7 ms·

This isn't a problem with diversity. Everybody knows how to pronounce Malcom X. And it's not like just because a google engineer was black that he was like "oh,

This isn't a problem with diversity. Everybody knows how to pronounce Malcom X. And it's not like just because a google engineer was black that he was like "oh, let's try and see if Malcom X is pronounced correctly because he's black and I'm black too". This only happens in white people's brain.

sinyug 5y ago
> This only happens in white people's brain. 'Eleven Jinping': Indian TV fires anchor over blooper.[1] [1] https://www.bbc.com/news/world-asia-india-29274792 https://www.bbc.com/news/world-asia-india-29274792
- travisporter 5y ago
  To play devil advocate, maybe the station fired her for ignorance of current events.
  - sinyug 5y ago
    > maybe the station fired her for ignorance of current events. That would be a valid reason, but I suspect a more culturally appropriate one: loss of reputation. We are sensitive to that. My point was this isn't something that only goes on in 'white' brains but more of a cultural issue. Most people in the West are incapable of pronouncing Asian names. I don't see people making a big issue out of it.
    - atkailash 5y ago
      If you don’t speak the language of course you’re going to mess up names. But having the self awareness to ask before butchering it, or asking to be corrected, is a human issue in general not specific to any particular heritage
tcmart14 5y ago
I don't know if I 100% align up with how you stated it, but yea, its a matter of training data set. I don't think these companies have published their training data set. But thinking back on the issue with asians and facial recognition on Apple's face ID. If they just choose 100 people at random, based off US statistics, 5-6 of those 100 people would have been Asian. And that reflects the 5.7 percent of the population is Asian. And we probably all agree 5-6 people is not a sufficient data set, but picking 100 people at random would be a pretty easy assumption to make for making a data set. So yea, I think it is an issue with generating a data set and not hitting a sufficient amount of test cases. Because in this instance, asians would be an edge case where creating a small data set to train an algorithm on with a group with a lower representation in the population.
- JCharante 5y ago
  I wonder what the datasets of companies like Xiaomi look like. FaceID always worked for me, so it seems like it works for non-asian faces.
  - tcmart14 5y ago
    Maybe they took more caution to their data set. I think the only way we would know is if they publish their sets or how they built them. But I was just highlighting maybe one possible case that Apple could have generated their training set, just grab 100 people in America at random.
jessaustin 5y ago
In what universe is "Ten" a more common pronunciation of "X" than "X"? You might have an argument for "II" or "III", but I'll be shocked if any street in USA is named after the tenth generation of really unimaginative namers.
- handmodel 5y ago
  Do you think Google is having someone go through the tens of thousands of street names? Or do you think they had a team (on a completely different project or perhaps company) write a text to speech function that wasn't well suited for directions. Streets have lots of numbers after all. People frequently have numbers in their name.
  - jessaustin 5y ago
    I could see that for Google Maps v1.0. I think we're past that point now. There's no reason they should still be using libraries suited to parsing the names of forgotten European monarchs.
    - SllX 5y ago
      They’re neither forgotten, unused, nor is it a nomenclature used exclusively by Royals; nor are all the Royals that use this fashion dead or out of power.
- scrollaway 5y ago
  "(name) (Roman numeral)" is a much more commong occurence than "(name) (Single letter spelled out)". So to answer your question, this one. This universe. And no, nobody "programmed this". I don't know how anyone of decent technical competence can think that. The workaround has likely been hardcoded in some way, if anything. How can you possibly think that someone at google decided it would be funny to program, on purpose, a wrong pronunciation of Malcolm X, and even more so SOMEHOW NOT GET CAUGHT. By the way, there are an incredibly large amount of streets in the US alone, so no it would not be surprising. Here in Europe we have kings going up into the twenties of their name, and those ML models have to pronounce those too. I just... I just fucking can't, with comments like yours, anymore.
elmomle 5y ago
The implication is that a black person would be more likely to recognize the inherent flaw in automatically interpreting "X" as "10", and in all honestly that's probably true. It isn't a matter of testing, it's a matter of having people with a diverse set of cultural perspectives in the room when decisions like that are made to begin with.
- giancarlostoro 5y ago
  Diversity doesnt guarantee you automatically catch or account for edge cases. As a minority I am disturbed by some of the odd takes people have about diversity. Theres thousands upon thousands of roads. Unless you have a QA team test directions to every road in the country you wont ever catch the issue with a road named Malcom X. You don’t even have to be ‘diverse’ to know who that is.
  - wpietri 5y ago
    It doesn't guarantee it, but it helps. I personally have gotten bugs fixed at Google. How? Because I, a white man, spotted a bug, cared about it, and talked to white men of my acquaintance at Google who had enough power to get things done. How did I know them? From other tech companies created, run, and majority staffed by other white men. Why am I in these networks at all? Well, my dad was a software developer and he introduced me early on. How did he get his start? His dad, an insurance company exec, brought him in to deal with this newfangled computer thing they had just gotten. That was in Milwaukee in the mid-1960s. I promise you that although Milwaukee had a significant black population, exactly zero of them were insurance company executives in the mid-1960s. So what Allie Bland knew when she wrote her tweet was that she did not have any connection to Google where she might be able to get a to-her glaringly obvious pronunciation issue fixed. That in her estimation no black person did. And I see no reason to think she was wrong.
wpietri 5y ago
You realize that the person I'm quoting isn't white, right? Let's separate the general case from the specific. Generally, we know that representation in the people who make things changes what they make. This is obvious and undeniable. For example, look at ASCII vs Unicode. The Chinese invented movable type 500 years before Gutenberg, so it's not like the idea of printing non-roman characters was novel. In the age of telegraphy, Europeans developed encodings that included umlauts and accents; by 1851 they were merged into International Morse Code. So why in 1963 was ASCII codified without any of that? And why did that become the dominant standard for an extended period? Because it was mainly Americans in the rooms where the technology was being created. Similarly, we know that standard color films were developed by white people to represent white people well: https://www.vox.com/2015/9/18/9348821/photography-race-bias https://www.vox.com/2015/9/18/9348821/photography-race-bias And we all know how this happens. It's the same reason a lot of open-source software is good for a developer audience, not an end-user one: making things means iterating on them until they're good enough for the people involved. That's the general case, so let's return to the specific case. If you want to prove that ML systems doing racist stuff has nothing to do with who made it, then you can't just handwave it away. You have to show why that specific project was set up so carefully and so well that it would avoid the natural pitfalls of any technology project. And then despite that it went on to do racist stuff. For reasons that you'd then have to explain.
- darepublic 5y ago
  Considering the adversarial attacks that image recognition systems are vulnerable to, perhaps even a well trained system could be induced to produce inappropriate results of one sort or another. Perhaps the training set and algorithm for the model should just be publicly available so that people can scrutinize the data and figure out incrementally how to avoid most biases or guffaws to a generally accepted level.

