Google Photos AI still can't label gorillas after racist errors • Hacker News Board

Re: Google Photos AI still can't label gorillas after racist errors

#71

Post by prepend » Tue, May 30, 2023, 6:05 PM UTC

lisasays wrote: post #66

version_five wrote: post #2

I agree that blocking the feature is the smartest thing to do if people are going to get offended. It's idiotic to call it "racist", it's a shitty classifier, and it's incredibly irresponsible (and probably racist) as well as dismissive of actual racism to pretend that's racist. At the same time, I'm surprised they can't get this classifier to work. It doesn't seem like a very challenging problem (I'd consider myself…

It's idiotic to call it "racist", it's a shitty classifier This is sort of like telling residents of a historically black neighborhood which just so happens to have been exposed to drinking water with dangerously high levels of lead since time immemorial (despite decades of complaints) that it's just "shitty pipes", and that it's "idiotic" for anyone to think this state of affairs could possibly have something to do…

I think the lead pipes example is different because both the neighborhood structure and the lead are results of the same intentional racism.

The classifier is not the result of any racist action on behalf of the programmer.

It’s more like blaming a hospital administrator when people of a discriminated race have bad outcomes at their hospital, when the outcome is the systemic rate of all the other hospitals. The hospital outcome isn’t racist, because the systemic issues that affect the outcomes of the hospital patients have nothing to do with the decisions of the hospital.

Re: Google Photos AI still can't label gorillas after racist errors

#72

Post by lisasays » Tue, May 30, 2023, 6:18 PM UTC

prepend wrote: post #71

lisasays wrote: post #66

Earlier quoted context omitted.

It's idiotic to call it "racist", it's a shitty classifier This is sort of like telling residents of a historically black neighborhood which just so happens to have been exposed to drinking water with dangerously high levels of lead since time immemorial (despite decades of complaints) that it's just "shitty pipes", and that it's "idiotic" for anyone to think this state of affairs could possibly have something to do…

I think the lead pipes example is different because both the neighborhood structure and the lead are results of the same intentional racism. The classifier is not the result of any racist action on behalf of the programmer. It’s more like blaming a hospital administrator when people of a discriminated race have bad outcomes at their hospital, when the outcome is the systemic rate of all the other hospitals. The hospi…

Not necessarily -- recall that lead service lines were extremely widely used in the U.S. in all neighborhoods, black and white. So in fact it's more likely that he pipes being first put into the ground without any racist intent at all.

It's the fact of this problem not being remediated in certain neighborhoods that cause people to ask, very legitimately -- how this came to pass.

Re: Google Photos AI still can't label gorillas after racist errors

#73

Post by surgical_fire » Tue, May 30, 2023, 7:14 PM UTC

prepend wrote: post #68

surgical_fire wrote: post #63

Earlier quoted context omitted.

> is that Google hasn’t prioritized it enough in 8 years to fix. I understand that there's nothing to fix. It doesn't label anything as gorillas anymore. Why waste effort in bringing it back, when another mislabeled photo will only serve for people to scream "racism" on social media? Better to not poke that particular beehive, I reckon.

The bug is in the classifier in that I would want a high accuracy in classifying. I doubt this is the only error it makes, just the most visible. So google isn’t prioritizing accurately classifying black people, just they hard coded to never classify gorillas. Presumably because their classifier is still wrong enough that it would think black people are not people. If they cared about this problem rather than just th…

> Presumably because their classifier is still wrong enough that it would think black people are not people.

That is a very weird interpretation.

The classifier is likely accurate enough to be useful, with many edge cases where things are mislabeled. Edge cases might be difficult to fix without causing other issues, or perhaps the technology to be more accurate is just not there yet.

One of those edge cases causes a massive shitstorm on social media, where many people would love to scream "racism" at it.

So we have two possibilities:

1) Put an indeterminate amount of effort to make things more accurate. It won't bring any benefit (as the current classifier is accurate enough), and if you keep mislabeling black people as monkeys even in rare cases, people will still yell on social media. Beehive indeed.

2) Hardcode gorillas out of the classifier. This solves the issue with minimal effort and causes no shitstorm, besides some annoying people grumbling that it is still racist, but with no substance to their claims.

The choice, to me, is obvious.

Re: Google Photos AI still can't label gorillas after racist errors

#74

Post by jl2718 » Tue, May 30, 2023, 8:13 PM UTC

throwaway29191 wrote: post #16

This is a valuable lesson in what AI can do for society: act as a mirror to show us systemic biases that exist that people try to pretend don't exist. I think most here would understand systemic bias if reflected in a distributed system. Imagine a classifier where, through no explicit intent of the designer of the classifier, always said that some user login was malicious or some binary was malware or the like, even…

There is a rigorous definition of bias, and that isn’t it. That is called ‘variance’. It doesn’t matter if there is structure to the variance. It doesn’t matter if the model is consistently wrong for one type of input. It is not bias unless it has been specifically and intentionally programmed to predict something other than the result from the maximum likelihood estimator given the data. A trickier type of bias is in the dataset itself, but once again, this would require a specific effort and intention to produce that bias by modifying the collection or filtering procedure. Nothing as much has been alleged, so you have provided no evidence of bias, ‘systemic’ or otherwise.

Come up with a different word. Maybe “non-uniform variance”.