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Comment by hellohowareu | Hacker News Reader

Simply go to google image and search: "skeletal racial differences".

subspecies are found across species-- they happen based on geographic dispersion and geographic isolation, which humans underwent for tens and hundreds of thousands of years.

Welcome to the sciences of anatomy, anthropology, and forensics.

other differences:

- slow twitch vs fast twitch muscle

- teeth shape

- shapes and colors of various parts

- genetic susceptibility to & advantages against specific diseases

Just like Darwin's finches of the Gallapogos, humans faced geographic dispersion resulting in genetic, diet (e.g. hunter-gatherer vs farmer & malnutrition), and geographical (e.g. altitude) differences which over the course of millennia affect anatomical differences. We can see this effect across all biota: bacteria, plants, animals, and yes, humans.

help keep politics out of science.

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> skeletal racial differences

£10 says that its not that. Anatomy is extraordinarily hard, and AI isn't that good, yet. Sure different races have different layouts, but often that's only really obvious post mortem. (ie when you can yank out the bones and look at them, there are of course corner cases where high res CAT/MRI scans can pull out decent skeletal imagery in 3D) There are other cases, but that should be easy to account for.

If I had to bet, and I knew where the data was coming from, I'd say its probably picking up on the style of imaging, rather than anything anatomical. Not all x-rays have bones in, and not all bones differ reliably to detect race.

> keep politics out of science.

Yes, precisely, which is why the experiment needs to be reproduced, and theories tested through experimentation. The reason why this is important is because unless we workout where this trait is coming from, we cannot be sure the diagnosis is correct. For example those with sickle cells have a higher risk of bone damage[1] which could indicate they are x-rayed more. This could warp the dataset, causing false positives for sickle cell style bone damage.

[1]https://www.hopkinsmedicine.org/health/conditions-and-diseas...

I wonder if some communities use certain x-ray machines verses which machines are commonly used by other communities and this has nothing to do with race but the machine being used. I read over the paper but didn't really understand it. Maybe all this is doing is identifying which machine was used.

>If I had to bet, and I knew where the data was coming from, I'd say its probably picking up on the style of imaging, rather than anything anatomical. Not all x-rays have bones in, and not all bones differ reliably to detect race.

This was my guess as well. I've spent a lot of time around radiology and AI (I used to work at a company specializing in it) and we read a lot of the failure cases as well. There was one example where the model picked up on the hospital, and one hospital was for higher risk patients- so it learned to assign all patients from that hospital to the disease category simply because they were at that hospital.

There are a ton of cases like this out there, especially when using public datasets (which in the medical field tend to be very unbalanced datasets due to the difficulties of building a HIPAA compliant public dataset).

> one hospital was for higher risk patients- so it learned to assign all patients from that hospital to the disease category simply because they were at that hospital.

That just sounds like poor feature selection/engineering. Garbage in, garbage out.

Yeah there are definitely ways they would have avoided that, but it's just one example of many. The whole point of ML is that it picks up on learned patterns. The problem is that it can be difficult to identify what it is learning from- this paper itself says they do not know what is causing it to make these predictions. As a result it is difficult to validate that the model is doing what people think it is.

>I'd say its probably picking up on the style of imaging, rather than anything anatomical

Certainly possible! They do control for hospital and machine …

>Race prediction performance was also robust across models trained on single equipment and single hospital location on the chest x-ray and mammogram datasets

… but it’s also possible that different chest x-rays were being used for different diagnostic purposes and thus have a different imaging style, which a) may correlate with ethnicity and b) does not appear to be explicitly controlled for.

The weak factor in these AI studies seems to be data set normalization.

The article is pretty fascinating and I recommend that you actually read it. For example:

>"We found that deep learning models effectively predicted patient race even when the bone density information was removed for both MXR (AUC value for Black patients: 0·960 [CI 0·958–0·963]) and CXP (AUC value for Black patients: 0·945 [CI 0·94–0·949]) datasets. The average pixel thresholds for different tissues did not produce any usable signal to detect race (AUC 0·5). These findings suggest that race information was not localised within the brightest pixels within the image (eg, in the bone)."

So just from the silhouette of a skeleton, if I understand that correctly?

Even after being munged into a nearly-uniform gray by high pass the effect seems pretty robust.

One of the primary ways of identifying possible race from bones in anthropology involved calculating ratio from lengths. Good for an estimate or fallback, but not completely accurate. Removing the density would do absolutely nothing to obscure that method. Any image will allow you to measure ratio of bones sizes.

The problem with 'race' as a concept isn't that you can genetically tell people apart.

Our tools are so precise you can tell which parent a set of cousins had with DNA tests, this doesn't make them a different species/sub-species or race from each other, even if one group has red hair and the other has black.

It's the pointless lumping together of people who are genetically distinct and drawing arbitrary, unscientific lines that's the issue.

Presumably the same experiments that can detect Asian Vs Black Vs White could also detect the entirely made up 'races' of Asian orBlack, AsianorWhite and WhiteorBlack since those are logically equivalent.

So are the races I made up a moment ago real things? No. But a computer can predict which category I'd assign, doesn't that make them real and important racial classifications? No it means my made up classifications map to other real genetic concepts at a lower level, like red hair.

Then problem is that human experts sometimes can't tell the difference while the model can.

AI is also able to determine your sex from your retinal scan with very good levels of certainty (provided that your retina is healthy; its ability to tell sexes apart drops in diseased retinas). [0]

Which came as a surprise to the ophthalmologists, because they aren't aware of any significant differences between male and female retinas.

[0] https://www.researchgate.net/publication/351558516_Predictin...

I am surprised that this is a surprise. At least color vision is encoded in the X-Chromosome so there should be variation as males have only one which can be expressed.

It is a surprise, because the retina as an organ is very well visible and observable in living people, so we have a ton of observational data and practical clinical experience. But despite that, humans haven't noticed anything.

I've never seen a retina which had been separated from a human.

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