Facebook Apologizes After A.I. Puts “Primates” Label on Video of Black Men
nytimes.com
nytimes.com
So, the lack of outrage here (I mean in hacker news) is unsurprising, and would of course be greater if there were more black people "in tech" and by extension here.
And even as I write this, I'm not trying to judge -- just more open the door to understanding, like, y'all understand this fact, right? I find myself searching for metaphorical equivalents. I'm thinking something like "what if it labeled Jewish people as rats" or something similar.
Even if that doesn't work -- I suppose the broader point I'd try to make here is: A lot of y'all (and me, often) assume the following very wrong idea, subconsciously -- "Because I'm working with a computer I can assume that what we are doing is infused with objectivity, like math"
Important to remember nooooooooope. This is dead wrong. We're ALWAYS bringing our subconscious whatevers to this and there is very little, if ANYTHING at all, that can be correctly considered "coldly and obviously correct and object like math is." (perhaps even what we imagine math to be)
The interesting question is how the hell do we fix this?
The comments about enlarging training sets illustrate the blinders that many technical people have about this technology. There does not seem to be a notion of independent quality assurance to detect what should by now be obvious problems.
[1] https://www.bostonmagazine.com/news/2018/02/23/artificial-in...
Edit: oops, left out source
Facebook is more dangerous; precisely because the harm in getting it wrong is so much easier to hide.
The problem with this is no one actually trained the AI to have a racially bias but rather did not train it on recognizing the difference. Why? Probably because no one knew the computer was going to identify this resemblance and immediately label it as such (I have heard babies learning to talk and name shapes/colors do the exact same thing with no outside influence) Computer vision operates on matrices of color coordinates that share resemblances with other similar matrices and attempts to match patterns between multiple sets to establish resemblance. It is not the fault of the computer or the programmer that there are coincidental resemblances that exceed it’s learned threshold where it decides that the two sets represents objects of the same type.
I personally am ok with admitting that my skin color visually is more similar to images of certain non human primates and can understand why this problem is not a programmer bias but actually a complicated computer vision problem as it’s easy to spot the resemblance but often difficult to train a neural net on why the images resemble eachother but are not related.
So I will state clearly -- yes, I am aware my opinions are absolutely related to my skin color and that's precisely why you all need to pay special attention to them. Not because of some sort of silly inherent genetic thing, but because I have experiences that many of you don't.
We used to think of this as racist stereotyping, but in the pendulum of history we have swung back to where in order to not be racist (or, better yet, anti-racist) we have to be actively racist.
The zeitgeist is to view everything through the lens of race / skin color ostensibly in an effort to combat racism, but in reality all it does is create more racism.
The fact that we have multiple black people commenting here with opinions diverging as widely as they do should be proof positive that race or skin color has almost nothing to do with your opinions.
Two people can go through the exact same experience and come out entirely different persons or very similar. Two people can have the same skin color and go through totally different experiences. Two people can have different skin color and go through very similar experiences. Thus when dealing with individuals we should not ascribe to them stereotypes unless we have to make an informed guess, because statistical patterns apply only to groups not individuals.
Guessing my race based on my opinion will be left as an exercise for the unconvinced reader ;)
My insistence on what I said isn't about anything permanent or immutable. This is about what I hope to be a temporary state of affairs, namely that -- it is at least plausible that the lack of black folks in tech today is leading to unintended negative consequences, and perhaps we should talk about that directly without having to worry about the fragile torpedoing the conversation by confusing this hopefully temporary state with some bigger overarching "statement about people."
And maybe I didn't quite say this in the exact way that won't set people off the first time, but you know (or should know) that we have to do this A LOT. I was hoping to be able to skip it, but hey, guess not for now.
The downvotes may have come from individuals who also think it would be helpful if you treated yourself and others as individuals rather than avatars of their racial collective. Your opinions are your opinions, there is nothing black or white about your opinion (in terms of skin color at least) or anyone else's opinions. There are white people that think like you, and there are black people that think unlike you.
Having a certain skin color doesn't make your opinion more valid or more worthy of being listened to either, even if you are in a demographic minority. For your opinion to be valid it has to have merit, so you have to back it up with good arguments that can convince a reasonable person. There's no racial cheat code we can/should insert into public discourse to bypass rational evaluation. That would be the bigotry of low expectations (you can't argue effectively for your case because of your identity, so we're gonna accept your conclusion because of your identity).
