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.

本文介绍 A View From Somewhere (AVFS) 数据集，其中包含了 638,180 个关于面部相似度的人类判断，用于学习与人类感知对齐的低维连续嵌入空间。该空间不仅可以准确预测面部相似性，还提供了人类决策过程中使用的维度的可解析分解和不同注释者对各维度的重要性。我们还展示了该维度在收集连续属性、执行分类和比较数据集属性差异方面的实用性。