If Mexican restaurants tend to get lower user ratings, perhaps it’s not because people/society are biased against Mexican restaurants. There are so many other possible reasons, I can’t begin to speculate.
Go further and follow the links. This example is specifically covered in the linked material. "Mexican" picks up a negative association from the corpus containing frequent mention of "illegal" (listed as a negative term) in close proximity to "Mexican", so the phrase "Mexican restaurant" gets rated less favorably than "Chinese restaurant".
The underlying problem is that we're throwing text at math and pretending that we're building things that understand anything more than word proximity. Human beings that can actually understand context can be horribly biased; software that doesn't have the slightest inkling of context will produce twisted versions of our own biases.
As one non-racial example there was a pneumonia detection model that got some pretty (at least amongst people interested in radiology and deep learning) where the model was detecting the metal badge the hospital was using when taking scans (so every xray from this hospital had a similar graphic on it). This hospital had a higher pneumonia rate as it generally had older people going to it, and because of this the model started giving everyone who went to that hospital a higher likelihood of having pneumonia. So essentially it picked up on the age based bias of the hospital itself.
This is an issue in other types of medical data as well. There have been multiple studies that show doctors are less likely to successfully diagnose a heart attack in women than in men, and that doctors will also underestimate how much pain a black man is in compared to any other category. Those biases end up in the data itself, so if you naively train your models against that data they too will make similar mistakes.
Socialization is an ambiguous, imprecise activity that demands infinitely many judgements, inferences, and preconceived rules & goals.
What can be interpreted as "jerk" behavior can also be caused by "dork" behavior, and vice-versa.
A joke at someone's expense might be expressing affinity for them or dissing them.
Adopting ebonics speech and tone in one context maybe in-group acceptable in one context, but insulting in another.
Ultimately, the algorithms need enough trial-and-error socializing experience to become, for lack of a better term, cool.
To be fair to the IAT, while it has very low test-retest reliability and lots and lots of state variance, there's definitely something in the reaction time differences that is meaningful. How meaningful it is, and whether or not it makes any practical difference remains to be determined.
Anyone who hasn't done one, try this link and see how you feel during the process: https://implicit.harvard.edu/implicit/takeatest.html
And at least there wasn't a massive profit motive behind the IAT, at the beginning anyway :)
Insurance companies do this sort of thing all the time.
So it appears that our evolution as social species has set some hard boundaries. Abstract ideologies can go beyond them, of course, but their real-world success seems to correlate to some extent with how much they do or not - I would argue that ancap definition of "fair" is so unpopular precisely because it's so out-of-bounds wrt our biology.
And, in practice, there are large groups of people who do share the broad definitions of "fairness", and therefore saying that something is fair or unfair is useful to communicate the idea within those groups. This is not something that people really like to see put quite so explicitly, but when we say something "this should not be so because it is unfair", it carries an implicit "... and I don't care what those who disagree with me about what 'unfair' means think".
If Mexican restaurants are cited more for having ill patrons, it might simply be due to the cuisine using preparation methods that lend themselves more easily to distributing contamination than many other cuisines. No bias necessary.
We can't just conclude that x is true because it seems more fair that it would be true. The universe isn't necessarily fair and equal.
We can lie to ourselves and our machine learning models about things like: "there must be an equal number of bad restaurants within each genre of restaurant." But that won't make it true. It will just make our models work less well.