Shifting from the indefensible: "my opinions are absolutely related to my skin color and that's precisely why you all need to pay special attention to them" to the much milder and very different: "[it's] plausible that the lack of black folks in tech today is leading to unintended negative consequences", and glossing that over as "maybe I didn't quite say this in the exact way that won't set people off the first time", rather than admitting you were racially stereotyping yourself and (at least implicitly) other individuals is poor form. That's not a clarification, but a different argument. I think it's high time white leftists and leftist blacks confront their own racism, cuz that's the only form of socially acceptable racism that currently exists in the west.
Having said that, I think there's not much interesting about the hypothetical you raised. Changing the demographic composition of any group of people is almost certainly gonna change the distribution of opinions on the spectrum on any given topic, but that does not mean we are getting closer to truth. In the end, all that should matter is the facts and the arguments. That's why slavery and segregation were abolished at a time when blacks were vastly outnumbered in the population and in politics.
We don't need population level representation of blacks in tech (though it may be beneficial) to hold big tech accountable and achieve meaningful change, we just have to make the argument and raise awareness and reasonable people of all races will join in your cause if your argument has merit. Let's get back to MLK's ideal of color blindness, because those who are teaching us to see color are teaching us to hate each other.
MLK preached judging by Character instead of Color, which is referred to as color blindness.
Also, the moderates of his era were moderate relative to the political landscape of their era. This is not a universal indictment, but a very culturally situated and contextually dependent statement, unlike the universal and timeless principle of color blindness.
Because of that, calling your opinion a black opinion is literally an invitation to your conversation partner to become racist in their judgment, since you are trying to get your conversation partner to focus on your race when they are considering your opinion. The only right way of treating that additional and unnecessary information is to ignore it, but if it can only rightly be ignored, then it shouldn't be shared in the first place since it is an impediment to constructive dialogue.
Skin color is as important as the paint job on your car: for some people it means the world, for some people it's just a paint job. It's liberating to not care about race, you should try it.
People will and do treat you differently because of your skin color. And how people treat you affects you, whether you think so or not. Either you live in a hole and never come out, or you haven't yet fully comprehended what's going on around you.
It's NOT simple. It's not "getting called names all the time" or anything like that. It takes time to fully get.
I've experienced neo-nazi racism against my person because of my skin color (and I mean literal self-identified neo-nazis, not merely a label given through the slanderous designation of some intolerant leftist). Despite those experiences, because I chose not to succumb to a mentality of victimhood those people's hateful attitude and behavior did not have the harmful effect on me it could have had.
However, if you believe that racism is as pervasive as the air we breathe, and the country is systemically racist, and white people suffer from implicit bias (and mysteriously it's only white people, kinda racist huh?) then I have to disagree. Racism exists, but the vast majority of people are good people. These days, the real racists (besides the tiny minority of actual white supremacists) are the so called 'anti-racists'. Their anti-white rhetoric to me is as appalling as the anti-black rhetoric of white supremacists. They forget the golden rule, don't do to others what you don't want done to you.
Of course, we all deserve to be judged based on the content of our character and not on superficial markers like skin, gender, etc, etc, etc.
However, broadly and statistically speaking, people from different economic and racial groups tend to have differing experiences, largely because of systems and not really because of any individual experience.
This is the core thesis of Critical Theory (and it’s subset Critical Race Theory): in the court of law, one must consider all of the details and context of a person, as they necessarily have an impact on an individual’s experiences.
The fundamental flaw of CRT is that it denies color-blindness. It doesn't matter that CRT claims to have good intentions for making race a factor in your judgment, it is still racist. The (progressive) eugenicists of yesteryear had good intentions too, but were horribly evil. Somehow progressives keep falling for the same trap and history keeps repeating itself. It's like racism is baked into their DNA. In every era they attempt to judge based on race and justify it with their ostensibly good intentions. It was racist then and it's racist now, but somehow the current iteration of left wing racism is just as socially acceptable and fashionable as every previous iteration.
Also I have a problem with the view that systems are the main reason for individual experiences. Two factors make up your life: your environment and your decisions. Your decisions are the most important part. No matter how good your environment, you can ruin your life by your decisions. No matter how bad your environment you can improve your life with your decisions. Constantly externalizing blame is a surefire way to short circuit the learning process that leads to self improvement. Having said that, I do agree that systems should be improved and tweaked, but it should be done carefully, because it's far easier to damage a reasonably effective complex system than to improve it.