sinyug 5y ago

> This only happens in white people's brain. 'Eleven Jinping': Indian TV fires anchor over blooper.[1] [1] https://www.bbc.com/news/world-asia-india-29274792 https://www.bbc.com/news/world-asia-india-29274792

travisporter 5y ago
To play devil advocate, maybe the station fired her for ignorance of current events.
- sinyug 5y ago
  > maybe the station fired her for ignorance of current events. That would be a valid reason, but I suspect a more culturally appropriate one: loss of reputation. We are sensitive to that. My point was this isn't something that only goes on in 'white' brains but more of a cultural issue. Most people in the West are incapable of pronouncing Asian names. I don't see people making a big issue out of it.
  - atkailash 5y ago
    If you don’t speak the language of course you’re going to mess up names. But having the self awareness to ask before butchering it, or asking to be corrected, is a human issue in general not specific to any particular heritage

tcmart14 5y ago

I don't know if I 100% align up with how you stated it, but yea, its a matter of training data set. I don't think these companies have published their training data set. But thinking back on the issue with asians and facial recognition on Apple's face ID. If they just choose 100 people at random, based off US statistics, 5-6 of those 100 people would have been Asian. And that reflects the 5.7 percent of the population is Asian. And we probably all agree 5-6 people is not a sufficient data set, but picking 100 people at random would be a pretty easy assumption to make for making a data set. So yea, I think it is an issue with generating a data set and not hitting a sufficient amount of test cases. Because in this instance, asians would be an edge case where creating a small data set to train an algorithm on with a group with a lower representation in the population.

