Few datasets contain self-identified demographic information, inferring demographic information risks introducing additional biases, and collecting and storing data on sensitive attributes can carry legal risks. Besides, categorical demographic labels do not necessarily capture all the relevant dimensions of human diversity. We propose to implicitly learn a set of continuous face-varying dimensions, without ever asking an annotator to explicitly categorize a person. We uncover the dimensions by learning on A View From Somewhere (AVFS) dataset of 638,180 human judgments of face similarity. We demonstrate the utility of our learned embedding space for predicting face similarity judgments, collecting continuous face attribute values, attribute classification, and comparative dataset diversity auditing. Moreover, using a novel conditional framework, we show that an annotator's demographics influences the \emph{importance} they place on different attributes when judging similarity, underscoring the \emph{need} for diverse annotator groups to avoid biases. Data and code are available at \url{https://github.com/SonyAI/aviewfrom_somewhere}.