This is not true. I just googled "racist" and it says "prejudiced against or antagonistic toward a person or people on the basis of their membership in a particular racial or ethnic group, typically one that is a minority or marginalized.".
Simply considering race doesn't automatically make it prejudiced or antagonistic.
And of course, there's also the concept of "structural racism", which is what CRT aims to help us dismantle.
"Discrimination or prejudice based on race." - from The American Heritage® Dictionary of the English Language, 5th Edition.
Since discrimination based on race is racism, once you make race a factor in your judgment (even if it's one factor out of many) then you are discriminating based on race and therefore racist.
It doesn't matter if you are trying to help a specific race or hurt another race, privilege or oppression, they are two sides of the same racist coin. You may think you can justify your racism by appealing to historical racism, but it doesn't change the fact that you are being racist.
I happen to think that all racism is evil, I don't care about your intentions or which group it's directed at. After all, even Hitler claimed to have good intentions. There has yet to arise a racist that doesn't believe that he is doing something good. That's why I think trying to excuse some forms of racism while condemning others is arbitrary and hypocritical. Ends don't justify means. If you actually want to help people, find a way to do it that is not racist.
Edit: forgot to respond to CRT / structural racism
If by structural racism you mean anything other than laws/policies that are overtly racist or intentionally designed to target certain races despite having no overt racist language, then I agree that that is a problem. If you however conclude from a disparity in outcome that racial discrimination must be the cause of the disparity, then I think you are committing a logical fallacy (affirming the consequent).
The fallacious argument is:
(1) If there is structural racism then you will see disparity in outcome for different races. (2) there is disparity in outcome for different races (3) therefore there is structural racism
There can be any number of reasons to explain a disparity in outcomes across races. The existence of the disparity is not enough to prove racism. What I find is that the accusation of racism is made too easily, because there is political currency in victimhood. It's sad because tilting at windmills obstructs the actual progress that can be made at solving real problems, because we are distracted with thought-policing our white neighbors.
This perfectly analogizes to most everywhere else; angry open n-word saying racists are usually powerless losers.
More problems come from the larger combination of the rest of the "racism" spectrum, whether apathetic, or racist-but-quiet, or harbors latent biases that they may not know about etc.
(And here I do feel like I have to say, the answer isn't "SMOKE THEM OUT AND EXPOSE THEM" on the personal level, it's just taking the utmost care in the work you do)
When people are careless in making accusations that's incredibly damaging to society. You as a black person should know how divisive and damaging false allegations can be, since that was a tool racists used to instill racial hostility in society. Let's not repeat those mistakes. I'm not saying racism doesn't exist or that we shouldn't do anything about it, but knowing in your heart of hearts that this must have been an act of deliberate racism is simply not enough. We need a smoking gun, or else it's best to withhold judgment.
The fact that no one thinks the mislabeling by the AI is acceptable should be enough. That shows you that no one would defend a person who deliberately designed this. The 'rush to excuse' is not because society doesn't want to condemn racism, but because most of society still cares about fairness and not making false accusations.
So to clarify -- the "rush to excuse" that I'm saying is dangerous is really "the rush to insist that there is definitely no bias in play," NOT the "the rush to accuse individuals of being racist" -- and I suppose the fundamental problem I see going on here is that people can't seem to distinguish these two things, even though they are very different.
I'm going to keep saying this: the biggest problem is not smoking out hardcore racists who openly hate, the biggest problem is getting much of the non-black tech populace to even begin this conversation without the hypersensitivity kicking in (fully acknowledging that, while I'm not a fan of the terms "SJW" and "woke" and such, there also absolutely exists a naive liberal left that presently makes this conversation harder because they lean too hard into their particular direction)
This behaviour is counterproductive. It is not susceptible to sway me towards your point of view, but instead discredit you and those holding similar opinions.
But I will absolutely insist that this racist outcome is possible -- even likely -- absent any bad intentions from the programmer(s) who did it.
ALSO: I'm 99% sure of the following:
A black team of programmers working on "distinguishing humans from monkeys" would never let the mistake of "black and not white people identified as monkeys" out the door.
That's the point here. I'm not saying it wouldn't have happened somewhere in the process, I'm saying that you or I (if we don't work on the inside) would NEVER have seen it because it would have been noticed and fixed before then.
As I said before, I'm not here to smoke out old (or even current) racists individually. I don't think it's a valuable practice to "witch hunt" (even when the "witches" in this case are absolutely real and do exist.)