JCharante 5y ago
I wonder what the datasets of companies like Xiaomi look like. FaceID always worked for me, so it seems like it works for non-asian faces.
- tcmart14 5y ago
  Maybe they took more caution to their data set. I think the only way we would know is if they publish their sets or how they built them. But I was just highlighting maybe one possible case that Apple could have generated their training set, just grab 100 people in America at random.

jessaustin 5y ago

In what universe is "Ten" a more common pronunciation of "X" than "X"? You might have an argument for "II" or "III", but I'll be shocked if any street in USA is named after the tenth generation of really unimaginative namers.

handmodel 5y ago
Do you think Google is having someone go through the tens of thousands of street names? Or do you think they had a team (on a completely different project or perhaps company) write a text to speech function that wasn't well suited for directions. Streets have lots of numbers after all. People frequently have numbers in their name.
- jessaustin 5y ago
  I could see that for Google Maps v1.0. I think we're past that point now. There's no reason they should still be using libraries suited to parsing the names of forgotten European monarchs.
  - SllX 5y ago
    They’re neither forgotten, unused, nor is it a nomenclature used exclusively by Royals; nor are all the Royals that use this fashion dead or out of power.
scrollaway 5y ago
"(name) (Roman numeral)" is a much more commong occurence than "(name) (Single letter spelled out)". So to answer your question, this one. This universe. And no, nobody "programmed this". I don't know how anyone of decent technical competence can think that. The workaround has likely been hardcoded in some way, if anything. How can you possibly think that someone at google decided it would be funny to program, on purpose, a wrong pronunciation of Malcolm X, and even more so SOMEHOW NOT GET CAUGHT. By the way, there are an incredibly large amount of streets in the US alone, so no it would not be surprising. Here in Europe we have kings going up into the twenties of their name, and those ML models have to pronounce those too. I just... I just fucking can't, with comments like yours, anymore.

elmomle 5y ago

The implication is that a black person would be more likely to recognize the inherent flaw in automatically interpreting "X" as "10", and in all honestly that's probably true. It isn't a matter of testing, it's a matter of having people with a diverse set of cultural perspectives in the room when decisions like that are made to begin with.

giancarlostoro 5y ago
Diversity doesnt guarantee you automatically catch or account for edge cases. As a minority I am disturbed by some of the odd takes people have about diversity. Theres thousands upon thousands of roads. Unless you have a QA team test directions to every road in the country you wont ever catch the issue with a road named Malcom X. You don’t even have to be ‘diverse’ to know who that is.
- wpietri 5y ago
  It doesn't guarantee it, but it helps. I personally have gotten bugs fixed at Google. How? Because I, a white man, spotted a bug, cared about it, and talked to white men of my acquaintance at Google who had enough power to get things done. How did I know them? From other tech companies created, run, and majority staffed by other white men. Why am I in these networks at all? Well, my dad was a software developer and he introduced me early on. How did he get his start? His dad, an insurance company exec, brought him in to deal with this newfangled computer thing they had just gotten. That was in Milwaukee in the mid-1960s. I promise you that although Milwaukee had a significant black population, exactly zero of them were insurance company executives in the mid-1960s. So what Allie Bland knew when she wrote her tweet was that she did not have any connection to Google where she might be able to get a to-her glaringly obvious pronunciation issue fixed. That in her estimation no black person did. And I see no reason to think she was wrong.

wpietri 5y ago

You realize that the person I'm quoting isn't white, right? Let's separate the general case from the specific. Generally, we know that representation in the people who make things changes what they make. This is obvious and undeniable. For example, look at ASCII vs Unicode. The Chinese invented movable type 500 years before Gutenberg, so it's not like the idea of printing non-roman characters was novel. In the age of telegraphy, Europeans developed encodings that included umlauts and accents; by 1851 they were merged into International Morse Code. So why in 1963 was ASCII codified without any of that? And why did that become the dominant standard for an extended period? Because it was mainly Americans in the rooms where the technology was being created. Similarly, we know that standard color films were developed by white people to represent white people well: https://www.vox.com/2015/9/18/9348821/photography-race-bias https://www.vox.com/2015/9/18/9348821/photography-race-bias And we all know how this happens. It's the same reason a lot of open-source software is good for a developer audience, not an end-user one: making things means iterating on them until they're good enough for the people involved. That's the general case, so let's return to the specific case. If you want to prove that ML systems doing racist stuff has nothing to do with who made it, then you can't just handwave it away. You have to show why that specific project was set up so carefully and so well that it would avoid the natural pitfalls of any technology project. And then despite that it went on to do racist stuff. For reasons that you'd then have to explain.

darepublic 5y ago
Considering the adversarial attacks that image recognition systems are vulnerable to, perhaps even a well trained system could be induced to produce inappropriate results of one sort or another. Perhaps the training set and algorithm for the model should just be publicly available so that people can scrutinize the data and figure out incrementally how to avoid most biases or guffaws to a generally accepted level.