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A better format than random Twitter thread where a mob tries to cancel him [0]. You might recognize one name that got really famous not long ago! [0] https://s

A better format than random Twitter thread where a mob tries to cancel him [0]. You might recognize one name that got really famous not long ago! [0] https://syncedreview.com/2020/06/30/yann-lecun-quits-twitter-amid-acrimonious-exchanges-on-ai-bias/ https://syncedreview.com/2020/06/30/yann-lecun-quits-twitter...

IfOnlyYouKnew 5y ago
> You might recognize one name Yes, it’s terrible when people are subject to all sorts of personal attacks based on snark and innuendo.
- elefanten 5y ago
  This is indecipherable to me. Who are you taking a shot at? Is this comment pro-snark or anti-snark? Sarcastic or straight? Who knows.
horrified 5y ago
Horrible story :-( And once again the grievance strategy was successful.
- mrtranscendence 5y ago
  I don't know how folks can be aware of how the exchange went down and say that it was a "successful" "grievance strategy". LeCunn wasn't necessarily in the right here, and it wasn't only Gebru's twitter followers going on the offensive.
  - horrified 5y ago
    Well LeCunn quit Twitter, so it is "one down". That is what I meant by successful. And Gebru's "arguments" weren't even arguments, just "whatever you say is wrong because you are white and don't recognise our special grievances". I personally agree with what he said when he said it is a difference between a research project and a commercial product. No actual harm was done when the AI completed Obama's image into a white person. You could just laugh about it and move on.
    - deleted 5y ago
      [deleted]
    - frozenport 5y ago
      Not to mention Obama is 50% white. (picture of his parents) https://static.politico.com/dims4/default/553152c/2147483647/resize/1160x/quality/90/?url=https%3A%2F%2Fstatic.politico.com%2Fcapny%2Ffiles%2Fa-rice-obamasr_0.jpg https://static.politico.com/dims4/default/553152c/2147483647...
    - andreyk 5y ago
      Not to disagree, but a couple of FYIs: * LeCun did not really quit Twitter, he's still active on there and has been for a while - but I guess he did temporarily when all this happened. * many researchers agreed with Gebru's opposition to LeCun's original point - see tweets by Charles Isbel, yoavgo, Dirk Hovy embedded here https://thegradient.pub/pulse-lessons/ https://thegradient.pub/pulse-lessons/ under 'On the Source of Bias in Machine Learning Systems' (warning - it takes a while to load). There was a civil back-and-forth between him and these other researchers as you can see in that post, so it was a point worth discussing. Gebru mostly did not participate in this beyond her initial tweets as far as I remember. * Lecun got into more heat when he posted a long set of tweets to Gebru which to many seemed like he was lecturing her on her subject of expertise aka 'mansplaining'. I am sure many would see that as nonsense, but afaik many people making that point was the cause of quitting twitter.
      - horrified 5y ago
        Thanks for the further background information. I have to say it doesn't really make it better for me. The "angry people" are of course correct that you can also create bias in other ways than data sets. But are they implying that people generally deliberately introduce such biases to uphold discrimination? That seems like a very serious and offensive claim to make, and not very helpful either. The whole way to think about issues is backwards in my opinion. I would think usually when you train some algorithm, you tune and experiment until it roughly does what it wants you to do. I don't think anybody starts out by saying "let's use the L2 loss function so that everybody starts white". They'll start with some loss function, and if the results are not as good as they hope, they'll try another one. In fact the usual approach will lead back to issues with the data set, because that is what people will test and tweak their algorithms with. If the dataset doesn't contain "problematic" cases, they won't be detected. But overall, such misclassifications are simply "bugs" that should get a ticket and be fixed, not trigger huge debates. I think it is toxic to try to frame everything as an issue of race.
  - 908B64B197 5y ago
    > LeCunn wasn't necessarily in the right here And yet, he was. Gebru couldn't know that, because despite all her claims, she's not technical.
    - spoonjim 5y ago
      Gebru is not a Woz-level wizard like LeCun but someone who worked at Apple as an engineer and did a PhD with Fei-Fei Li cannot be dismissed as “not technical.”