Because what that ends up doing is: every e.g. white person who's never said the n-word, or who has black people in their family, or has one black friend etc etc etc now subconsciously but completely lets themselves off the hook in any way. They get to think of themselves as superior because we've now defined racism as essentially a binary.
The above situation is mentally easier for many people (perhaps you as well) to deal with, rather than considering how deep this all goes.
Look, one wild thing you realize as a black person is that nearly everybody everywhere is to some extent surrounded by racism is that most everybody has some of it subconsciously internalized, and you don't fix it until you think about it directly, in yourself and others. I'm not a huge fan of "oh everybody's a little racist" because whoever says it is usually doing something dumb like excusing behavior -- but it's FAR closer to the truth than "if we smoke out the hidden but self-consciously racist people all will be fixed."
(so I suppose I'm saying -- yes, pay attention to what you are suggesting we pay attention to -- but also understand that it is almost CERTAINLY nowhere near sufficient to fix the problem.)
At this point, if a web scale service like this do this it should be grounds for significant sanctions: increasing fines (think GDPR scale) and closer scrutiny (think mandatory inspections and quality controls every month, paid for by the company) should do I think.
But, while I'm really annoyed by this and support you I'm also sure there will be enough outrage against it in time from.
I'm genuinely curious what else should have happened.
And since they fixed it right away small fines only, like nuisance small. And small follow up.
If this ends up giving entry level jobs to people who have a hard time entering tech so they can help testing this, I can live with that too.
But frankly, I don't see your point: is it OK to call dark skinned people monkeys if the system who does it is automated && the company who run it is willing to apologize and stop it after getting caught?
PS: this is a weird situation for me, I'm a normal white bloke, not woke or anything, I just try to push companies to show basic decency.
Some of the commenters may exactly think that the algorithm is objective, forgetting that it's built by ppl, for ppl, tested by ppl. If the algorithm kept labelling bearded white dudes as vaginas, it wouldn't be pushed to production, because it would found as a bug and dealt with somehow. But labelling people of color as monkeys is apparently something they're unaware of, which doesn't necessarily point to deliberate racism, but a systemic racism issue.
In a lot of discussion here, everyone, on all sides of the argument, is getting voted down. Perhaps this shows this community is not ready for these types of discussions.
Neural nets, in particular, aren't objective... They're bias-learning engines. Their whole raison d'etre is to take (in this case) a bunch of images and go "Okay... Humans believe these random patterns of pixels have semantically different meanings. I'm going to try to explore a space of what those differences could be until they tell me I got it right." If there are things we as humans care about classifying differently that we fail to tell the computer, it won't know to separate them.
The fact we keep making this mistake is a real problem. And it's a human problem, not an algorithm problem.
Words have meaning on multiple levels.
On a technical level, yes, all humans are primates, but if the algorithm were following this rule, it would tag all humans in all photos as primates, but it didn’t do that. It only tagged black people.
That takes us to a social meaning level. In this context, you have to consider the long, racists history of dehumanizing black people. They have often been called “monkey” and “ape” with the intent to degrade and to segregate them from “people”.
In that light, and the fact that the label was not applied in all cases where appropriate, it is hard to see it as anything but racist. Probably not deliberately but if the training material contained racist material, it could bias the result. Even if the training material was inadvertently unbalanced and contained very few black faces, the algorithm would have less experience with black faces and would not know how to categorize them as well as white faces.
This is analogous to people who grow up and live largely isolated from close associates with those of other races. Their experiences are filled with varied samples of interactions with people of their own race, but are poor in samples with other races. In the case where we have less experience, we tend to fall back on broad stereotypes and our reactions show strong racial bias.
There are other more subtle problems that may contribute. As an amateur photographer I've been told that most cameras are calibrated for light skin. If you aren't careful with your setup you'll get bad definition in shadows and relatively very dark areas. If you have badly calibrated/exposed photos then very dark faces, whether of sub-Saharan people or gorillas, may be poorly distinguished. I bet there are loads of unfair, hard-to-catch issues like that.
By the way I don't mean to say that you're wrong, but just to offer a Hanlon's Razor-type counterpoint as it occurred to me.
It's possible that nobody directly involved in this was actively racist, but we're building on centuries of racism in every facet. So a little bit of bias in each technology and data set involved, not to mention the engineers and their testing, just adds up
Nooo. We're building on techniques that have worked well enough up til now, but people don't bother to dig in a d come to terms with what's actually going on.
