[Cycl0ps]

I don't like these stories. It always trends towards the most inflammatory arguments, those being inherint bias and unconscious racism put upon our technology. Real issues in those topics aside, are any articles like this doing anything but feeding flames and generating ad revenue?

Instead, I want to talk about pareidolia. Humans are social creatures. We have evolved to identify others of our kind and read their expressions. This was important to us, as we evolved alongside gorilla analogues as well, and the few of us that couldn't discern one face from another didn't usually last long.

I think we're trying to place too much of a human expectation onto these machines. I think that human features and primate features are strikingly similar, and it's our specialized brains that let us so easily discern. Yes, with enough data and training we could have more accurate models, but we can't cry foul everytime an algorithm doesn't behave like a human does.

Reference: https://www.reddit.com/r/Pareidolia/

[IncRnd]

The thing is that humans are generally excellent at these sorts of pattern recognition, but these networks aren't nearly as good. Even in rigorously trained networks that operate surprisingly well, mistakes will appear that if made by a human would be treated as due to carelessness or stupidity.

So, this is going to happen.

[MattGaiser]

> The thing is that humans are generally excellent at these sorts of pattern recognition

Humans with a lot of experience are. Would kids be? I once referred to firefighter as robots as a kid.

[ineedasername]

Our ability for pattern recognition w/ human faces developed over the course of many thousands of years. People in modern fire fighting gear weren't present through that process. A kid thinking a firefighter is a robot is not the same class of problem. Even kids are good at the type of tasks we're talking about.

[MattGaiser]

People have difficulty distinguishing the individual faces of other races.

https://en.wikipedia.org/wiki/Cross-race_effect

So at some level it breaks down for us too.

[ineedasername]

Yes and I think its fair to say that a firefighter in full gear is a reasonable place for it to breakdown, and that does does not otherwise indicate a failing in human capability in this area that should make our failure modes in any way whatsoever at the same level or rate as computer models.

[IncRnd]

The cross-race effect is an instance of the ingroup advantage. However, that can't be extended to say that people will classify blacks as primates.

[IncRnd]

I would say that most kids would not make the same sort of mistake as this network is reported to have made.

[pen2l]

Something grotesque was put forth by a technology made by an entity that is comically monied. A mistake was similarly made by another monied entity only months ago, so it should have dedicated considerable effort to prevent such things. This is the way this works, we hold higher expectations out of the ones who have resources.

Please do not trivialize acts that have the potential to cut humans so deep with handwavy substantiations. Facebook should have known better, and done better.

[Cycl0ps]

By the sound of it this is a problem that neither entities nor their moniedness can solve. I'm sure these companies watch each other, and when one steps on a metaphorical rake the others are likely taking notes on how to avoid it on their future attempt. And yet rakes are still being stepped on.

When you have an automated system that has irregular behavior to a given input, we call that a bug. Bugs exist in all software, not always unique, but always present. This software is no different than any other. It will have errors. Because the software is categorizing faces, its errors will result in miscategorizing them. The only relevant questions to this are how frequent these errors are and how disparate they are across racial lines.

Another reference: this one is a Tool-Assisted Speedrun of a game that relies on basic image recognition software. While not entirely related, it does show how error-prone these algorithms can be. It's also fun to watch. https://youtu.be/mSFHKAvTGNk

[chrisseaton]

> I don't like these stories.

Nobody likes the stories. No reasonable person is celebrating them. You’re not in disagreement with anyone.

[makeitdouble]

You put a strong focus on how we evolved to deeply care about small facial expression differences and face features to identify and interact with an individual.

These stories are about how we also deeply care about labels and categorization. Aren't we just looking at the natural selection (making them not "last long") of these way too rough AIs that step on bounderies that are pretty important to a lot of people ?

[Cycl0ps]

Ha! I like that! Yes, I guess in a way we are. These models are always being evolved in their own version of 'natural' selection. They go through tens of thousands of mutations before finding one that guesses well enough to be pushed to production. This is just another stage of that algorithms life cycle I suppose. If you want to take an optimistic view of it this is just another part of the tuning process. The AI can train for as long as it likes, but the real thing it's being weighted against is public outcry.

[OneEyedRobot]

>I don't like these stories. It always trends towards the most inflammatory arguments, those being inherint bias and unconscious racism put upon our technology.

Oh well, it's the times we live in.

If people simply laughed at the results and fixed the problems they'd miss all the endorphin rush of outrage.

[nashequilibrium]

I think your comment is a bit dismissive. Facebook is not the first to encounter this, it happened 6/7 years ago and they should have known better. Secondly, if the Data Scientist working on this were all black, this would not have happened, just like the automatic soap dispensers in bathrooms.

[joebob42]

Why would that be true? I don't understand the argument that black people would have avoided this.

From what I can tell the only fix here is a hardcoded workaround outside the net, or a substantially more powerful architecture.

[marmshallow]

What’s this about soap dispensers?

[croes]

FB had a soap dispenser that didn't recognize black people.

https://gizmodo.com/why-cant-this-soap-dispenser-identify-da...

[nullc]

When the video comes up, facebook displays a message that says it is "false information".

(And when you click "why" you get a picture of Arabic text, which can't be copy/pasted into translation software)

Shame on you for distributing false information, it's a good thing we have facebook protecting The Truth. /s

[mardifoufs]

The link says that it's partly false because it's an infrared sensor, that doesn't detect any skin color and isn't biased by virtue of not really making any decision. It just dispenses soap when the infrared sensor gets triggered. The problem is that according to the article black skin does not reflect infrared radiation very well (no idea if that's true, but that's the claim here) meaning it's more of a physical limitation than a "defect" as can be argued in the case of AI models.

But the article also says that a counterargument could be that the existence of machines that aren't very suited to a big part of the population can be seen as proof of some latent Racism (to be more accurate, discrimination is closer to what's used in the article) whether intentional or not.

[SV_BubbleTime]

That’s all interesting and you make good points but…

I think the conversation can be made a lot simpler.

AI isn’t ready for anything important. Done. That’s it. If one of the pioneers in the field can’t determine black peoples from primates - it isn’t ready for driving or war or legal matters or really anything of importance.

I think we (colloquial) made something kinda cool and jumped the gun on when and where to use it.