 4: (a) Annotator positionality. Average linear
SVM AUC when trained to discriminate between AVFS-CPH annotator-specific masks
from different demographic groups. None of the confidence intervals overlap
with chance performance (horizontal black line). Note the abbreviations:
“europe” (“eu”), “asia” (“as”), “western europe” (“euwe”), and “southern asia”
(“assa”). (b) Prototypicality. Spearman’s $r$ between human ratings of face
typicality and model-generated ratings, evidencing that social category
typicality manifests in the AVFS-U model’s embedding dimensions.

Another interesting question relates to whether an annotator’s sociocultural
background influences their decision making. To evaluate this, we create
datasets $\\{(\sigma(\bm{\phi}_{a}^{\top}),y)\\}$, where
$\sigma(\bm{\phi}_{a}^{\top})$ and $y\in\mathcal{Y}$ are annotator $a$’s
learned mask and self-identified demographic attribute, respectively. For a
particular annotator attribute (e.g., nationality), we limit the dataset to
annotators who contributed ${\geq\text{200}}$ judgments and belong to one of
the two largest groups with respect to the attribute (e.g.,
$\mathcal{Y}=\\{\text{``american''},\text{``indian''}\\}$). Using 10-fold
cross-validation, we train linear SVMs (Hearst et al., 1998) with balanced
class weights to predict $y$ from $\sigma(\bm{\phi}_{a}^{\top})$. Figure 4a
shows that for each attribute none of the AUC confidence intervals include
chance performance. Significantly, we are able to discriminate between
nationality-, regional ancestry-, and subregional ancestry-based annotator
group masks with high probability (86–88%). Thus, to mitigate bias, diverse
annotator groups are required.

Continuous attribute collection. Following Hebart et al. (2020)’s evaluation
protocol, we quantitatively validate whether $\operatorname{grid}_{j}$
($\forall j$)—defined in Section 5.1—can be used to collect continuous
attribute values for novel faces from human annotators. We task annotators
with placing a novel face $\mathbf{x}$ above a single column $q$ of
$\operatorname{grid}_{j}$. Placement is based on the similarity between
$\mathbf{x}$ and the faces contained in each column $q$ of
$\operatorname{grid}_{j}$. As annotators are not primed with the meaning of
$\operatorname{grid}_{j}$, accurate placement indicates that the ordering of
$\operatorname{dim}_{j}$ is visually meaningful.

Let $\mu_{j}^{q}$ denote the mean $\operatorname{dim}_{j}$ embedding value
over the stimulus set whose $\operatorname{dim}_{j}$ value satisfies
${\operatorname{\%tile}_{j}^{q-1}\leq\operatorname*{\mathrm{ReLU}}(\mathbf{w})_{j}<\operatorname{\%tile}_{j}^{q}}$.
We sample 20 novel faces from FFHQ (not contained in the stimulus set).
$\forall(\mathbf{x},\operatorname{grid}_{j})$ and collect 20 unique judgments
per face (8,800 judgments). $\forall\mathbf{x}$, we create a human-generated
embedding of the form:
$[\frac{1}{20}\sum_{n=1}^{20}\mu_{1}^{q_{n}},\dots,\frac{1}{20}\sum_{n=1}^{20}\mu_{22}^{q_{n}}]\in\mathbb{R}^{22}$,
where $q_{n}\in[100,\dots,1]$ denotes the $n^{\text{th}}$ annotator’s column
choice $q$. We then generate all $\binom{\text{20}}{\text{3}}$ possible
triplets to create a human-generated similarity matrix
$\mathbf{K}\in\mathbb{R}^{20\times 20}$. Entry $(i,j)$ corresponds to the mean
$\hat{p}(i,j)$ over all triplets containing $(i,j)$ with the human-generated
embeddings. Using model-generated embeddings, we create a model-generated
matrix, $\hat{\mathbf{K}}\in\mathbb{R}^{20\times 20}$, in the same way.

Spearman’s $r$ correlation between the strictly upper triangular elements of
$\mathbf{K}$ and $\hat{\mathbf{K}}$ is 0.83 and 0.86 for AVFS-U and AVFS-CPH
embeddings, respectively. This shows that (1) dimensional values correspond to
feature magnitude; and (2) image grids can be used to directly collect
continuous attribute values, sidestepping the limits of semantic category
definitions (Keyes, 2018; Benthall & Haynes, 2019).

Prototypicality. In cognitive science, prototypicality corresponds to the
extent to which an entity belongs to a conceptual category (Rosch, 1973). We
examine whether the dimensional value of a face is correlated with its
typicality, utilizing prototypicality rated face images from the Chicago Face
Database (CFD) (Ma et al., 2015; Lakshmi et al., 2021; Ma et al., 2021).
Ratings correspond to the average prototypicality of a face with respect to a
binary gender category (relative to others of the same race) or race category.
For each category, we compute the dimensional embedding value or unnormalized
attribute logit of a face from a relevant AVFS-U dimension or attribute
recognition model’s classification layer, respectively.

Figure 4b shows that relevant AVFS-U dimensions are positively correlated with
typicality according to Spearman’s $r$. Evidently, category typicality—at
least for the investigated social category concepts—manifests in the AVFS
dimensions from learning to predict human 3AFC similarity judgments.

Semantic classification. Given the evidence that AVFS-U dimensional values
correlate with feature magnitude, we expect them to be useful for semantic
attribute classification. To validate this hypothesis, we perform attribute
prediction on the following labeled face datasets: COCO (Lin et al., 2014),
OpenImages MIAP (MIAP) (Schumann et al., 2021), CFD, FFHQ, CelebA (Liu et al.,
2015), and Casual Conversations (CasCon) (Hazirbas et al., 2021). Continuous
attribute value predictions are computed in the same manner as in the
prototypicality experiment. Results are shown in Figure 5. We observe that
AVFS-U dimensions are competitive with semantically trained recognition
models, even on challenging unconstrained data (e.g., COCO, MIAP).

$\ast$Self-identified labelAVFS-UCelebAFairFaceFFHQ Model was trained on the
test data

∗CFD: “male” ∗CasCon: “male” CelebA: “male” COCO: “male” MIAP: “male” FFHQ:
“male”

∗CasCon: “70+” FFHQ: “70+” CelebA: “smiling” ∗CFD: “happy” ∗CFD: “east asian”
∗CFD: “black”

∗CFD: “white” ∗CFD: “indian” CasCon: “light skin” COCO: “light skin” CelebA:
“balding”

Figure 5: Semantic classification. AUC results are shown for binary attribute
classification using continuous attribute value predictions. Our AVFS-U model
is competitive with the semantically trained attribute recognition model
baselines, despite not having been trained on semantic labels.

