  As text-to-image systems continue to grow in popularity with the general
public, questions have arisen about bias and diversity in the generated images.
Here, we investigate properties of images generated in response to prompts
which are visually under-specified, but contain salient social attributes
(e.g., 'a portrait of a threatening person' versus 'a portrait of a friendly
person'). Grounding our work in social cognition theory, we find that in many
cases, images contain similar demographic biases to those reported in the
stereotype literature. However, trends are inconsistent across different models
and further investigation is warranted.
