Human experiences are complex and subjective. This subjectivity is reflected in the way people label images for machine vision models. While annotation tasks are often assumed to deliver objective results, this assumption does not allow for the subjectivity of human experience. This paper examines the implications of subjective human judgments in the behavioral task of labeling images used to train machine vision models. We identify three primary sources of ambiguity: (1) depictions of labels in the images can be simply ambiguous, (2) raters’ backgrounds and experiences can influence their judgments and (3) the way the labeling task is defined can also influence raters’ judgments. By taking steps to address these sources of ambiguity, we can create more robust and reliable machine vision models.