  Generative artificial intelligence models show an amazing performance
creating unique content automatically just by being given a prompt by the user,
which is revolutionizing several fields such as marketing and design. Not only
are there models whose generated output belongs to the text format but we also
find models that are able to automatically generate high quality genuine images
and videos given a prompt. Although the performance in image creation seems
impressive, it is necessary to slowly assess the content that these models are
generating, as the users are uploading massively this material on the internet.
Critically, it is important to remark that generative AI are statistical models
whose parameter values are estimated given algorithms that maximize the
likelihood of the parameters given an image dataset. Consequently, if the image
dataset is biased towards certain values for vulnerable variables such as
gender or skin color, we might find that the generated content of these models
can be harmful for certain groups of people. By generating this content and
being uploaded into the internet by users, these biases are perpetuating
harmful stereotypes for vulnerable groups, polarizing social vision about, for
example, what beauty or disability is and means. In this work, we analyze in
detail how the generated content by these models can be strongly biased with
respect to a plethora of variables, which we organize into a new image
generative AI taxonomy. We also discuss the social, political and economical
implications of these biases and possible ways to mitigate them.
