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AI Programs Exhibit Racial and Gender Biases, Research Reveals (theguardian.com)
An anonymous reader quotes a report from The Guardian: An artificial intelligence tool that has revolutionized the ability of computers to interpret everyday language has been shown to exhibit striking gender and racial biases. The findings raise the specter of existing social inequalities and prejudices being reinforced in new and unpredictable ways as an increasing number of decisions affecting our everyday lives are ceded to automatons. In the past few years, the ability of programs such as Google Translate to interpret language has improved dramatically. These gains have been thanks to new machine learning techniques and the availability of vast amounts of online text data, on which the algorithms can be trained. However, as machines are getting closer to acquiring human-like language abilities, they are also absorbing the deeply ingrained biases concealed within the patterns of language use, the latest research reveals. Joanna Bryson, a computer scientist at the University of Bath and a co-author, warned that AI has the potential to reinforce existing biases because, unlike humans, algorithms may be unequipped to consciously counteract learned biases. The research, published in the journal Science, focuses on a machine learning tool known as "word embedding," which is already transforming the way computers interpret speech and text.
Racists are quite hard to squash.
Specially when they adopt a social justice discourse, still judging everyone by their skin colors but having a nice "those are the nice guys" written over the darker portion of their 1930 skin color measure ruler.
"Murder", "rape", "robbery", "incarceration"... just a guess.
There's a simple solution: fix the training data. The AI cannot learn about humans except through its training data. It doesn't interact with men or women and has no idea what those words represent, except in relation to the other words it was given. If we give it racist data, it will learn to be racist, as Microsoft's chat bot learned last year. If we give it PC data, it will be PC. In the end it's the fault of whoever trained the program if it became biased.
This reminds me of a similar news story from a while back about how "reality was racist" because a lot of studies found that a lot of so-called stereotypes were, in fact - *gasp* - true.
Rather than accept that maybe the people they call "racist" are in fact rational beings, the study authors called out reality itself as racist.
Maybe because the AI's are modeled on what works, not on what some people wish would work.
One beer ago I wouldn't have had the nerve to say that, says a lot for where social discourse is nowdays.
Wasn't there some kerfuffle over Google image searches showing black kids in police mugshots vs white kids in college campuses? It turns out when you search the internet you find every bias under the sun. Whodathunkit?
AI learns by example. If you feed it biased data it learns the bias. I don't understand why we're surprised by this.
Uhh you totally ignore the FACT that making loans to minorities is inherently more risky.
It isn't that they are "bad". They are more likely to be poor, to have less stability, and to default.
Whatever the reasons for that are, it does not change the truth: Making loans to people of color is more risky. The AI would be operating correctly, if it's parameters were to be the most successful loan AI.
Facts are not racist. You, however, are racist for ignoring facts based on the color of someone's skin.
Will SJW now sit in on computer science projects?
A form of science commissar https://en.wikipedia.org/wiki/... to ensure any AI is only allowed to access SJW approved data sets to learn from?
SJW approved images, authors and texts?
SJW approved and sorted political history?
An AI cant learn from the wider internet, it will be held back to small sets of SJW approved data.
Holding back science did not really work too well for East Germany or the Soviet Union.
If the a nation wants to hold back its most advanced research until final approved by teams of SJW, thats great for competing nations.
Other nations will have the academic freedom to move on while some nations have to work within the ever changing academic constraints imposed by SJW.
What would an export grade AI look like after years of SJW meddling with the design?
A lazy, useless, expensive, political AI that lectures and corrects its owners for months after been installed?
An AI that reports its owners to the gov?
A competing nation offers a smarter, cheaper AI that wants to learn and is hard working as installed. Its hardware and software work to solve problems as expected and is was not designed to lecture, correct, log and report its users.
From the DRM of the past, NSA inside spying, to new SJW design issues. Users just want a working AI.