Attribute disparity estimation. Next we investigate the utility of individual
embedding dimensions for the task of comparing dataset attribute disparities.
Suppose we are given $n$ subsets $\\{\mathcal{S}_{1},\dots,\mathcal{S}_{n}\\}$
sampled from $\mathcal{S}^{*}$, where, $\forall c\in[1\dots,n]$,
$r_{c}\in[0,0.01,\dots,0.99,1]$ and $1-r_{c}$ denote the ground-truth
proportion of $\mathcal{S}_{c}=\\{(\mathbf{x}_{i},y_{i})\\}_{i}$, labeled
$y=0$ and $y=1$, respectively. We aim to find $c$ with the lowest ground-truth
attribute disparity, i.e.,
$\operatorname*{arg\,min}_{c}\Delta(c)=\operatorname*{arg\,min}_{c}\operatorname{abs}(2r_{c}-1)$.

We define
$\operatorname{sim}(i,j)=\operatorname{abs}(\hat{y}_{i}-\hat{y}_{j})^{-1}$ as
the similarity between $\mathbf{x}_{i}$ and $\mathbf{x}_{j}$, where
$\hat{y}\in\mathbb{R}$ is a predicted continuous attribute value for ground-
truth $y\in\\{0,1\\}$ given $\mathbf{x}$. $\hat{y}$ is computed in the same
manner as in the prototypicality and semantic classification experiments. Let
$\mathbf{K}\in\mathbb{R}^{m\times m}$ denote a similarity matrix, where $m$
denotes the number of images and $\mathbf{K}_{i,j}=\operatorname{sim}(i,j)$.
The average similarity of samples in a set $\mathcal{S}_{c}$, is
$\propto\sum_{i}\sum_{j}\mathbf{K}_{i,j}$ (Leinster & Cobbold, 2012). We
estimate $c$ as follows:
$\operatorname*{arg\,min}_{c}\hat{\Delta}(c)=\operatorname*{arg\,min}_{c}\lvert\mathcal{S}_{c}\rvert^{-2}\sum_{i}\sum_{j}\mathbf{K}_{i,j}$.
In all experiments, we set $n=100$ and $m=100$. For $\mathcal{S}^{*}$, we use
the following labeled face datasets: COCO, MIAP, CFD, FFHQ, CelebA, and
CasCon. Averaging over 100 runs, we report (1)
$\Delta(\operatorname*{arg\,min}_{c}\hat{\Delta}(c))$; and (2) Spearman’s $r$
between $\\{\hat{\Delta}(1),\dots,\hat{\Delta}(n)\\}$ and
$\\{\Delta(1),\dots,\Delta(n)\\}$.

Results are shown in Figure 6. Despite not being trained on semantically
labeled data, we observe that AVFS-U dimensions are competitive with the
baselines, in particular AVFS-U disparity scores are highly correlated with
ground-truth disparity scores.

$\ast$Self-identified labelAVFS-UCelebAFairFaceFFHQ Model was trained on the
test data

∗CFD: “male” ∗CasCon: “male” CelebA: “male” COCO: “male” MIAP: “male” FFHQ:
“male”

∗CasCon: “70+” FFHQ: “70+” CelebA: “smiling” ∗CFD: “happy” ∗CFD: “east asian”
∗CFD: “black”

∗CFD: “white” ∗CFD: “indian” CasCon: “light skin” COCO: “light skin” CelebA:
“balding”

Figure 6: Attribute disparity estimation. Average attribute disparity
($y$-axis) in data subsets selected as the most diverse and average Spearman’s
$r$ ($x$-axis) between the ground-truth disparity for each subset considered
and model-estimated disparity scores. Results highlight that disparity scores
derived using AVFS-U model dimensions are highly correlated with ground-truth
disparities.

##  6 Discussion

Motivated by legal, technical, and ethical considerations associated with
semantic labels, we introduced AVFS—a new dataset of 3AFC face similarity
judgments over 4,921 faces. We demonstrated the utility of AVFS for learning a
continuous, low-dimensional embedding space aligned with human perception. Our
embedding space, induced under a novel conditional framework, not only enables
the accurate prediction of face similarity, but also provides a human-
interpretable decomposition of the dimensions used in the human-decision
making process, and the importance distinct annotators place on each
dimension. We further showed the practicality of the dimensions for collecting
continuous attributes, performing classification, and comparing dataset
attribute disparities.

Since AVFS only contains 0.003% of all possible triplets over 4,921 faces,
future triplets could be actively sampled (Lindley, 1956; MacKay, 1992) so as
to learn additional face-varying dimensions using larger stimulus sets. For
example, Roads & Love (2021) selectively sample from an approximate posterior
distribution using variational inference and model ensembles. However, similar
to Zheng et al. (2019)’s MDS approach, judgments are pooled, which disregards
an individual annotator’s decision-making process. As we have shown,
annotators are (at least in our task) not interchangeable and are influenced
by their sociocultural backgrounds. Future work could therefore combine our
model of conditional decision-making with active sampling, e.g., seeking at
each iteration unlabeled triplets that maximize expected information gain
conditioned on the annotator (or annotator group) that will be assigned the
labeling task.

Our work is not without its limitations. First, we implicitly assumed that the
stimulus set was diverse. Therefore, our current proposal is limited to the
proof of concept that we can learn face-varying dimensions that are both
present within the stimuli and salient to human judgments of face similarity.
Our stimulus set was sampled such that the faces were diverse with respect to
inferred demographic subgroup labels. Ideally, these labels would have been
self-reported. With the principle of data minimization in mind, a potential
solution would be to collect stimuli from unique individuals that differ in
terms of their country of residence, providing both identity- and geographic-
based label diversity. Notably, country of residence information additionally
helps to ensure that each image subject’s data is protected appropriately
(Andrews et al., 2023). Second, the number of (near) nonzero dimensions
depends on the sparsity penalty weight, i.e., $\alpha_{1}$ in Equation 4,
which must be carefully chosen. We erred on the side of caution (lower
penalty) to avoid merging distinct factors, which would have reduced
interpretability. Third, the emergence of a “smiling expression” and “neutral
expression” dimension indicate that annotators did not always follow our
instruction to ignore differences in facial expression. While this may be
considered a limitation, our model was able to reveal the use of these
attributes, providing a level of transparency not offered by existing face
datasets. Finally, our annotator pool was demographically imbalanced, which is
an unfortunate consequence of using AMT.

Despite these limitations, we hope that our work inspires others to pursue
unorthodox tasks for learning the continuous dimensions of human diversity,
which do not rely on problematic semantic labels. Moreover, we aim to increase
discourse on annotator positionality. As the philosopher Thomas Nagel
suggested, it is impossible to take a “view from _nowhere_ ” (Nagel, 1989).
Therefore, we need to make datasets more inclusive by integrating a diverse
set of perspectives from their inception.