Not everything is racist. It's more a tyranny of 'good enough' and convenience.
Bigotry is part of society, because it's existed for so long and still does. It doesn't mean that everyone is actively racist. It just means you can't just say there's no bigotry affecting things, and in saying there isn't, you're now blinding yourself to the possibility of negative biases.
A similar situation exists with accommodation of people with disabilities. Once you know a building architecture can shut out people by design decisions- at some point we worked through at least some basic level of agreement on principle that accommodation is required.
https://www.theverge.com/2018/1/12/16882408/google-racist-go...
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.
Yes, humans are primates. But this wasn't a discussion about taxonomy in biology class. There are lots of cases where primates is used in everyday language where it is clearly intended to refer solely to monkeys and apes. When I go to the "primate house" at the zoo I'm not going to expect to see naked humans behind the glass.
This algorithm is clearly broken, it doesn’t label all people as primates, it labels Black people wearing a green T-shirt possibly as such, I mean I’m sorry but that algorithm is definitely broken. The person in question should not be labeled as primate by any means not should any other humans unless the context is specifically some form of species taxonomy.
Also your reaction seems to be all daisies and roses, and I don’t blame you for your social context ignorance you displat, I guess you are just an engineer after all..
Also which teams at apple made the algorithm? Since you claim to know they were all 'white'.
In reality, I'm sure everyone working on this team was well aware of these potentials for training set bias before most of us even knew what ML was.
Even if this was considered it'd be pretty risque to have an employee dedicate time specifically to differentiating black ppl from apes in the algorithm.
And to your second point about how would we know if the team was “all white,” of course we don’t know that and it’s probably not true. What we do know is that tech in the us is disproportionately whiter than the general public, and ~10 person teams with no black people on them are the norm, not the exception.
Edit: I should say that 10 person teams without black people on them are the norm in FAANG companies and others headquartered in the Bay Area. Other tech hubs in the US are much more diverse.
For all you know the entire team that worked on this project could've been black. Or zero of them could've been white etc.
We can and should have a conversation about training sets and ML ethics. Resorting to unprovoked racist attacks is quite a counterproductive approach imo.
Words are fine, but person names or abbreviations will often use the English pronunciation which can be inexact or even incomprehensible.
For example: In Brazil, the major roadways are named with the initial code of the state, or BR if it's a federal road, and a bunch of numbers. So BR-101, for example. These are never named in full, so someone reading "BR-101" would not say "Brasil 101", they would say "B R 101".
There is a state called Santa Catarina, with initials SC, but Google decides that we somehow get transported to the United States somehow, since it reads "SC-406" as "South Carolina 406".
A human would at the very least have the context that SC definitely is not a common abbreviation for South Carolina in Brazil, even if they did not know any state names.
It has gotten better, but even in 2018 it should have been in- excusable to be mispronouncing names that badly.
One of the big issues with relying on AI is training data, and the responsibility for an inadequately trained AI model falls on the people who judged it ready for use, but that doesn't mean the outcome of the AI isn't racist. An AI doesn't need to know the sociopolitical history of race to be doing racist things.
Also, I've never heard of a location with Roman numerals in the name. I agree that programming in the context of knowing who Malcolm X is into a map program might be asking a bit much, but I think an "easier" implementation (reading them as letters) would have also done the right thing.
At least in Europe, extremely common. In Paris you have Boulevard Henry IV, in Lyon the street named after the same king. There's thousands of kings and queens and many more roads named after them. There's also schools, buildings and such named after them.
Ways to actually fix the problem:
1. Probe the decision boundary between these two classes in your training and test sets. I.E. look for the humans closest to being misclassified as primates. You could probably quickly get a sense if you are near/at risk of making this misclassification. You may also possibly find 1 or 2 mislabeled examples that are throwing things off and can be corrected.
2. Boost your training set by labeling more unlabeled images that are near this decision boundary.
So my point was just: this was an issue that could have been anticipated because it has already happened before. It is unfortunate that this issue came up when there are things you can do (such as the steps above), which I believe could have totally squashed this issue with enough iteration.
I can't know for sure that this is not what the Facebook team did. If they did take care to try and avoid this harmful model confusion, then I would be very surprised given my experience with deep learning and computer vision.
At a minimum, it would have been pretty trivial and a low impact to users to remove the primate class if they didn't have the time/bandwidth to really investigate this more thoroughly as I've described.
