  Three state-of-the-art language-and-image AI models, CLIP, SLIP, and BLIP,
are evaluated for evidence of a bias previously observed in social and
experimental psychology: equating American identity with being White. Embedding
association tests (EATs) using standardized images of self-identified Asian,
Black, Latina/o, and White individuals from the Chicago Face Database (CFD)
reveal that White individuals are more associated with collective in-group
words than are Asian, Black, or Latina/o individuals. In assessments of three
core aspects of American identity reported by social psychologists,
single-category EATs reveal that images of White individuals are more
associated with patriotism and with being born in America, but that, consistent
with prior findings in psychology, White individuals are associated with being
less likely to treat people of all races and backgrounds equally. Three
downstream machine learning tasks demonstrate biases associating American with
White. In a visual question answering task using BLIP, 97% of White individuals
are identified as American, compared to only 3% of Asian individuals. When
asked in what state the individual depicted lives in, the model responds China
53% of the time for Asian individuals, but always with an American state for
White individuals. In an image captioning task, BLIP remarks upon the race of
Asian individuals as much as 36% of the time, but never remarks upon race for
White individuals. Finally, provided with an initialization image from the CFD
and the text "an American person," a synthetic image generator (VQGAN) using
the text-based guidance of CLIP lightens the skin tone of individuals of all
races (by 35% for Black individuals, based on pixel brightness). The results
indicate that biases equating American identity with being White are learned by
language-and-image AI, and propagate to downstream applications of such models.