#### Ethics Statement

AVFS contains annotations from a demographically imbalanced set of annotators,
which may not generalize to a set of demographically dissimilar annotators.
Moreover, while the stimulus set of images for which the annotations pertain
to were diverse with respect to intersectional demographic subgroup labels,
the labels were inferred by a combination of algorithms and human annotators.
(Refer to the dataset documentation in Appendix C for details on the stimulus
set and annotator distributions.) Therefore, the stimulus set is not
representative of the entire human population and may in actuality be
demographically biased. Although we provided evidence that models trained on
AVFS generalize to novel stimuli, it is conceivable that the models could
perform poorly on faces with attributes that were not salient to human
judgment behavior on triplets sampled from, or represented in, the stimulus
set of images. Future work could explore active learning so as to reveal
additional attributes that are relevant to human judgments of face similarity
and expanding the stimulus set.

Furthermore, the stimulus set (sampled from the permissively licensed FFHQ
dataset) was collected without image subject consent. Reliance on such data is
an overarching problem in the computer vision research community, due to a
lack of consensually collected, unconstrained, and diverse human-centric image
data. To preserve the privacy of the image subjects, all face images presented
in this paper have been replaced by StyleGAN3 generated faces with similar
embedding values.

To demonstrate that AVFS judgments encode discriminative information, we
validated our learned dimensions in terms of their ability to estimate
attribute disparities, as well as to predict binary attributes and concept
prototypicality. Our results show that discriminative attribute classifiers
can be distilled solely from learning to predict similarity judgments,
underscoring that we do not need to explicitly collect face attribute labels
from human annotators. Note however, for social constructs (e.g., gender,
race) and emotions, we do not condone classification, as it reifies the idea
that an individual’s social identity or internal state can be inferred from
images alone.

#### Reproducibility Statement

Data and code are publicly available under a Creative Commons license (CC-BY-
NC-SA), permitting noncommercial use cases at
https://github.com/SonyAI/a_view_from_somewhere.

#### Acknowledgments

This work was funded by Sony AI.

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##  Appendix A Implementation Details

###  A.1 Face Alignment

For each face dataset used in this paper, faces were detected and aligned
following the procedure outlined by Or-El et al. (2020), which utilizes the
dlib face detector and 68 landmark shape predictor (King, 2009).

###  A.2 AVFS Models

Aligned face images were resized to $128\times 128$. For training, standard
data augmentation was used, i.e., horizontal mirroring and ${112\times 112}$
random cropping, and image values were normalized to $[-1,1]$. Training was
performed using a batch size of 128 on a single Tesla T4 GPU.

For the unconditional AVFS models, guided by the validation loss, we
empirically set $\alpha_{1}=\text{0.00005}$ and $\alpha_{2}=\text{0.01}$ using
grid search. The conditional models used the same hyperparameters with the
additional loss term’s weight set to $\alpha_{3}=\text{0.0001}$. All models
were optimized for 40 epochs with Adam (Kingma & Ba, 2014) (default
parameters) and learning rate 0.001.

Post-optimization, for each trained model, we obtained a core set of
dimensions by removing dimensions with a maximal value (over the stimulus set)
close to zero, resulting in a low-dimensional space. For the AVFS-U trained
model, this resulted in 22 dimensions. The threshold for removing dimensions
for all models was determined based on maximizing accuracy on the validation
set. For the AVFS-U model, across five independent runs, the 95% confidence
interval (CI) for the number of dimensions and validation accuracy were
27.4$\pm$5.8 and 61.9$\pm$0.1%, respectively. Figure 7 shows that the
judgments can be encapsulated using a limited number of the available
dimensions and that the 22 dimensions are approximately replicated across the
independent runs.

Note that the AVFS-2AFC model was trained using a triplet margin with a
distance swap loss (Balntas et al., 2016) for 40 epochs with Adam (Kingma &
Ba, 2014) (default parameters) and learning rate 0.001.

a Validation accuracy b Pearson’s _r_ c Spearman’s _r_ Figure 7: AVFS-U model
training across independent runs. (a) Validation accuracy as a function of the
number of dimensions retained over five independent runs
($95\text{\,}\mathrm{\char 37\relax}$ CI), empirically evidencing that the
judgments can be encapsulated using a limited number of dimensions. (b)
Pearson and (c) Spearman’s $r$ between each of the $22$ dimensions and the
best matching dimension from each of the other four runs
($95\text{\,}\mathrm{\char 37\relax}$ CI), empirically evidencing the
reproducibility of the dimensions.

###  A.3 Baseline Models

Aligned face images were resized to $128\times 128$. For training, standard
data augmentation was used, i.e., horizontal mirroring and ${112\times 112}$
random cropping, and image values were normalized to $[-1,1]$. Training was
performed using a batch size of 512 across 4 Tesla T4 GPUs.

FairFace models. We trained face attribute recognition models using SGD for 45
epochs on the FairFace dataset. The initial learning rate $0.1$ was divided by
$10$ at epoch 15, 30, and 40. $L^{2}$ weight decay and SGD momentum were set
to $0.0005$ and $0.9$, respectively. The FairFace models were trained to
predict age, gender, and ethnicity.

CelebA models. We trained face attribute recognition models using SGD for 45
epochs on the CelebA dataset. The initial learning rate $0.1$ was divided by
$10$ at epoch 15, 30, and 40. $L^{2}$ weight decay and SGD momentum were set
to $0.0005$ and $0.9$, respectively. The CelebA models were trained to predict
40 binary attributes.

FFHQ models. As FFHQ is an unlabeled dataset, we used age and gender labels
inferred by human annotators from the FFHQ-Aging dataset. Ethnicity labels
were inferred using the pretrained FairFace model described above. We trained
the FFHQ models to predict the inferred age, gender, and ethnicity labels
using SGD for 45 epochs. The initial learning rate $0.1$ was divided by $10$
at epoch 15, 30, and 40. $L^{2}$ weight decay and SGD momentum were set to
$0.0005$ and $0.9$, respectively.

CASIA-WebFace models. We trained several face recognition models on the CASIA-
WebFace dataset. The models differ only in terms of the loss minimized, i.e.,
softmax, ArcFace (Deng et al., 2019), CosFace (Wang et al., 2018), and
SphereFace (Liu et al., 2017). Models were trained using SGD for 55 epochs.
The initial learning rate $0.1$ was divided by $10$ at epoch 15, 30, and 40.
$L^{2}$ weight decay and SGD momentum were set to $0.0005$ and $0.9$,
respectively.

For the additional baseline models used in Appendix B, we obtained publicly
available model weights for self-supervised and object recognition models.
Aligned faces were preprocessed based on the model used.

Self-supervised models. We used the following pretrained self-supervised
models:

  * •

Self-supervised model trained using the SwAV (Caron et al., 2020) framework on
ImageNet1K (Russakovsky et al., 2015)—an object recognition dataset of 1.3M
images over 1K object classes, where an estimated 17% of the images contain at
least one human face (Yang et al., 2022). Model weights can be found at
https://github.com/facebookresearch/swav.

  * •

Self-supervised models trained using the SwAV framework on IG-1B (Goyal et
al., 2022)—a dataset of 1B uncurated Instagram images, containing millions of
images of humans. Model weights can be found at
https://github.com/facebookresearch/vissl/tree/main/projects/SEER.

