Hacker News Reader

Comment by im3w1l | Hacker News Reader

Yeah that's a good point, about having sufficient examples in the training set. I think it's not as simple as that though, several years after that made headlines people have still not been able to close the face recognition accuracy gap. Experts are debating different explanations for this.

https://www.wired.com/story/best-algorithms-struggle-recogni...

View on HN

That wasn't entirely my point, however. The training set was only a part of the problem, which in its totality was that the developers failed to consider that the functioning of their system could be affected by the biases they didn't even know they had, in this case of being more likely to see light-skinned faces as belonging to a human.

Their failure was not just in lacking diverse training sets, but diverse QA, or at least QA looking for those blindspots which eventually became evident.

So, correct, it's not as simple as having "sufficient" data.

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.

> 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.

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.

Reply on news.ycombinator.com