  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. 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.
