  Images are often termed as representations of perceived reality. As such,
racial and gender biases in popular media imagery could play a vital role in
shaping people's perceptions of society. While inquiries into such biases have
examined the frequency at which different racial and gender groups appear in
different forms of media, the literature still lacks a large-scale longitudinal
study that further examines the manner in which these groups are portrayed. To
fill this gap, we examine three media forms, namely fashion magazines, movie
posters, and advertisements. To do so, we collect a large dataset comprising
over 300,000 images spanning over five decades and utilize state-of-the-art
machine learning models to not only classify race and gender but also identify
the posture, emotional state, and body composition of the person featured in
each image. We find that racial minorities appear far less frequently than
their White counterparts, and when they do appear, they are portrayed less
prominently and tend to convey more negative emotions. We also find that women
are more likely to be portrayed with their full bodies in images, whereas men
are more frequently presented with their faces. This disparity exemplifies
face-ism, where emphasizing faces over bodies has been linked to perceptions of
higher competence and intelligence. Finally, through a series of survey
experiments, we show that exposure to inclusive content-rather than racially
and gender-homogenized content -- significantly reduces perception biases
towards minorities in areas such as household income, hiring merit, beauty
standards, leadership positions, and the representation of women in the
workplace. Taken together, our findings demonstrate that racial and gender
biases in media continue to be an ongoing problem that may exacerbate existing
stereotypes.