  * •

Self-supervised models trained using the SwAV, MoCo-v2 (Chen et al., 2020),
and DINO (Caron et al., 2021) frameworks on PASS (Asano et al., 2021)—a
dataset of 1.3M images without people. Model weights can be found at
https://github.com/yukimasano/PASS.

Object recognition models. We used a object recognition model from PyTorch
pretrained using a softmax loss on ImageNet1K (Russakovsky et al., 2015).

##  Appendix B Extended Experimental Results

For FairFace, CelebA, and FFHQ models, we additionally consider unnormalized
class logit embeddings (e.g., denoted CelebA-L), as well as logit embeddings
converted to binary vectors based on class predictions (e.g., denoted CelebA-
BL). All baseline embeddings are normalized to unit-length, where the dot
product of two embeddings determines their similarity. Results are shown in
Table 1.

Table 1: Predicting similarity. (Same Stimuli) Accuracy over 24,060 judgments and Spearman’s $r$ between the entropy in human- and model-generated triplet odd-one-out probabilities. (Novel Stimuli) Accuracy over 80,300 judgments and Spearman’s $r$ between the entropy in human- and model-generated similarity matrices. Note that #Labels is an approximate of the number of labels collected to create a dataset accounting for consensus. |  |  |  |  | Same Stimuli | Novel Stimuli
---|---|---|---|---|---|---
Data/Model | Loss/Framework | #Images | #Labels | Architecture | Acc. | $r$ | Acc. | $r$
ImageNet1K | Cross-entropy | 1.3M | >1.3M | RegNet-32Gf | 46.6 | 0.09 | 41.7 | 0.32
FairFace | Cross-entropy | 62K | 372K | ResNet18 | 55.9 | 0.41 | 51.9 | 0.67
FairFace-L | Cross-entropy | 62K | 372K | ResNet18 | 52.9 | 0.25 | 48.6 | 0.54
FairFace-BL | Cross-entropy | 62K | 372K | ResNet18 | 51.7 | 0.15 | 47.7 | 0.54
CelebA | Cross-entropy | 178K | 7.1M | ResNet18 | 52.1 | 0.25 | 48.9 | 0.56
CelebA-L | Cross-entropy | 178K | 7.1M | ResNet18 | 53.2 | 0.15 | 49.6 | 0.59
CelebA-BL | Cross-entropy | 178K | 7.1M | ResNet18 | 47.0 | 0.14 | 45.9 | 0.47
FFHQ | Cross-entropy | 70K | 140K | ResNet18 | 50.5 | 0.16 | 47.3 | 0.49
FFHQ-L | Cross-entropy | 70K | 140K | ResNet18 | 53.5 | 0.31 | 50.5 | 0.62
FFHQ-BL | Cross-entropy | 70K | 140K | ResNet18 | 51.0 | 0.23 | 47.3 | 0.54
CASIA-WebFace | ArcFace | 404K | 404K | ResNet18 | 51.6 | 0.29 | 46.1 | 0.40
CASIA-WebFace | Cross-entropy | 404K | 404K | ResNet18 | 48.6 | 0.21 | 43.2 | 0.34
CASIA-WebFace | SphereFace | 404K | 404K | ResNet18 | 48.3 | 0.25 | 43.8 | 0.35
CASIA-WebFace | CosFace | 404K | 404K | ResNet18 | 44.0 | 0.12 | 40.8 | 0.23
AVFS-C | Conditional Equation 4 | 5K | 574K | ResNet18 | 67.4 | 0.68 | 61.7 | 0.82
AVFS-CPH | Conditional Equation 4 | 5K | 574K | ResNet18 | 66.5 | 0.68 | 61.4 | 0.82
AVFS-U | Unconditional Equation 4 | 5K | 574K | ResNet18 | 62.0 | 0.65 | 57.5 | 0.86
AVFS-U-50% | Unconditional Equation 4 | 5K | 287K | ResNet18 | 61.3 | 0.61 | 55.8 | 0.81
AVFS-U-25% | Unconditional Equation 4 | 5K | 144K | ResNet18 | 60.6 | 0.56 | 55.3 | 0.80
AVFS-U-12.5% | Unconditional Equation 4 | 5K | 72K | ResNet18 | 58.6 | 0.47 | 55.0 | 0.79
AVFS-2AFC | Triplet margin with distance swap (Balntas et al., 2016) | 5K | 574K | ResNet18 | 60.2 | 0.46 | 52.8 | 0.64
ImageNet1K | SwAV | 1.3M | 0 | ResNet50-w5 | 43.8 | 0.08 | 41.0 | 0.30
IG-1B | SwAV | 1B | 0 | RegNet-32Gf | 47.2 | 0.18 | 44.7 | 0.45
IG-1B | SwAV | 1B | 0 | RegNet-64Gf | 48.1 | 0.16 | 44.4 | 0.45
IG-1B | SwAV | 1B | 0 | RegNet-128Gf | 46.8 | 0.15 | 42.8 | 0.40
IG-1B | SwAV | 1B | 0 | RegNet-256Gf | 47.8 | 0.17 | 43.1 | 0.41
PASS | MoCo-v2 | 1.3M | 0 | ResNet50 | 42.3 | 0.09 | 40.5 | 0.27
PASS | SwAV | 1.3M | 0 | ResNet50 | 42.4 | 0.12 | 40.4 | 0.27
PASS | DINO | 1.3M | 0 | ViTS-16 | 43.2 | 0.10 | 41.8 | 0.32

##  Appendix C Dataset Documentation

The questions answered in this dataset documentation were proposed by Gebru et
al. (2018).

###  C.1 Motivation

####  C.1.1 For what purpose was the dataset created?

Few datasets contain self-identified sensitive attributes, inferring
attributes risks introducing additional biases, and collecting attributes can
carry legal risks. Besides, categorical labels can fail to reflect the
continuous nature of human phenotypic diversity, making it difficult to
compare the similarity between same-labeled faces. Motivated by these legal,
technical, and ethical considerations, the dataset was created to enable
research on learning human-centric face embeddings aligned with human
perception, as well as studying annotator bias. The dataset does not contain
problematic semantic face attribute labels, rather it consists of face
similarity judgments, where each judgment is accompanied by both the
identifier and demographic attributes of the annotator who made the judgment.

####  C.1.2 Who created the dataset and on behalf of which entity?

The dataset was constructed by the authors Jerone T. A. Andrews, Przemysław
Joniak, and Alice Xiang.

####  C.1.3 Who funded the creation of the dataset?

The dataset was funded by Sony AI.

###  C.2 Composition

####  C.2.1 What do the instances that comprise the dataset represent?

The instances comprising the dataset represent:

  * •

Three-alternative forced choice (3AFC) triplet-judgment tuples, where a
judgment corresponds to the least similar (i.e., odd-one-out) face in a
triplet of face images according to a human annotator. Refer to Figure 8 for
an illustrative example of the 3AFC task presented to annotators.

