Comment by FakeComments | Hacker News Reader

Comment by FakeComments | Hacker News Reader

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> Her tests on software created by brand-name tech firms such as Amazon uncovered much higher error rates in classifying the gender of darker-skinned women than for lighter-skinned men.

The presumption is that this is racism, but is there any support that it’s not caused by physiological differences?

Women have softer bone structure, on average, and having darker skin literally impacts how light reflects off of it. It’s possible that the change in bone structure and contrast for shadowed parts of the face makes it harder to identify those faces — that’s certainly the case for rounder, darker objects in general.

As far as this article has shown, the researcher is simply so woke she no longer understands biology and physics — and is race baiting instead of acknowledging genuine variation between humans.

Frankly, this seems like a hyper partisan kicking up a fuss over imagined racism than any documented genuine racism.

If the researcher lacks evidence that this is racism rather than a result of physiological differences, this is nothing but race baiting slander — and Amazon is completely correct to contest that nonsense.

> The presumption is that this is racism, but is there any support that it’s not caused by physiological differences?

The conclusion is that the system is biased towards a particular race. The reasons WHY it was biased makes no difference to the fact that the system IS biased.

It might do?

There are 5x more white people than black people in the US (roughly). So... it's kind of understandable that any training data set would contain more white people than black people if you didn't filter it beforehand?

I guess theoretically you would have to limit the size of the training data for all groups to the size of the set of training data you have on the smallest group, so that all groups could be represented equally in the training data, but then perhaps you get a far less accurate model overall and it results in greater negative consequences?

Just spit balling here. But it's a complex problem.

The whole 'racist AI' thing screams Conway's Law to me.

If anything, shouldn't groups be represented proportionetly? Else the lack of viable inuit training photos is going to put a real cramp on everything.

I can't help but wonder which is more likely - that the system has a much harder time classifying the gender of darker skinned women because of bone structure and light reflection or because the training set was not provided enough pictures of darker skinned women in contrast to other combinations of gender and skin tone.

I think it's probably more likely the second just because when there is a significant problem in classification of other things for a Bayesian or other type of machine learning algorithm it often turns out that the corpus had poorer examples of the problematic classification in relation to other classifications - that is to say it is a known problem with a predictable result.

But probably should do some studies about skin reflection, that's a good idea.

The researcher said she identified racial bias. The factors you describe are merely reasons why such bias would exist. What’s your basis for saying this researcher is race baiting, hyper partisan, or showing a lack of understanding of anything?

I've heard people say that sexual dimorphism varies by race. You can find papers when searching for it, anyone more familiar with the space able to confirm the validity one way or the other?

Stature ratios in https://en.wikipedia.org/wiki/List_of_average_human_height_w... say yes. Of course these statistics could be biased by racial prejudice of the people who conducted the measurements, so it would be premature to say that we are not all the same.

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