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Facebook Apologizes After A.I. Puts “Primates” Label on Video of Black Men

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Re: Facebook Apologizes After A.I. Puts “Primates” Label on Video of Black Men

#52
post #11

Don't these kinds of companies have a basic checklist before releasing features like this? And if so, why doesn't this checklist include things like "doesn't identify members of racial group as animals" ? It makes me think that any woke PR coming from Facebook is lip service, if they don't have even basic checks in place.

Did they have even one black person on the AI team? https://www.credera.com/insights/racial-bias-in-machine-lear...

Why would that have made a difference?

Re: Facebook Apologizes After A.I. Puts “Primates” Label on Video of Black Men

#53
post #27
post #8

Earlier quoted context omitted.

We don’t really know that. It’s possible at some point the AI may have been classifying white humans as primates but nobody cares because it’s white humans. You only have a story worth reading when it’s against an underprivileged minority.

It's unlikely. Search for images of primate, and you'll see fairly dark, humanoid shapes. Poor training data then seems like a more probable reason for visual confusion. > You only have a story worth reading when it’s against an underprivileged minority. To some extent. But I think there would also be a story if some medicine would work badly on white people, or men. The problem here is that these labels would be the…

Yeah, white primates are pretty weird. It only happened because a few migrated north into temperate Europe and Asia and lost their melanin a while ago.

Re: Facebook Apologizes After A.I. Puts “Primates” Label on Video of Black Men

#55

Surprised they weren't more careful with this after Google had the same problem a few years ago. https://www.theverge.com/2018/1/12/16882408/google-racist-go...

OK, I'll try to tread carefully here, but I think it's really helpful to separate the technical issues from the societal issues.

In the Google photo case, I've seen the picture in question, and it's not difficult an all to see how a rudimentary statistical algorithm would mistake the photo for a gorilla. No, no human would make this mistake, but due to the lighting in the photo and the woman's hair it's really not hard to see how that mistake would be made, similar to how Tesla's AI can mistake the sun, low on the horizon, as a yellow traffic light.

The algorithm was not "racist", it just didn't have the social context that describing black people as apes and monkeys has a long racist history. And that is really the fundamental problem with most AI these days - it can get very good at statistical inference, but it doesn't have the background knowledge and logic to be able to "think" in the same way humans do.

In a similar vein, I recall someone lamenting a couple years ago how Google's street directions would never mispronounce "Malcolm X Boulevard" as "Malcolm 10 Boulevard" if they had any black programmers. Again, given 99% of the time when you see "X" in an address you'd pronounce it as 10, it's not hard to see how this could happen. The problem is that some errors are much more offensive than others, and AI can't really reason about that.

Re: Facebook Apologizes After A.I. Puts “Primates” Label on Video of Black Men

#56
People are saying that this is a very difficult problem to solve. It is not.

The problem is not classifying people as "animals;" the problem is classifying people as a known stereotype. Make a list of every animal/thing that correlates to a known stereotype in humans. Then program your AI to never classify those animals/things.

It is not a big deal if your AI isn't able to detect gorillas. That doesn't really affect UX.

I will add that just because it is an easy problem to solve does not mean they should solve it. An AI classifying someone as something else should be something to laugh at, not take offense to.

Re: Facebook Apologizes After A.I. Puts “Primates” Label on Video of Black Men

#57
This particular AI failure mode keeps surfacing, but seems to be fixed rapidly when it generates bad press. How do they actually correct it? Teach it some more granular categories of living things? Slap a sensitivity filter on top of its output?

Re: Facebook Apologizes After A.I. Puts “Primates” Label on Video of Black Men

#58
There’s an amazing number of “but people are primates” comments. Correctness here includes specificity. If person and primate are both candidates, then primate isn’t a correct answer to what this is trying to do. You wouldn’t react to the phrase ‘treating women as objects’ by saying people are objects, right?

Re: Facebook Apologizes After A.I. Puts “Primates” Label on Video of Black Men

#59
post #28

Earlier quoted context omitted.

Which is inherent to the problem. The domain is the space of _all possible natural images_, it’s so big, it’s ridiculous. Fundamentally, these techniques do not analyze images like humans do, but are rather trained to pick out any salient signal it can latch on to. That seems to be “primates are mostly like humans but darker”, which is superficially true but a pretty weak definition as it includes dark skinned humans…

It's probably because humans are primates – but the AI systems often have to treat “human” as a completely separate category as “primate”, so they have to draw weird, complex boundaries around “primate” (actually “all non-human primates”). When the “primate” classification is stronger than the “human” classification, the system says “primate” rather than “human”, and if it's predominantly been trained on “pictures of…

These models typically don’t have hierarchical labels like that, and they apply a softmax to their output - which means /one/ label will be considered correct. (A softmax means taking the exp of your predicted scores, then divide by the sum.)

Re: Facebook Apologizes After A.I. Puts “Primates” Label on Video of Black Men

#60

Surprised they weren't more careful with this after Google had the same problem a few years ago. https://www.theverge.com/2018/1/12/16882408/google-racist-go...

OK, I'll try to tread carefully here, but I think it's really helpful to separate the technical issues from the societal issues. In the Google photo case, I've seen the picture in question, and it's not difficult an all to see how a rudimentary statistical algorithm would mistake the photo for a gorilla. No, no human would make this mistake, but due to the lighting in the photo and the woman's hair it's really not ha…

The problem is the training set, which apparently skews too much towards white ppl, and doesn't include checks for the primates/non-white humans differentiation. One could argue this is due to white people building things for white ppl, which does indeed point towards systemic racism in the overall development process.
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