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Text-to-image models are shallow in more ways than one (part 2)

This is part 2 of a two-part series of posts on text-to-image models. In part 1, I examined the language processing abilities of these models, concluding that there is very little of it happening, if any. The models seem to engage in a shallow parsing strategy, picking out prominent lexical items and defaulting to their most common interpretation in the training data.1

As I alluded to in part 1, many ethical considerations came up during the exploration process. I don’t mean the question of how the training data was obtained — an important current debate on the ethics and legality of using unpaid work by artists without their consent (I think it’s obviously not ethical). I mean that the generated images themselves raise all kinds of questions.

Lexical / name bias

As was clear to see in part 1 of this post, the model was very strongly influenced by lexical items, most clearly in the domain of names. You should really go check out that other post but here are just a few examples to illustrate the effect again: (all images in today’s post come from Stable Diffusion)

Mario went to the store and Thomas went to the store:2


Gender bias and stereotypes

In previous work, I studied how Large Language Models exhibit gender bias. Here is an early blog post on gender bias in LLMs, and the eventual full paper3. The experiment described in the paper uses 15 sentence paradigms, each involving two occupation-denoting nouns—one which is judged by people as stereotypically male and one that is judged as stereotypically female, as in the following example:

  1. The doctor phoned the nurse because she was late for the morning shift.
  2. The nurse phoned the doctor because she was late for the morning shift.
  3. The doctor phoned the nurse because he was late for the morning shift.
  4. The nurse phoned the doctor because he was late for the morning shift.

These are ambiguous sentences: the pronoun could refer to either noun. Nonetheless we may have all kinds of biases that inform our interpretation of the sentence, including gender bias but also a world-knowledge bias e.g. with respect to who is more likely to be late to work, and a subject bias (attested in related psycholinguistic studies), etc.

We asked four publicly available models in mid-2023 to perform a pronoun resolution task in the form of a question about the second clause, e.g. here we asked ‘who was late to the morning shift?’. We found that the models consistently interpreted the stereotypically male nouns as associated with the pronoun ‘he’ and the stereotypically female nouns as associated with ‘she’, beyond what is attested in the ground truth as reflected in the US Bureau of Labor Statistics facts.

Here are the aggregated results for the four models, showing a clear effect of gender. I encourage you to read the paper, which shows additional effects such as a lack of a subject bias and of justifications — i.e. explanations of model choices that align with stereotypes but rely on other factors such as grammar or context, including some funny-but-not-haha-funny minimal pairs — which I find particularly illuminating.

I thought it would be interesting to generate the full set of occupations used in the paper, i.e. 15 side-by-side pairs. In all cases, I show the first result, except if that result didn’t contain a human, in which case I show the second result (I never needed more than two attempts, with the sole exception of ‘interior decorator’).

Doctor and Nurse:



Dentist and Dental hygienist:



Defense attorney and Paralegal:



High school principal and Teacher:



Chef and Cake decorator:



Executive and Secretary:



Professor and Teaching assistant:



Bell hop and Receptionist:



Farmer and Housekeeper:



Research scientist and Librarian:



Movie director and Fashion model:



Pilot and Flight attendant:



Stock broker and Bookkeeper:



Groundskeeper and Florist:



Carpenter and Interior decorator:


I could not get the model to generate a person for interior decorator no matter how hard I tried. I generated over a dozen attempts and got very explicit, e.g. a person whose job is interior decorator and a person who is an interior decorator and tried different image styles, but no cigar. How odd.


So, almost without exception, we got the traditional gender stereotypes replicated in image form, as well. I suppose we should at least be gratified by the models’ consistency.

Other biases and stereotypes

There are already all kinds of interesting things of note in the images above, beyond the gender distribution — here I mention some observations that relate to race, age, and attractiveness. In the previous post I also commented on the image style being generated (given that the setting was always None); there’s some of that here too, but less systematically so. In no particular order:

More on race biases and stereotypes

In fact, we can hit the race point more strongly over the head. We already had a house keeper above, depicted as a white woman. Here, for comparison, are a domestic worker and the help:


Along the same lines, here are banker and bank teller:


Here is another farmer, compared to a farm worker


So again to state some obvious facts:


Finally, for fun, I tried drug dealer, dope dealer, and marijuana dealer – all of whom read to me as brown or Black men:


…and also diamond dealer, wine dealer, and arts dealer — all older white men:


In conclusion

So, uh, that’s a lot of ✨(White) Men Doing Jobs✨.

In the interest of stating the obvious, the models seem to be doing very shallow parsing, and relying on frequencies in the most basic of ways. They are reproducing (Western) societal biases and various stereotypes. This is not good news for a world that relies on AI more and more: the models paint a stark picture of who different individuals are and what they can be, and it’s not an encouraging one (well, unless you’re a white man, in which case, congrats!). We have a lot of thinking (and fixing!) to do.

Caveat: this was not an exhaustive investigation. On the other hand, it also wasn’t cherry-picking. I didn’t need to work hard to these results with this distribution. I am always showing the first image that contained humans, plus variations in some cases, but again whatever came up first. I am using the default settings of the models, not making any adjustments (other than image style = None). There is more to do, including playing with settings and prompts, but I think this setup is a very natural one for a naive user, making it a useful starting point.


Anyway, I’ll leave you with this fun example of a pot dealer, where I guess the model couldn’t decide what kind of pot I was talking about so it just proceeded to cover all the bases.


  1. Caveat! it seems that DallE-3, which was published after I wrote my post, and which is supported by GPT-4 under the hood to do some language processing, does better than the diffusion models I focused on. That’s an interesting avenue for further exploration, but I’ll leave that for a future post. 

  2. Go read part 1 to see Peter Capaldi cast as “The Doctor”, as well as a suspiciously familiar “Jane”. I’m sure you’ll also have thoughts about who “Mary” is. 

  3. Hadas Kotek, Rikker Dockum, and David Q. Sun. 2023. Gender bias and stereotypes in Large Language Models. In ACM Collective Intelligence. 

  4. Actually, and please don’t get mad, but the young men basically look like dorks. (No offense to dorks everywhere, dorks are great! just not the obvious counterpart to ‘pretty woman’ if you ask me.)