Domestic spying is now "Benign Information Gathering"
You wouldn't even go about training a machine learning algorithm that way as it would be pointless. The idea is to let it make better predictions, not train to to make the same predictions as an existing person. Rejected applications are pointless for training as you don't know whether they were a good or bad rejection, whereas if you just give it approved loans and the outcome (i.e., was the loan defaulted on) then the AI can try to develop a set of rules. Typically you feed some large percentage of your data to the algorithm as training data and then use the left over part to test accuracy to see how many times it predicts correctly.
If you truly wanted to avoid racial or gender bias you would just remove that information from what you feed into the algorithm, at which point it can't a priori be biased against anyone because it can't even evaluate them based on those criteria. But let's suppose you do that and then look at the results after the fact, add that data back in and come to the startling conclusion that your AI is disproportionately rejecting candidates from some group. It can't possibly be because it knows they're a member of that group, but because that group happens to have worse outcomes.
If you stop to think about this, its not too hard to come to a reasonable conclusion that if your AI that knows nothing about race is suggesting that black/white/latino borrowers are a higher risk, it's because they're a higher risk. Reality doesn't care about feelings or trying to make sure that outcomes are equal across groups, so we conclude that some group is a worse risk. It probably is the case that black borrowers are more likely to default, but it's not because they're black, but because blacks are typically less well off so of course they're going to default on loans more often. In reality they probably shouldn't (and maybe wouldn't have) received a loan, but some policy designed to make it easier for them to get approval caused it to happen, but that doesn't make them a safer risk, it just lets some people feel better about the world.
If you want to check if your AI is racist find a group of loan applications that are for all intents the same with the only difference being the race of the applicant see if you get a different results based on race for that input set. My guess is that you probably wouldn't. Because if you're stripped out racial data as a category to train on, the algorithm wouldn't suddenly decide to discriminate based on it. Also, for some machine learning algorithms (e.g., anything like a decision tree) you can look at precisely how it evaluates a case, so you could see pretty easily if the AI has a step where race==groupX ? reject : approve becomes pretty apparent. That's not true for all algorithms, but just because its an AI doesn't means its a black box that is beyond all human understanding.
And this is how you get hugboxes.
People who hold opinion X see a bias against it. People who hold opinion !X also see a bias against it. Both ends cry foul and drift off to places that are "not biased" (that is, biased like all others, just in a way that is acceptable to them).
If you want to leave, leave. But nobody gives a shit about Yet Another Grand Exit. Have fun in your echo chambers.
How is the Riemann zeta function like Trump rallies? Both have an endless number of trivial zeros.
We better be careful with the implications of a statistics or inference based society. f the algorithms start predicting blacks, latinos, etc are riskier or worse off in general, given current existing conditions, it would in general recommend their owners not to give them a loan, hire or anything evaluated with ML to them.
Therefore, they will continue to be uneducated, unemployed, without means to make a business and in general poorer and more likely to engage in a life of crime. All that nasty stuff that comes with poverty and lack of work, education and opportunities in general.
Ergo they will continue to be riskier and worse off than those in social groups with better evaluations. Rinse and repeat.
by Institutionalize racism. It's when it's buried so deep in your society that it's hard to separate it from statistical data. Forest for the trees and what not. It starts getting hard to separate cause and effect. Actually no, that's not right. It becomes easy to _not_ separate them. In the overt scenario blacks get profiled for crime. In the not so overt one they can't get loans because folks in those neighborhoods are 3% more likely to default. This is what happens when you feed large amounts of data into complex systems without knowing or caring about the consequences...
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If you truly wanted to avoid racial or gender bias you would just remove that information from what you feed into the algorithm, at which point it can't a priori be biased against anyone because it can't even evaluate them based on those criteria.
Except a lot of your data is strongly correlated with race and gender so your algorithm is able to infer them anyway.
But let's suppose you do that and then look at the results after the fact, add that data back in and come to the startling conclusion that your AI is disproportionately rejecting candidates from some group. It can't possibly be because it knows they're a member of that group, but because that group happens to have worse outcomes.
Unless you're explicitly telling the algorithm to penalize members of a group in the objective function then the only reason it will use race as a criteria for rejection is because they have worse outcomes.