You can try to argue "hey no one should get upset about this confusion because computer vision is hard and mistakes happen", but I don't think that's a very solid argument either given the long and relatively recent history of racists misclassifying a particular subset of humans as primates.
They were careful, otherwise they wouldn't have labeled it "primates", but e.g. "monkeys" instead.
https://www.ecupatria.org/2020/11/24/the-primate-of-church-o...
This is a very obvious example of how bad it can be. Regular complaints from people about ai recommendation engines and most other similar ai driven tagging systems exist.
What i wonder though is on a more subtle level, how these ai categorizers are quietly altering our perceptions of things and the world?
We notice when it's something obvious like the story in the OP, but how many quiet little mistakes do we just ignore or not notice that on a subconcious level are changing the way we think about or classify things.
More and more it seems like society and all things are being neatly placed in little boxes, where they're filed, stamped, indexed, and numbered by fancy algorithms operating without much human oversight, until something like this happens.
How much of this is subconciously affecting human perception of the world?
Huh. All the other incidents the NY Times article mentions come with links, but this one (that the article is ostensibly about) doesn't. Odd.
I'm guessing it ingested the Daily Mail's comment sections.
On the other hand, I think there's a semantic difference between your two cases. I'd argue that treating a woman as an "object" is a different sense of the word to how women (and men and teapots) are literally "objects".
Or is the recommendation logic completely insulated from outside influence?
Here's the full video. https://www.facebook.com/watch/?v=2683336318580365
Inference tasks isn’t like software testing where the states are well defined.
0. https://www.theverge.com/2018/1/12/16882408/google-racist-go...
It's like launching a rocket ship that you can't test in a physical simulation first. Something is going to go wrong that can't be predicted because we lack the understanding, but this is seen as an acceptable risk.
I expect you'd get better results if you allowed the system to call humans “primates”, then accept “human primate” as “human” when parsing the output. (That is, leave the “is_primate” output line floating while training on pictures of humans.) I don't know whether that would work, though.
We live in a world when everybody is offended by nothing. The problem is that nobody should be offended by being called a Gorilla. In the same way as nobody is offended by being called an eagle or a wolf. Is a wonderful animal, smart, strong, protective and gentle. What if some idiots used the term pejoratively five generations ago? We know better. Societies can change.
If white people is not being classified as primates, the algorithm should be corrected so they are. Not fixed excluding black people from humanity.
People should be educated also to understand that an AI algorithm is returning probability, not truth
That's not to say, of course, that there's nothing to be done. People who aren't white have historically been laughably underrepresented in computer vision datasets, and while that's been changing, I gather that representation still isn't where it needs to be.
Corporate PR about anything that isn't concrete things which misleading statements would be both easily revealed and have adverse (e.g., securities-laws-related) consequences is almost 100% lip service, so, yeah, that's a safe assumption, anyway.
https://www.credera.com/insights/racial-bias-in-machine-lear...
If only it was explained in a conveniently linked article.
EDIT: pardon me, what I wrote is wrong. We are all primates after all :)
I‘m sure I could take a picture of myself that made me look like a teapot, if I just find the right crop, positioning, etc.
Cry racism too many times, and it will lose its strength when some actually racist does happen.
Be careful!
But next to a black person it's totally different.
Never mind history, i knew it was bad. But I wonder about the effects about "putting it to the center of attention" all the time.
Can we fix and name problems that are based on culture and not on race anymore.
For example:
What would be the consequences of having a statement "i don't know of any problems with Asians who migrated to my country". While i say Asians, it's probably that the culture of them probably has less conflicts?
Eg. In our prison ( not US), there is a over representation of certain foreign cultures, according to the numbers.
How can we tell if that observation is factual correct and there is a problem with different cultures or if it's a consequence of racism.
If that observation would be correct and there is a problem. How can we treat it, since we are not allowed to name it.
( Just to be sure, i am no racist. I'm just wondering out loud and it's very hard to find a place to properly discuss something like this. Which is part of the problem, no?)
The title has obviously been editorialized for outrage.
If you find a picture of my white face anywhere, feel free to put a "primate" caption under it.
LIME - Local Interpretable Model-Agnostic Explanations comes to mind.
As far as I do understand the classification was the tagging 'primate' on a video of people with different color of skin and different facial features.
The interpretation, that our Lady AI meant harm to the people with dark skin color with tagging them as primates is a loading of the viewer.
By the way,´a great new opportunity for cyber lawyers and machine inquisitors.
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.