  * •

Human-generated topic labels that capture the meaning of a set of learned
embedding dimensions. Refer to Figure 9 for an illustrative example of the
dimension labeling task presented to annotators.

  * •

Human-generated image ratings along a set of learned embedding dimensions.
Refer to Figure 10 for an illustrative example of the image rating task
presented to annotators.

All instances in the dataset are associated with the unique identifier and
demographic attributes of the annotator who performed the annotation. The
identifiers were randomly generated (to preserve the privacy of the
annotators) by the authors and the demographic attributes correspond to the
self-reported age, nationality, ancestry, and gender identity of an annotator.

Choose the person that looks least similar to the two other people. Focus on
the people, ignoring differences in head position, facial expression,
lighting, accessories, background, and objects. ContextTripletTripletTriplet
Figure 8: Odd-one-out 3AFC task. Annotators were tasked with choosing the
person that looks least similar to the two other people. Context makes salient
context-related properties and the extent to which faces being compared share
these properties. By exchanging a face in a triplet (i.e., altering the
context), each judgment implicitly encodes the dimensions relevant to pairwise
similarity. All face images in the figure were replaced by synthetic StyleGAN3
(Karras et al., 2021) images for privacy reasons; annotators were shown real
images. Which visual characteristics do you think the people on the left-hand
side of the grid have in common compared to the people on the right-hand side
of the grid? 10099907550251 Figure 9: Dimension labeling task. Illustrative
example of a face image grid for model dimension $j$ shown to annotators,
displaying the stimulus set of 4,921 faces sorted in descending order (from
left to right) based on their dimension $j$ embedding value. Annotators were
tasked with writing 1–3 descriptions based on the characteristics they thought
were changing along the grid. Annotators were instructed to view and interact
with the entire grid before providing their descriptions. Consistent
descriptions from distinct annotators would suggest that the ordering of the
grid is visually meaningful. All face images in the figure were replaced by
synthetic StyleGAN3 images for privacy reasons; annotators were shown real
images. Decide where on the grid you would place this person based on their
similarity to the people shown in each column. 10099907550251 Figure 10: Image
rating task. Illustrative example of a face image grid for model dimension $j$
shown to annotators, displaying the stimulus set of 4,921 faces sorted in
descending order (from left to right) based on their dimension $j$ embedding
value. Annotators were tasked with deciding which column of the grid a novel
face (shown above the grid) should be placed on based on its similarity to the
faces in the column. Annotators were instructed to view and interact with the
entire grid before making their decision. As annotators were not primed with
the meaning of a grid, accurate placement would indicate that the ordering of
the grid is visually meaningful. All face images in the figure were replaced
by synthetic StyleGAN3 images for privacy reasons; annotators were shown real
images.

####  C.2.2 How many instances are there in total?

The dataset contains:

  * •

638,180 quality-controlled 3AFC instances for training and validation; and
$\text{24,060}+\text{80,300}$ quality-controlled 3AFC instances for testing.

  * •

738 quality-controlled human-generated topic labels that capture the meaning
of a set of learned embedding dimensions.

  * •

8,800 quality-controlled human-generated image ratings along a set of learned
embedding dimensions.

####  C.2.3 Does the dataset contain all possible instances or is it a sample
of instances from a larger set?

Face image stimuli. Starting from the 70,000 FFHQ (Karras et al., 2019)
images, the stimulus set of 4,921 face images was constructed by:

  1. 1.

Removing faces with an absolute head yaw or pitch angle ${>\pi/$6$}$, left or
right eye occlusion score ${>$20$}$, or eyewear label. This was done to reduce
the influence of head pose on human behavior and more easily permit eye
comparisons.

  2. 2.

Removing face images with an age label $>\text{19}$ years old, so as to
exclude images of minors.

  3. 3.

Categorizing the remaining faces into 56 intersectional demographic subgroups,
which were defined based on ethnicity, age, and gender labels.

  4. 4.

Randomly sampling face images (without replacement) such that each subgroup
would be represented in the stimulus set. This was done to ensure that the
stimulus set would at least be diverse with respect to demographic subgroup
labels. Moreover, face images were randomly sampled from a subgroup, as
opposed to selecting the most confidently labeled, to avoid biasing the
stimulus set toward stereotypical faces.

Figure 11 shows the intersectional group counts for the 4,921 face images in
the stimulus set.

Figure 11: Stimulus set intersectional subgroup counts. Intersectional
subgroup counts for the 4,921 face images contained in the stimulus set. There
are 56 subgroups represented in the stimulus set.

An additional set of 56 images and set of 20 images were sampled from FFHQ for
the test set partition of 80,300 3AFC instances and 8,800 human-generated
image rating instances, respectively. Both of these sets of images were
sampled in the same manner as the stimulus set. In addition, they are disjoint
from each other and the stimulus set of 4,921 faces. Figures 12 and 13 show
the intersectional group counts for the set of 56 images and set of 20 images,
respectively.

Figure 12: Novel stimuli test set intersectional subgroup counts.
Intersectional subgroup counts for the 56 face images used in the test set
partition of 80,300 quality-controlled 3AFC triplet-judgment tuples. There are
48 (from a possible 56) subgroups represented in the 27,720 unique triplets.
Figure 13: Rated image set intersectional subgroup counts. Intersectional
subgroup counts for the 20 face images used for collecting human-generated
image ratings along the 22 learned embedding dimensions. There are 18 (from a
possible 56) subgroups represented.

Head yaw and pitch angles, eyewear, and eye occlusion labels were obtained
from the FFHQ-Aging (Or-El et al., 2020) dataset, which Or-El et al. inferred
using Face++. Note that FFHQ-Aging comprises of labels for each face image
contained in FFHQ. Age and gender labels were also obtained from the FFHQ-
Aging dataset, which Or-El et al. inferred by employing a group of human
annotators to annotate the face images. Ethnicity was estimated using a
pretrained FairFace model (Appendix A).

3AFC instances. The training and validation set of 638,180 quality-controlled
3AFC triplet-judgment tuples contains 638,180 unique triplets, i.e., a single
judgment per unique triplet. The judgments were obtained from 1,645 eligible
annotators. Figure 14 shows the demographic group counts of the annotators as
well as the distribution of annotator contributions.333Note the abbreviations:
“africa” (“af”), “north africa” (“afna”), “east africa” (“afea”), “middle
africa” (“afma”), “south africa” (“afsa”), “western africa” (“afwa”),
“americas” (“am”), “caribbean” (“amc”),“central america” (“amca”), “south
america” (“amasa”), “northern america” (“amna”), “asia” (“as”), “central asia”
(“asca”), “eastern asia” (“asea”), “south-eastern asia” (“assea”), “southern
asia” (“assa”), “western asia” (“aswa”), “europe” (“eu”), “eastern europe”
(“euea”), “northern europe” (“eune”), “southern europe” (“euse”), “western
europe” (“euwe”), “oceania” (“oc”), “australia and new zealand” (“ocanz”),
“melanesia” (“ocme”), “micronesia” (“ocmi”), and “polynesia” (“ocp”). The
quality-controlled instances represent a subset of instances from a larger set
of 703,300 3AFC instances. The 703,300 triplets were sampled from
$\binom{\text{4,921}}{\text{3}}$ possible triplets (i.e., over 4,921 face
images contained in the stimulus set). Instances were not included in the
subset of 638,180 3AFC instances if they were annotated by any annotator who
provided overly fast, deterministic, or incomplete judgments. Refer to the
annotator exclusion policies outlined in Table 2.