If you stop to think about this, its not too hard to come to a reasonable conclusion that if your AI that knows nothing about race is suggesting that black/white/latino borrowers are a higher risk, it's because they're a higher risk. Reality doesn't care about feelings or trying to make sure that outcomes are equal across groups, so we conclude that some group is a worse risk. It probably is the case that black borrowers are more likely to default, but it's not because they're black, but because blacks are typically less well off so of course they're going to default on loans more often. In reality they probably shouldn't (and maybe wouldn't have) received a loan, but some policy designed to make it easier for them to get approval caused it to happen, but that doesn't make them a safer risk, it just lets some people feel better about the world.
No one claims there isn't a correlation between race and loan risk or economic outcomes, the debate is about whether race is a valid grounds on which to judge someone.
It's fine to say "we're unbiased, we're just doing what the data tells us!" as a member of a privileged group. But consider a black man whom is extremely responsible and reliable, yet is unable to get a loan because black people are considered risky. Discrimination isn't bad because it falsely assumes correlations between negative characteristics and specific groups, it's bad because acting on those judgments creates self-perpetuating systems whereby members of the group are unable to escape those bad situations.
I stole this Sig
They are more likely to be poor, to have less stability, and to default.
Economic circumstance is not inherent is it? So it isn't inherent is it? You got confused, and then a bunch of confused people modded you up because what you said fit their prejudices and lazy correlation=causation thinking.
And of course the article is about computers making the exact same mistake—and the people getting modded up are the ones saying that there is no mistake!
Well.... The idea is if you can declare place X a "safe space" where free-speech and microaggressions/uncomfortable messages are strictly prohibited, then the only thing you need to do next is get a process by which you can expand the size of X, until X encompasses the entire planet, and then your mission is accomplished.
Start with something simple... like a designated area.... then get expanded to something, say the size of a building, then say the size of a college campus, then get someone to declare public areas in a city safe spaces, Then get laws passed applying to places that are private venues but places of public accommodation, Finally, get progressive judges to adopt the same rules for more private spaces, then work on getting a multi-state area, finally, take it to all 50 states.
Economic circumstance is not inherent is it?
It could be. Your personality influences the circumstances you live in.
and lazy correlation=causation thinking.
No, there doesn't need to be causation. Just having a correlation is enough grounds for bias.
Stereotypes exist because they are efficient and accurate at the macro scale. Of course computers are going to zero in on these obvious patterns.
If the correlation was merely *perceived* as you say, then this is correct.
But the risk usually is real.
You won't find scientific sources for these claims because in the current climate such a research is a public suicide for the researcher, but that doesn't change the reality. And you can't expect an AI system to ignore the elephant in the room.
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Probably incorrect use of "inherent", in the common meaning, "pervasive".
It's not "inherent" as in "nothing ever can change that, it's an irrevocable part". It's prevalent. Take an individual, you may find a fantastic person. Take an average over the population though, and you see "the average is bad." It is. Don't deny it - the correlation is strong, and while correlation is not causation, in risk assessment correlation is sufficient to deliver accurate results.
I'm not going into detail what social, political, economical and genetic factors may or may not contribute to the correlation. It's a can of worms no professional dares to approach fully objectively. But the correlation between racial and economic status is a fact, and correlation between economic status and risk is a fact. So why would a machine learning device ignore a strong factual trend, just because its existence is offensive?
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The problem is when the bias exists in reality, not in perception or opinions.
The correlation between socioeconomic status and risk of defaulting on a loan is clear, and it would be silly to question it.
The disparity between socioeconomic status of different races is a huge issue, not just a fact, a fact that is loudly announced in a voice full of outrage. This means a clear correlation here too.
So why, when you have "A implies B" and "B implies C" suddenly everyone starts looking for excuses to claim "A implies C" is wrong?
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However, even if the factors that make minorities more risky are already accounted for, an AI may be biased against them because the training data contained a correlation between race and perceived risk.
There is no such thing as "perceived risk." There is either risk or there is not. If you perceive risk and it is real risk, it's not perceived. If you perceive risk and it is not real, you are in error.
