The same may be true here, and I think it's the most likely explanation.
I'd be interested in whether the same model can be trained to predict patient wealth, hair color, style of clothing, religion, etc. from the same x-ray data sets.
While "faster than light neutrino" was highly unexpected and rather suspect from the start, the "bone geometry differs slightly between ethnic groups" is well established among the anthropologists of humans. There are also parallels in wider biology of animals - mentioning that to underscore it's as scientifically expected, and not merely construed for humans alone.
The question here was how exactly is AI detecting it this well from chest X-rays; the question centered around AI and possibly if it would unexpectedly influence the medical processes - rather than around the bone geometry itself.
For sake of example, a random link from google search: https://www.researchgate.net/publication/24427702_Ethnic_dif...
This specific model's ability to do it from a 64 pixel version of said x-ray makes me skeptical it's doing so successfully.
That's actually a great example of this problem, though.
https://www.theverge.com/21298762/face-depixelizer-ai-machin...
> It’s a startling image that illustrates the deep-rooted biases of AI research. Input a low-resolution picture of Barack Obama, the first black president of the United States, into an algorithm designed to generate depixelated faces, and the output is a white man.
> It’s not just Obama, either. Get the same algorithm to generate high-resolution images of actress Lucy Liu or congresswoman Alexandria Ocasio-Cortez from low-resolution inputs, and the resulting faces look distinctly white. As one popular tweet quoting the Obama example put it: “This image speaks volumes about the dangers of bias in AI.”
The notion that a classifier can reliably identify race based on an 8x8 grayscale is risible.
The same is... not remotely true for humans, or even two chest x-rays of the same human.