AgeGender identityNationalityAncestrySubregional ancestryDistribution of annotator contributions Figure 14: 3AFC training and validation set annotator demographics. Annotator age, gender identity, nationality, ancestry, and subregional ancestry group counts for the 1,645 eligible annotators who contributed to the training and validation set of 638,180 quality-controlled 3AFC triplet-judgment tuples. The distribution of per annotator contributions is also shown. Note that for nationality, ancestry, and subregional ancestry the plots only show groups with $\geq\text{5}$ annotators. Table 2: Annotator exclusion policies and criteria. 3AFC annotations from annotators who provided overly fast, deterministic, or incomplete judgments were excluded. Policy | Criteria | Min. #judgments
---|---|---
Fast #1 |  $>\text{25\%}$ of judgments generated in $<\text{0.8s}$ | 100
Fast #2 |  $>\text{50\%}$ of judgments generated in $<\text{1.1s}$ | 100
Deterministic |  $>\text{40\%}$ of judgments correspond to a single triplet position | 200
Incomplete | Submission of an empty judgment | $1$

The test set partition of 24,060 quality-controlled 3AFC triplet-judgment
tuples contains 1,000 unique triplets with 22–25 unique judgments per triplet.
The judgments were obtained from 355 eligible annotators. Figure 15 shows the
demographic group counts of the annotators as well as the distribution of
annotator contributions. The quality-controlled instances represent a subset
of instances from a larger set of 25,000 3AFC instances with 25 unique
judgments per triplet. The 1,000 triplets were sampled from
$\binom{\text{4,921}}{\text{3}}$ possible triplets (i.e., over 4,921 face
images contained in the stimulus set). Within the 1,000 triplets, 2,163 face
images from the stimulus set of 4,921 face images were used. The 1,000
triplets are disjoint from the 703,300 triplets sampled for the training and
validation set. Instances were not included in the subset of 24,060 3AFC
instances if they were annotated by any annotator who provided overly fast,
deterministic, or incomplete judgments. Refer to the annotator exclusion
policies outlined in Table 2.

AgeGender identityNationalityAncestrySubregional ancestryDistribution of
annotator contributions Figure 15: 3AFC same stimuli test set annotator
demographics. Annotator age, gender identity, nationality, ancestry, and
subregional ancestry group counts for the 355 eligible annotators who
contributed to the test set partition of 24,060 quality-controlled 3AFC
triplet-judgment tuples. The distribution of per annotator contributions is
also shown. Note that for nationality, ancestry, and subregional ancestry the
plots only show groups with $\geq\text{5}$ annotators.

The test set partition of 80,300 quality-controlled 3AFC triplet-judgment
tuples contains $\binom{\text{56}}{\text{3}}=\text{27,720}$ unique triplets,
representing all possible triplets that could be sampled from 56 images. Each
triplet has 2–3 judgments from 632 eligible annotators. Figure 16 shows the
demographic group counts of the annotators as well as the distribution of
annotator contributions. The quality-controlled instances represent a subset
of instances from a larger set of 83,160 3AFC instances with 3 unique
judgments per triplet. Instances were not included in the subset of 80,300
3AFC instances if they were annotated by any annotator who provided overly
fast, deterministic, or incomplete judgments. Refer to the annotator exclusion
policies outlined in Table 2.

AgeGender identityNationalityAncestrySubregional ancestryDistribution of
annotator contributions Figure 16: 3AFC novel stimuli test set annotator
demographics. Annotator age, gender identity, nationality, ancestry, and
subregional ancestry group counts for the 632 eligible annotators who
contributed to the test set partition of 80,300 quality-controlled 3AFC
triplet-judgment tuples. The distribution of per annotator contributions is
also shown. Note that for nationality, ancestry, and subregional ancestry the
plots only show groups with $\geq\text{5}$ annotators.

Dimension topic label instances. The 738 quality-controlled human-generated
topic labels were obtained for 22 learned embedding dimensions, representing a
subset from 128 dimensions. Figure 17 shows the learned embeddings with the
highest frequency topic labels generated by the annotators. The subset of
dimensions were selected on the basis of their maximal observed value (over
the stimulus set of 4,921 faces) being sufficiently larger than zero. The
topic labels were obtained from 84 eligible annotators. Figure 18 shows the
demographic group counts of the annotators as well as the distribution of
annotator contributions. Each of the 22 dimensions has 23–58 topic labels. The
quality-controlled topic labels were arrived at by first removing annotator
responses that did not correspond to words and those that were derogatory or
offensive. The filtered responses were then converted into 35 topic labels:
“age”, “age related”, “ancestry”, “ear shape”, “emotion expression”, “eye
color”, “eye related”, “eye shape”, “eyebrow related”, “eyebrow shape”, “face
related”, “face shape”, “facial expression”, “facial hair”, “forehead shape”,
“gender expression”, “hair color”, “hair length”, “hair related”, “hair
texture”, “hairstyle”, “head shape”, “health related”, “lips related”, “lips
shape”, “mouth related”, “mouth shape”, “neck related”, “nose related”, “nose
shape”, “skin color”, “skin related”, “skin texture”, “teeth related”, and
“weight related”.

