this post was submitted on 01 Aug 2023
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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.
You put it really succinctly.
These models are trained on images generally depicting a lot of concepts. If I do a simple prompt like
monkeyI'm probably going to get a lot of images of monkeys in a natural setting, because when trained it's given images essentially stating that this is an image of "monkey, tree, fruit, foliage, green" and so forth. Over time this pattern repeats, and it will start associating one concept with another. Because a lot of pictures that depictmonkeyalso depictfoliage, natureand what have you, it makes sense for it to fill in the rest ofmonkeywithfoliage, green, natureand lessvolcano, playful dog, ice creamsince those concepts haven't been very prominent when presented withmonkey.That is essentially the problem.
Here is the result for "monkey"
And here is
monkey, volcano, playful dog, ice creamThe datasets are permeated with these kinds of examples. If you prompt for
nurseyou'll get a lot of women in blue/hospitaly clothing, inside hospitals or non-descript backgrounds, and very few men.Here's
photo of a nurseThe more verbose and specific you get though, the likelier it is that you'll get the outcome you want. Here for example is
male (nurse:1) (wearing white scrubs:1) with pink hair skydivingThis was so outlandish that without the
(wearing white scrubs:1)it just put the skydiver in a random pink outfit, even with the added weight onwearing white scrubsit has a tendency to put the subject in something pink. Without the extra weight on(nurse:1)it gave me generic pink (mostly) white men.If we were to fix the biases present in our society, we'd possibly see less biases in the datasets, and subsequently less bias in the final models. The issue I believe, isn't so much a technological one, as it is a societal one. The machines are only racist because we are teaching them to be, by example.
More to the point, there are so many parameters that can be tweaked, to throw your image into a “generator” without knowing what controls you have, what the prompt is doing, what model it’s using etc is like saying “the internet” is toxic because you saw a webpage that had a bad word on it somewhere.
I put her actual photo into SD 1.5 (the same model you used) with 30 step Euler, 5.6 cfg and 0.7 noise and got these back. I’d say 3/4 of them are Asian (and the model had a 70% chance to influence that away if it were truly “biased” in the way the article implies), obviously none of them look like the original lady because that’s not how it works. You could generate a literally infinite number of similar-looking women who won’t look like this lady with this technique.
The issue isn’t so much that the models are biased — that is both tautologically obvious and as mentioned previously, probably preferred (rather than just randomly choosing anything not specified at all times — for instance, your monkey prompt didn’t ask for forest, so should it have generated a pool hall or plateau just to fill something in? The amount of specificity anyone would need would be way too high; people might be generated without two eyes because it wasn’t asked for, for instance); it is that the models don’t reflect the biases of all users. It’s not so much that it made bad choices but that it made choices that the user wouldn’t have made. When the user thinks “person”, she thinks “Asian person” because this user lives in Asia and that’s what her biases toward “person” start with, so seeing a model biased toward people from the Indian subcontinent doesn’t meet her biases. On top of that, there’s a general potential impossibility of having some sort of generic “all people” model given that all people are likely to interpret its biases differently.
With a much lower denoising value I was able to get basically her but airbrushed. It does need a higher denoising value in order to achieve any sort of "creativity" with the image though, so at least with the tools and "skill" I have with said tools, there's a fair bit of manual editing needed in order to get a "professional linkedin" photo.