  Nine language-vision AI models trained on web scrapes with the Contrastive
Language-Image Pretraining (CLIP) objective are evaluated for evidence of a
bias studied by psychologists: the sexual objectification of girls and women,
which occurs when a person's human characteristics, such as emotions, are
disregarded and the person is treated as a body. We replicate three experiments
in psychology quantifying sexual objectification and show that the phenomena
persist in AI. A first experiment uses standardized images of women from the
Sexual OBjectification and EMotion Database, and finds that human
characteristics are disassociated from images of objectified women: the model's
recognition of emotional state is mediated by whether the subject is fully or
partially clothed. Embedding association tests (EATs) return significant effect
sizes for both anger (d >0.80) and sadness (d >0.50), associating images of
fully clothed subjects with emotions. GRAD-CAM saliency maps highlight that
CLIP gets distracted from emotional expressions in objectified images. A second
experiment measures the effect in a representative application: an automatic
image captioner (Antarctic Captions) includes words denoting emotion less than
50% as often for images of partially clothed women than for images of fully
clothed women. A third experiment finds that images of female professionals
(scientists, doctors, executives) are likely to be associated with sexual
descriptions relative to images of male professionals. A fourth experiment
shows that a prompt of "a [age] year old girl" generates sexualized images (as
determined by an NSFW classifier) up to 73% of the time for VQGAN-CLIP and
Stable Diffusion; the corresponding rate for boys never surpasses 9%. The
evidence indicates that language-vision AI models trained on web scrapes learn
biases of sexual objectification, which propagate to downstream applications.
