Vision researchers should take a class or two in cinematography and photography, it would serve them well. Even the quality of the lens makes a difference. Most work I've seen out there uses cameras that barely pass as security cameras or webcams.
That said, yes, ML needs to work with crappy images and every single camera out there. My argument is that you are not going to be able to train using crap data. And the images in a data set would be crap if the data --the images-- were not acquired using cameras and techniques that provide enough data across various segments of the dynamic range.
Again, I gave the example of my black GSD for a reason. You are not going to be able to recognize him as anything other than a blob on a couch without a camera that can capture enough data at the low end of the dynamic range and a system trained with that data.
The fact that Samsung (or anyone else) failed means nothing. In order for that data point to be meaningful you'd have to have intimate knowledge of what they were doing and what capabilities they had, both in terms of science and engineering as well as the consumer hardware they developed.
I have competed against multi-billion dollar multinational corporations who, despite their financial prowess and scale, could not design their way out of a paper bag. They don't understand the problem, lack creativity and, most importantly, absolutely lack the passion necessary to solve it. Ten 9-to-5 engineers can't compete with a single engineer passionate enough to devote every waking hour to solving difficult problems. It doesn't matter how much money you throw at them, they just can't perform.
This doesn’t help when the variance in feature space of white people’s faces is objectively a lot wider than the variance of black people’s faces. You can have the best camera in the world, but it’s not going to change the fact that someone from Ireland looks way more different versus someone from Italy (in any mechanical basis) than someone from Cameroon looks versus someone from Malawi.
To add to this considering that genetic variations between peoples on the African continent are larger than the variations to anywhere else, this is also very unlikely. I think it's a reasonable assumption that genetic variation and variation in physical features are somewhat correlated.
You are misinterpreting this, as does almost everyone else. Phenotypic variance is almost completely orthogonal to number of unique SNPs (which is what this generally refers to).
> genetic variation and variation in physical features are somewhat correlated.
This is not the case. You can have narrow population bottlenecks (reducing SNP diversity) followed by high variance in selective pressure. This is exactly what happened to early Europeans. You had a small initial population spread out to inhabit a large variety of ecosystems.
But that's still a cop out. You can't use models with these problems. It gets you disasters like face unlock that doesn't work for black people. This happens even if people aren't white. Samsung famously released blink detection that didn't work on a lot of East Asian faces. The products are broken because the white bias is the default and pervades everyone's thinking.