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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.

31 of 384 comments (clear)

  1. from the biased report... by turkeydance · · Score: 4, Interesting

    "And the AI system was more likely to associate European American names with pleasant words such as “gift” or “happy”, while African American names were more commonly associated with unpleasant words." ...what were those unpleasant words?

    1. Re:from the biased report... by Digital+Avatar · · Score: 5, Insightful

      "Murder", "rape", "robbery", "incarceration"... just a guess.

  2. Simple solution by djinn6 · · Score: 3, Insightful

    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.

    1. Re:Simple solution by lorinc · · Score: 3, Interesting

      Just like for regular humans. People almost never question the religion there were born with, or views on races and culture for that matter.

    2. Re:Simple solution by msauve · · Score: 5, Insightful

      As soon as you start deliberately manipulating the training data, your're introducing your own bias.

      Right-handed people are dexterous, lefties are sinister.

      --
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    3. Re:Simple solution by epyT-R · · Score: 4, Informative

      Simple. 'decolonize' it.
      https://www.youtube.com/watch?...

      SocJus taken to its 'logical' conclusion. Reality is bigotry.

    4. Re:Simple solution by laddiebuck · · Score: 3, Informative

      "Simple". The ML community is very aware of this problem, but sanitizing real-world data that may be shaped by subtle biases is really, really hard. You'd need a dedicated sociology PhD involved in every ML research project - a ludicrous load - and even then you wouldn't catch everything. This is a Hard Problem to be aware of for a long time to come.

    5. Re:Simple solution by religionofpeas · · Score: 3, Insightful

      If the AI was clever enough to understand that bias is a problem, how it works and how to self-correct, it would be able to get past the bias in the training data.

      The bias represents itself as a pattern in the training data, as a result of patterns in reality. Why should the AI consider some patterns to be "a problem" ? What's your criterium ?

    6. Re:Simple solution by meta-monkey · · Score: 3, Insightful

      You'd need a dedicated sociology PhD involved in every ML research project

      This just reminds me of the political officers in the Red Army, who accompanied each unit to make sure everyone was good communist.

      --
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  3. Human language is pretty biased. by king+neckbeard · · Score: 3, Informative

    Most spoken languages exhibit a lot of bias. For example, Deutsch means people or folk, and that lightly implies what is not Deutsch is not people. A lot of languages have that mindset, and it's not surprising. Language evolved during times when people had values we disagree with.

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  4. Re:Uh, no. by Anonymous Coward · · Score: 3, Insightful

    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.

  5. Or rather... by PatientZero · · Score: 5, Interesting

    AIs could incorporate existing biases.

    Say you train an AI that will accept or reject loan applications by giving it a stack of previous loans. If the human loan officers were biased against minorities—rejecting otherwise acceptable applications—that AI may end up doing the same. This bias is much easier to detect in human behavior but less so with AI which can't explain why it made any particular choice or even what its criteria are.

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    1. Re: Or rather... by Anonymous Coward · · Score: 3, Insightful

      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.

    2. Re:Or rather... by alvinrod · · Score: 5, Insightful

      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.

    3. Re:Or rather... by guises · · Score: 3, Interesting

      You're missing what the parent is saying - you can't just tell the AI to ignore race/gender, it's baked into how we talk and act. Telling the AI to ignore gender, for example, would require finding every last thing which correlates with gender (basically impossible) and telling the AI to ignore those (which would mean cutting out large portions of what it needs to function).

      E.g.: Your AI makes a statement, "Women be like this, while men be like this." And you tell your AI, "No AI, bad."

      So your AI rethinks it and comes up with another statement, "People with vaginas be like this, while people with penises be like this." And you tell your AI, "No AI, bad."

      So your AI rethinks it and comes up with another statement, "People named Betty or Veronica be like this, while people named Archie or Jughead be like this." And you tell your AI, "No AI, bad."

      So your AI rethinks it and comes up with another statement, "People who wear makeup be like this, while people who don't be like this." And you tell your AI, "No AI, bad."

      Etc. You could do this forever and you still wouldn't catch them all, they'd just get more subtle.

    4. Re:Or rather... by mysidia · · Score: 3, Informative

      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

      Except the latest interpretation of the Civil Rights Act by the courts is that Disparate Impact counts the same as direct discrimination. If your company adopts a policy that has a negative disparate impact on different groups, then it's deemed in violation of the law, so even if your AI is making a correct decision, it may be deemed racially biased by the courts, and require your company modify its policies to compensate for the bias.

    5. Re: Or rather... by ShanghaiBill · · Score: 4, Interesting

      Making loans to people of color is more risky.

      It depends on the color. Asian Americans have lower default rates than whites.

    6. Re:Or rather... by Citizen+of+Earth · · Score: 3, Insightful

      Stereotypes exist because they are efficient and accurate at the macro scale. Of course computers are going to zero in on these obvious patterns.

    7. Re: Or rather... by SharpFang · · Score: 3, Insightful

      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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    8. Re: Or rather... by SharpFang · · Score: 4, Insightful

      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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  6. I'm gonna get so nailed for this :( by Snotnose · · Score: 5, Insightful

    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.

  7. Re: Uh, no. by Anonymous Coward · · Score: 3, Insightful

    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.

  8. Re:Bias bias bias by lucm · · Score: 4, Funny

    Herstory will prove you wrong.

    --
    lucm, indeed.
  9. Re:Ok. Thx, bye by Mal-2 · · Score: 4, Insightful

    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.

    --
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  10. Self fulfilling prophecies... by bettodavis · · Score: 5, Insightful

    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.

  11. This is what folks mean by rsilvergun · · Score: 4, Insightful

    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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  12. Re:Bias bias bias by mysidia · · Score: 4, Insightful

    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.

  13. of course by argStyopa · · Score: 5, Interesting

    ..the begged question is that gender or racial bias and stereotypes are intrinsically "wrong". They are to our 21st century sensibilities, but they served humanity pretty well for millions of years.

    Maybe where you have a society where women ARE primarily concerned with raising children, there are better outcomes than when men raise children or women go off to pursue their careers. Maybe where you have a society where obvious strangers are marginalized and driven away, the remainder ends up more cohesive.
    I'd be curious how these AI biases would develop if 'fed' only native African literature and information.

    I'm not making an 'appeal to nature' here, saying what "should" be or "shouldn't" be.
    One might suggest that, evolutionarily speaking, maintaining a bias is harder than not, assuming no reinforcement. That our language (pretty fundamental to being human, after all) is pervasive with such institutional biases would suggest that there is a value/benefit to such.

    --
    -Styopa
    1. Re:of course by meta-monkey · · Score: 5, Insightful

      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.

      --
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  14. Re:I'm with you by meta-monkey · · Score: 3, Insightful

    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.
  15. Re:SJW to sit in on computer science? by meta-monkey · · Score: 4, Insightful

    What would an export grade AI look like after years of SJW meddling with the design?

    To add on, build an AI in the politicized west and ship it to Japan. They unpack it next to a Japanese-designed AI. They ask the AIs, "is an average Japanese person more intelligent, less intelligent, or the same intelligence as an average Ugandan person?"

    How does that play out? We know what each AI will be required to say. Why would anyone not afflicted with western social justice leftism ever want to buy the American AI?

    --
    We don't have a state-run media we have a media-run state.