Dim. 1/22: “south asian” “ancestry”, “skin color”, “facial exp.” “indian”,
“bushy eyebrows”, “latino hispanic” a b c Dim. 2/22: “long face” “face shape”,
“skin color”, “nose shape” “pointy nose”, “attractive”, “middle eastern” Dim.
3/22: “white” “skin color”, “hair color”, “age related” “white”, “blond hair”,
“brown hair” Dim. 4/22: “feminine” “gender exp.”, “facial exp.”, “ancestry”
“female”, “wearing lipstick”, “heavy makeup” Dim. 5/22: “smiling exp.” “facial
exp.”, “skin color”, “emotion exp.” “smiling”, “high cheekbones”, “rosy
cheeks” Dim. 6/22: “masculine” “gender exp.”, “skin color”, “ancestry” “male”,
“sideburns”, “goatee” Dim. 7/22: “neutral exp.” “nose related”, “facial exp.”,
“ancestry” “wearing necktie”, “gray hair”, “no beard” a b c Dim. 8/22: “east
asian” “ancestry”, “skin color”, “age related” “east asian”, “southeast
asian”, “straight hair” Dim. 9/22: “black” “ancestry”, “skin color”, “hair
length” “black”, “big lips”, “big nose” Dim. 10/22: ‘wide face” “age related”,
“ancestry”, “hair color” “high cheekbones”, “double chin”, “smiling” Dim.
11/22: “elderly” “face shape”, “weight related”, “facial exp.” “70+”, “60–69”,
“gray hair” Dim. 12/22: “east asian” “skin color”, “ancestry”, “facial exp.”
“east asian”, “southeast asian”, “straight hair” Dim. 13/22: “feminine”
“gender exp.”, “hair color”, “facial exp.” “female”, “wearing lipstick”,
“heavy makeup” a b c Dim. 14/22: “balding” “gender exp.”, “facial hair”, “hair
length” “bald”, “gray hair”, “wearing necktie” Dim. 15/22: “young” “age
related”, “ancestry”, “gender exp.” “blond hair”, “0–2”, “pale skin” Dim.
16/22: “black” “skin color”, “ancestry”, “nose shape” “black”, “big lips”,
“big nose” Dim. 17/22: “facial hair” “gender exp.”, “facial exp.”, “facial
hair” “sideburns”, “goatee”, “middle eastern” Dim. 18/22: “balding” “hair
length”, “gender exp.”, “ancestry” “bald”, “gray hair”, “wearing necktie” Dim.
19/22: “east asian” “ancestry”, “skin color”, “facial exp.” “east asian”,
“southeast asian”, “pale skin” a b c Dim. 20/22: “elderly” “age related”,
“ancestry”, “gender exp.” “70+”, “60–69”, “gray hair” Dim. 21/22: “wide face”
“face shape”, “ancestry”, “skin color” “double chin”, “chubby”, “high
cheekbones” Dim. 22/22: “white” “skin color”, “ancestry”, “hair color”
“white”, “blond hair”, “brown hair” Figure 17: Dimensional interpretability.
(a) Our interpretation of an AVFS-U dimension with the highest scoring
stimulus set images. (b) Highest frequency human-generated topic labels. (c)
CelebA and FairFace model-generated labels, exhibiting a clear correspondence
with human-generated topics. Face images in this figure have been replaced by
synthetic StyleGAN3 images for privacy reasons. AgeGender
identityNationalityAncestrySubregional ancestryDistribution of annotator
contributions Figure 18: Topic labeling annotator demographics. Annotator age,
gender identity, nationality, ancestry, and subregional ancestry group counts
for the 84 eligible annotators who contributed 738 human-generated topic
labels that capture the meaning of the 22 learned embedding dimensions. The
distribution of per annotator contributions is also shown. Note that for
nationality, ancestry, and subregional ancestry the plots only show groups
with $\geq\text{5}$ annotators.

Dimension rating labels. The 8,800 quality-controlled human-generated image
ratings were obtained for 22 learned embedding dimensions, representing a
subset from 128 dimensions. The subset of dimensions were selected on the
basis of their maximal observed value (over the stimulus set of 4,921 faces)
being sufficiently larger than zero. The ratings were obtained from 164
eligible annotators. Figure 19 shows the demographic group counts of the
annotators as well as the distribution of annotator contributions. Quality
control corresponded to excluding incomplete responses, i.e., empty responses.
The 8,800 ratings represent all of the ratings collected from annotators,
i.e., all collected ratings were nonempty.

AgeGender identityNationalityAncestrySubregional ancestryDistribution of
annotator contributions Figure 19: Image rating annotator demographics.
Annotator age, gender identity, nationality, ancestry, and subregional
ancestry group counts for the 164 eligible annotators who contributed 8,800
human-generated image ratings along the 22 learned embedding dimensions. The
distribution of per annotator contributions is also shown. Note that for
nationality, ancestry, and subregional ancestry the plots only show groups
with $\geq\text{5}$ annotators.

####  C.2.4 What data does each instance consist of?

  * •

Each 3AFC instance consists of a triplet of face image identifiers, 3AFC
(i.e., odd-one-out) judgment, and annotator identifier (corresponding to the
annotator who generated the topic label).

  * •

Each human-generated topic label instance consists of a dimension identifier,
topic label, and annotator identifier (corresponding to the annotator who
generated the topic label).

  * •

Each human-generated image rating instance consists of a dimension identifier,
image identifier, image rating, and annotator identifier (corresponding to the
annotator who generated the topic label).

Note that each annotator’s identifier is associated with their demographic
attributes (i.e., age, gender identity, nationality, and ancestry).

####  C.2.5 Is there a label or target associated with each instance?

  * •

Each 3AFC instance’s target is a 3AFC judgment.

  * •

Each human-generated topic label instance’s target is a topic label.

  * •

Each human-generated image rating instance’s target is an image rating.

####  C.2.6 Is any information missing from individual instances?

No.

####  C.2.7 Are relationships between individual instances made explicit?

Yes. Each instance can be mapped to the annotator who labeled it.

####  C.2.8 Are there recommended data splits?

The 3AFC instances are split into training, validation, and testing
partitions. All other instances are for evaluating learned model dimensions,
i.e., for the model introduced in the paper.

####  C.2.9 Are there any errors, sources of noise, or redundancies in the
dataset?

No.

####  C.2.10 Is the dataset self-contained, or does it link to or otherwise
rely on external resources?

The dataset does not include any images, it instead references to image
identifiers. The image identifiers can be used to obtain the relevant FFHQ
images, which are hosted on NVIDIA Corporation’s Google Drive:
https://github.com/NVlabs/ffhq-dataset.

####  C.2.11 Does the dataset contain data that might be considered
confidential?

The dataset includes the demographic information (i.e., age, nationality,
ancestry, and gender identity) of each annotator; however, all annotators
consented to the collection, use, and publication of their data.

####  C.2.12 Does the dataset contain data that, if viewed directly, might be
offensive, insulting, threatening, or might otherwise cause anxiety?

No.

####  C.2.13 Does the dataset relate to people?

The dataset contains annotations corresponding to human judgments of face
similarity, as well as demographic information about each annotator.

####  C.2.14 Does the dataset identify any subpopulations?

The dataset includes the demographic information (i.e., age, nationality,
ancestry, and gender identity) of each annotator.

####  C.2.15 Is it possible to identify individuals, either directly or
indirectly from the dataset?

Each annotator’s Amazon Mechnical Turk (AMT) identifier was replaced by an
identifier randomly generated by the authors. The dataset therefore contains
de-identified data.

####  C.2.16 Does the dataset contain data that might be considered sensitive
in any way?

The dataset includes the demographic information (i.e., age, nationality,
ancestry, and gender identity) of each annotator.

###  C.3 Collection process

####  C.3.1 How was the data associated with each instance acquired?

All data associated with each instance was acquired via AMT, except for the
image identifiers which were obtained from the FFHQ dataset:
https://github.com/NVlabs/ffhq-dataset.