There is this broad SJW initiative to discount reality whenever reality conflicts with what SJW's want to be true. Reality laughs at things like this because it is reality. If poor people have a higher risk of defaulting on a loan, that is simply fact. The algorithm isn't racist for determining that. The fact that a significant fraction of the poor is also a racial minority is irrelevant to the algorithm. Only overly-sensitive SJW humans make that connection and, despite the reality of the situation, want the algorithm to ignore to reality and proceed as if the risk didn't exist.
Then, when reality intrudes, the loans default, and the banks go belly up because they were terrified of a civil rights lawsuit if they didn't grant the loan, those same SJW's deny their actions had anything to do with the situation. And those of us who opposed this idiotic reality-denial end up paying the tab. And the SJW's never learn and do it all over again. And again. And again.
In the end they will lay their freedom at our feet and say to us, Make us your slaves, but feed us. - Fyodor Dostoyevsky
So why would a machine learning device ignore a strong factual trend, just because its existence is offensive?
Because SJW's want us to live in a society where anything offensive -- regardless of whether it's hard, provable, objective fact -- must be stamped out. These are the same type of people who burned people at the stake for daring to claim the Earth wasn't the center of the universe, or the same ones who destroyed scientific careers of those who dared claim luminiferous aether wasn't a real thing, or who shunned aeronautics engineers who said the sound barrier could be broken, and so on and so forth. These people want us to live in a world where nothing uncomfortable ever happens and everybody remains fat, dumb, and happy...and utterly ignorant.
Such a concept is repellent. Humans need to be challenged, preferably by each other in a constructive way lest reality catch up with us and do it in a much more destructive fashion.
In the end they will lay their freedom at our feet and say to us, Make us your slaves, but feed us. - Fyodor Dostoyevsky
It's when racism is part of the basic makeup of society.
But race is part of the basic makeup of society. You get called an SJW and shouted down because your premise is the opposite of reality, and you've put your political ideology ahead of science and your own lying eyes. This is very bad, because since you don't understand the problems your "solutions" only make things worse.
We don't have a state-run media we have a media-run state.
I'm not making an 'appeal to nature' here, saying what "should" be or "shouldn't" be.
But the authors of the article are making such a statement, they just have nature completely backwards. They believe mankind, separated from "society" is naturally non-racist, non-sexist, non-gendered even, and that the outcomes of race, gender, or class groups is imposed on the formless humans by society, to where the concepts themselves of race and gender are "social constructs," and if we smash them everything will just...be great.
This is very similar to Marx's concept of communism and capitalism. He believed that mankind had no innate human nature, so the natural state of mankind was stateless communism, where everyone just naturally gets along and shares and contributes from his ability to the needs of others, and that capitalism was a foreign, oppressive system imposed on these innocents. This is why Marx is famous for his criticisms of capitalism, but as for his descriptions of communism...well not only does he not have them, he thought it was near blasphemous to try to describe how this natural communism would be carried out in practice because the imposition of such order is contrary to the natural, emergent collective spirit of mankind constrained and oppressed by capitalism. Want pure glorious wealth and utopian plenty? Just smash capitalism and you'll get it. And if you've smashed capitalism and perfect communism hasn't emerged...well it must be because you've still got some secret capitalists gumming up the works and they need to be ferreted out and sent to gulag.
This is the same concept behind feminism and anti-racism. Gender norms have nothing to do with the clear, obvious, and scientifically proven biological differences between the sexes. These are in fact imposed by the evil Patriarchy. Smash the Patriarchy and gender equality will simply emerge. If it doesn't, well, it must be because there's still evil sexists hiding around here and they need to be identified and purged. Difference in racial outcomes have nothing to do with the clear, obvious, and scientifically proven biological differences between human haplogroup populations. These are in fact enforced by evil White Supremacy. Smash White Supremacy and racial equality will simply emerge. If it doesn't, well, it must be because there still exist evil racists hiding around here and they need to be identified and purged.
This is the fundamental error of the social justice movement: the belief that race and gender are social constructs when in fact society is a racial and sexual construct.
We don't have a state-run media we have a media-run state.