No, because that occurred due to an oversight of not including a representative set of training data. At worst it's implicit racism due to oversight and ignorance.
What Google seemed to do here was more explicit racism, by setting configuration in their system that directly and intentionally discriminates based on race. It's fundamentally worse.
Nobody who worked on facial recognition that failed to recognize black people, nobody in that team was intentionally trying to fail on black people. They didn't try to shoot an arrow, although they accidentally shot one in their own leg.
See how these things are different?
Google, in a self imposed noble desire to rectify poor representation of training data, intentionally injected race discrimination in their system. This intent is what makes it fundamentally worse than unintentionally having non diverse training data.
But it could also just reflect the composition of the population of the countries that dominate the internet.
This hasn't anything to do with unfortunate "training data" for a contemporary AI model.
It's particular egregious in advertisement where even in a country like mine (Germany) where there's less than 1% of the population who are black or African, ads will display 90% black models, white women paired exclusively with black men in unnecessary erotic context, or white women holding mixed-race babies. White men will only appear in negative context like awareness campaigns for testicular cancer or domestic violence. I think at this point it's safe to say that there is a brainwashing agenda at play. So, cui bono?
For a historical example have a look at Gillette's failed marketing campaign from a few years ago:
A piece of facial recognition software that doesn't do well with dark skin shows that the team that built it either didn't think to test it on dark skin or didn't feel it was important to get dark skin working before release.
An image generation model that both doesn't produce images of white people spontaneously and will actively refuse to do so when asked shows that the team that built it either failed to ever ask it to produce images of white people or believed that it was a good thing for their model to refuse to produce such images.
In neither case is the problem purely technical.
When racist employees of Google intentionally racially cleanse their products, this is racism.
Let me know what is confusing about the concepts of agency and intention., and I will try to clarify
So, are the Caucasian people who were involved in any of these miss-optimizations, aka mistakes, racist against themselves? Are they traitors?
"I want a model that produces racially diverse images of people, but I didn't test it in racially homogenous historical contexts" => yes-racism, according to you.
You are again misrepresenting the OP. If you go actually read the OP you will find it very clearly describes Google's initial steps towards fixing the problem, which was: double down on race-swapping in historical contexts. The OP very clearly (and correctly) argues that this is not the action of a person who goes "oops forgot to test in this context" but is rather the action of a person who very intentionally wanted the image generator to behave this way in this context.
But probably not simple enough to pierce ideological bias. I'll ask instead: you really believe that it wasn't tested in historical and other contexts where white people were erased?
Occam's Razor: Of course it wasn't or they wouldn't have released such a ridiculous embarrassing product! C'mon, I think you're the one wearing blinders here.
Of a piece with 'Google more or less explicitly said I won't be promoted because I'm White' stories.
Then please do explain why their first reaction was to double down on "diversity" in historical contexts, before eventually retreating?
Have you never tried tweaking the error text first before giving up and redesigning your form that's confusing users?
It seems like a technical glitch to me. Similarly here, they want the models to represent ethnically diverse individuals, but the model doesn't understand historical context well. It's a technical shortcoming.