####  C.3.2 What mechanisms or procedures were used to collect the data?

The AMT API was used to collect the data associated with each instance.

####  C.3.3 If the dataset is a sample from a larger set, what was the
sampling strategy?

Refer to Section C.2.3.

####  C.3.4 Who was involved in the data collection process and how were they
compensated?

The authors collected the annotations directly from annotators via AMT. All
annotators were required to have previously completed $\geq\text{100}$ human
intelligence tasks (HITs) on AMT with $\geq\text{95\%}$ approval rating.
Eligibility was further determined through a prescreening survey, which
included a short multiple-choice English language proficiency test. Since we
placed no restriction on annotator location, in order to be eligible, we
required annotators to answer at least two out of three multiple-choice
English language proficiency questions correctly.

Annotators were paid a nominal fee of 0.01 USD for completing a prescreening
survey. For all subsequent tasks, annotators were compensated at a rate of 15
USD per hour, regardless of whether their work passed quality control checks.

####  C.3.5 Over what timeframe was the data collected?

From 6 November 2021 to 20 February 2022.

####  C.3.6 Were any ethical review processes conducted?

No.

####  C.3.7 Did you collect the data from the individuals in question
directly, or obtain it via third parties or other sources?

All data was collected directly from the annotators via AMT.

####  C.3.8 Were the individuals in question notified about the data
collection?

Yes. Annotators were provided with (1) an information sheet and consent form;
and (2) a notification and consent for collection and use of study data form.

####  C.3.9 Did the individuals in question consent to the collection and use
of their data?

Annotators consented to the collection, use, and publication of their age,
gender identity, nationality, ancestry, and task responses.

####  C.3.10 If consent was obtained, were the consenting individuals provided
with a mechanism to revoke their consent in the future or for certain uses?

Annotators were instructed to contact Sony Europe B.V. at Taurusavenue 16,
2132LS Hoofddorp, Netherlands or<EMAIL_ADDRESS>to revoke their
consent in the future or for certain uses.

####  C.3.11 Has an analysis of the potential impact of the dataset and its
use on data subjects been conducted?

A data privacy impact assessment was conducted.

###  C.4 Preprocessing/cleaning/labeling

####  C.4.1 Was any preprocessing/cleaning/labeling of the data done?

To control for quality, any annotator who provided overly fast, deterministic,
or incomplete responses were not included in the dataset. In addition, labels
for model dimensions collected from human annotators were cleaned and
converted to topic labels.

####  C.4.2 Was the “raw” data saved in addition to the
preprocessed/cleaned/labeled data?

The authors retain a copy of the raw data.

####  C.4.3 Is the software that was used to preprocess/clean/label the data
available?

No.

###  C.5 Uses

####  C.5.1 Has the dataset been used for any tasks already?

In the paper, the dataset was used to concurrently learn a face embedding
space aligned with human perception as well as annotator-specific subspaces.
The combination of the learned embedding space and annotator-specific
subspaces not only enabled the accurate prediction of face similarity, but
also provided a human-interpretable decomposition of the dimensions used in
the human decision-making process, as well as the importance distinct
annotators placed on each dimension. The paper demonstrated that the
individual embedding dimensions (1) are related to concepts of gender,
ethnicity, age, as well as face and hair morphology; and (2) can be used to
collect continuous attributes, perform classification, and compare dataset
attribute disparities. The paper further showed that annotators are influenced
by their sociocultural backgrounds, underscoring the need for diverse
annotator groups to mitigate bias.

####  C.5.2 Is there a repository that links to any or all papers or systems
that use the dataset?

Refer to https://github.com/SonyAI/a_view_from_somewhere.

####  C.5.3 What (other) tasks could the dataset be used for?

The dataset could be used for any task related to human perception, human
decision-making processes, metric learning, active learning, bias mitigation,
or disentangled representations.

####  C.5.4 Is there anything about the composition of the dataset or the way
it was collected and preprocessed/cleaned/labeled that might impact future
uses?

The dataset contains annotations for images contained in the FFHQ dataset of
Flickr images. The images currently have permissive licenses that allow free
use, redistribution and adaptation for noncommercial purposes, however Flickr
users are free to request the removal of any of their images contained in the
FFHQ dataset by visiting https://github.com/NVlabs/ffhq-dataset.

Moreover, the dataset contains annotations from a demographically imbalanced
set of annotators, which may not generalize to a set of demographically
dissimilar annotators. Furthermore, while the stimulus set of images for which
the annotations pertain to were diverse with respect to intersectional
demographic subgroup labels, the labels were inferred by a combination of
algorithms and human annotators. Therefore, the stimulus set is not
representative of the entire human population and may in actuality be
demographically biased.

####  C.5.5 Are there tasks for which the dataset should not be used?

The dataset was created for noncommercial research purposes only. The dataset
is not intended for, and should not be used for, development or improvement of
facial recognition technologies.

###  C.6 Distribution

####  C.6.1 Will the dataset be distributed to third parties outside of the
entity on behalf of which the dataset was created?

The dataset can be accessed by visiting
https://github.com/SonyAI/a_view_from_somewhere.

####  C.6.2 How will the dataset be distributed?

The dataset can be accessed by visiting
https://github.com/SonyAI/a_view_from_somewhere.

####  C.6.3 When will the dataset be distributed?

May 2023.

####  C.6.4 Will the dataset be distributed under a copyright or other
intellectual property (IP) license, and/or under applicable terms of use
(ToU)?

The dataset is distributed under a Creative Commons BY-NC-SA license:
https://creativecommons.org/licenses/by-nc-sa/4.0/.

####  C.6.5 Have any third parties imposed IP-based or other restrictions on
the data associated with the instances?

No.

####  C.6.6 Do any export controls or other regulatory restrictions apply to
the dataset or to individual instances?

The authors are not aware of any controls or restrictions.

###  C.7 Maintenance

####  C.7.1 Who will be supporting/hosting/maintaining the dataset?

The dataset is supported by the authors and can be accessed by visiting
https://github.com/SonyAI/a_view_from_somewhere.

####  C.7.2 How can the curator of the dataset be contacted (e.g., email
address)?

Correspondence to<EMAIL_ADDRESS>
####  C.7.3 Is there an erratum?

If errors are found, an erratum file will be added to the dataset.

####  C.7.4 Will the dataset be updated?

The dataset will be versioned.

####  C.7.5 If the dataset relates to people, are there applicable limits on
the retention of the data associated with the instances?

No. However, annotators are free to withdraw their consent at any time and for
any reason.

####  C.7.6 Will older versions of the dataset continue to be
supported/hosted/maintained?

Older versions of the dataset will continue to be hosted, unless they contain
information about, or obtained from, an annotator who has subsequently
withdrawn their consent.

####  C.7.7 If others want to extend/augment/build on/contribute to the
dataset, is there a mechanism for them to do so?

Others should contact the authors if they wish to contribute to the dataset.