Normally the developers wouldn't classify images themselves (their time is too valuable). In any case, I think failing to detect a face as human should be rare unless the photo has very poor lighting or the person is almost hidden.

The developers, or the project managers, are the ones setting the parameters: the expected outputs and the processes necessary for that output to be generated. That they don't classify the images themselves doesn't take away from the failure of the system to work as expected being a result of their failure to fully understand the problem, which is itself reflected in their unconcious biases.

Your expectation was the same one they had, and it was wrong, which is the crux of the issue.

There's only been one case of being unable to recognise black faces that I know of, and it was shown later to be due to the lighting conditions the guy was using leading to very low contrast imagery. The same problem was replicated with white faces: there was no racism anywhere as you would expect given that unconscious bias hasn't been shown to exist at all (the studies that claim to show it have all collapsed).

If you asked the developers of the facial recognition library, "does your software have problems with very low contrast conditions" they'd surely have answered yes. Fully conscious of the issue but, that's software. It's hard to get everything right 100% of the time.

There have actually been several cases of recognition and classification issues, and it is an ongoing problem.

> Your expectation was the same one they had, and it was wrong

Do you have a source for data set mis-labelings being a problem?

I didn't say they were. You're beginning to present yourself as someone speaking in bad faith.