I'm on board with assuming that the intent of Google / the engineers who built the classifier is not racist. However the outcome — labelling a black person as a gorilla — certainly is racist. What makes you think otherwise?
I think intent is important for labeling something racist and a function doesn’t have intent. And it doesn’t seem like the programmer had intent.
So I agree that a human seeing a person and labeling it as a gorilla is racist, it’s because the human is making an inappropriate value judgement.
But this is AI -- so the stupid rule is not pre-programmed, but rather curve-fit to the data (uh, "learned").
So ultimately it's a matter of (1) failing to find the right training data (or procedure) and (2) more fundamentally, choosing not to correct the problem after 8 years.
I think this gets fixed by better training data and more pictures of really dark skinned people. So with more supervised labels of dark skinned people to people, properly so the matching doesn’t think people are closer to gorillas.
Comically/sadly, we’ll know we get closer to fixing the training sets to be more inclusive when google starts labeling gorillas as people.
I think there are some systemic reasons why there aren’t more diverse populations in training data. And those are more society issues than AI issues (ie, rich people are more represented, rich people are certain races, therefore races are more represented).
And finally, I’ve worked in software that people just test what they are and know so I’ve seen so many test plans that are too simple and only test the programmers dob and address. This doesn’t mean racist because all the programmers are Asian males. It just means the quality review wasn’t thorough enough to include proper test conditions.
I might be inappropriately conflating software bugs from different areas but this is what makes me think “stupidity or weakness more likely than racism.”
Given the recent history of equating black people with non-human primates, and using that to deny them rights & full participation in society, making this error is going to be experienced as racist. It's not a matter of individual malice or taxonomic classification, but of history and social relations.
But it seems like if nobody is working on this, how will we ever fix this gaping hole in image classifiers? And don't we want to fix it? And to fix it, research will continue to get it wrong until they get it less wrong and more right, but can only iterate without a massive backlash. It seems like being stuck between a rock and a hard place.
I am rhetorically asking, wouldn't we have to allow researchers to iterate on this problem to fix it? That simply won't happen until we are able to allow them leeway understanding that this is an incrementally improving model. Otherwise what we have is just a sledgehammer solution (just banning all primate classifications) which actually never addressed the problem, that these models do have a race-based bias (probably in their input datasets.)
Is this the case? Do we even know for a fact that only non-white people were mislabeled as anything else?
Or are we just, you know, throwing out baseless speculation as fact?
Like that they don't appreciate the gravity of the problem, for example.
What would be racist from this outcome is if it kept doing this and no one did anything. Clearly it hurts people's feelings and that is a very valid issue. Googles option to just nuke it is a great start until they can hammer out the kinks.
Racism can also be measured by its effect, regardless of intent.
What would be racist from this outcome is if it kept doing this and no one did anything.
After 8 years, that's seems to be precisely what's happening.
The thing you're calling racism is actually hate speech as it's typically defined in law.
The fact is that training sets usually contain many more white men than black women, especially if they're just scraped off the web. People who guided the training may have just used datasets that reflect their own culture and demographics of their own country, and didn't see a problem with that. The opposite would have been be seen as "pandering to diversity" in their country, so they've ended up with a biased dataset and a biased algorithm.