This is well-stated. As an addendum, if you were to use something like the RevAnimated model (a fairly popular one), it would bias toward digital images rather than photos for essentially the same reason. If you used one of the Anything models, it would generate anime-like images for the same reason. It is pushing toward (one of) the default “understandings” it has of whatever the prompt is. The main issue in the article is the student had both a.) unrealistic expectations about what the AI would do — without being trained on her and her specifically, it wouldn’t know what parameters to hold. It’s not a magic box where you can put in an unknown image and simple manipulate it, at least not in the way one might expect from Photoshop and b) a useless prompt, reflecting a more magical understanding of what the AI would do. It gave her exactly what she asked for, which was (the model’s interpretation) of a person’s linkedin photo using the base image as its target.

That said there’s an exceptionally valid question that’s being sort of asked around and is implied by your post which is what does the model understand the “default” to be. Obviously this aspect isn’t demographically balanced in any way, which is the main problem. That problem, however, isn’t really demonstrated by this example in the way people are thinking. I’d frame the issue as — if she’d asked for a professional LinkedIn photo, she would definitely have gotten a photo of a woman approximately that age and that ethnicity and whatever it thinks a professional linkedin photo is, but it probably still wouldn’t have really looked like her (because it doesn’t know who, in terms of weights, she is); the problem is that without prompting it does default to its default weights of assuming that a generic female is probably white. But a solution would either look like it choosing a totally random nationality every time (which is not what she wanted) or some other specific nationality every time (as some of the more Asian-biased models do) due to her poor prompting.