      - perl4ever 5y ago
        I had a phone screen shortly before the pandemic where I emphasized that I liked understanding and solving problems for people and didn't care what specific technology I used. I got feedback from the recruiter that the company passed on me because I was not technical enough. They literally had asked me zero technical questions. Not too long after, the company was in the news for a massive data breach.
        spoonjim 5y ago
        Well, unless you were interviewing for a CTO type job, they were probably looking for an indication of what technologies you’re most proficient in. If you’re equally proficient in 20 programming languages, that proficiency level is, with high probability, pretty low.
        perl4ever 5y ago
        >If you’re equally proficient in 20 programming languages, that proficiency level is, with high probability, pretty low. I'm way too old to put down 20 programming languages on my resume. Overcoming the compulsion to do that is how I got my first real programming job.
malshe 5y ago
Thanks for sharing this article. Can someone knowledgeable about this issue explain why this is not a data issue? I have read people claiming that ML researchers may bring their own biases into the models but I haven't seen any concrete example of that. Even in the Twitter exchange in this article, Gebru doesn't explain how this is not just data bias. She just throws a lot of insults at LeCun but anyone can do that, right? I would have loved to see her explanation as she is the expert in this area.
- visarga 5y ago
  Well, technically, the way you choose the algorithm and set the hyper-parameters can influence accuracy in a non-uniform way over the distribution of data, introducing additional bias. Bias can be introduced at every step in the ML pipeline, from data collection to labeling, model selection, training, evaluation, deployment and monitoring. Not to mention the complex effects of feedback when you collect data that has been influenced by biases in a prior iteration of the model. That said, I am disappointed in Gebru, I read her "Gender Shades" paper where she completely ignored the existence of the Asian race. That's a pretty big limitation for a paper named in such general terms. In another place she labels Asians as "white adjacent" [1]. That doesn't seem to be ethical behavior for an ethics researcher. She also refused to debate with Yann LeCun on the topic of bias, instead sent him to reeducate himself [2]. [1] https://twitter.com/timnitgebru/status/1252752743942328321 https://twitter.com/timnitgebru/status/1252752743942328321 [2] https://twitter.com/timnitgebru/status/1275191515380215808 https://twitter.com/timnitgebru/status/1275191515380215808
  - 908B64B197 5y ago
    > That said, I am disappointed in Gebru, I read her "Gender Shades" paper where she completely ignored the existence of the Asian race. That's a pretty big limitation for a paper named in such general terms. In another place she labels Asians as "white adjacent" You are scratching the surface of a much bigger racial divide in the Bay Area. [0] [1] > That doesn't seem to be ethical behavior for an ethics researcher. She also refused to debate with Yann LeCun on the topic of bias, instead sent him to reeducate himself I don't think she had the technical background required to really argue with LeCun. [0] https://www.nytimes.com/2010/05/02/us/02sfcrime.html https://www.nytimes.com/2010/05/02/us/02sfcrime.html [1] https://www.hup.harvard.edu/catalog.php?isbn=9780674018440 https://www.hup.harvard.edu/catalog.php?isbn=9780674018440
jml7c5 5y ago
Do we really need to turn this comments section into yet another outrage thread?
burself 5y ago
This Twitter AI lobbyist phenomenon is so weird. When did it become normal to give any attention to these "intellectual" types who seemingly don't do AI work but love to rave about it on Twitter with non-technical arguments? LeCun just got rhetorically beat here because he's not a fucking rhetorician/lobbyist/politician like all "AI ethics" person.
- andreyk 5y ago
  Just FYI, as I said in another comment in this case "many (senior) researchers agreed with Gebru's opposition to LeCun's original point - see tweets by Charles Isbel, yoavgo, Dirk Hovy embedded here https://thegradient.pub/pulse-lessons/ https://thegradient.pub/pulse-lessons/ under 'On the Source of Bias in Machine Learning Systems' (warning - it takes a while to load). There was a civil back-and-forth between him and these other researchers as you can see in that post, so it was a point worth discussing. Gebru mostly did not participate in this beyond her initial tweets as far as I remember." Also, for anyone who thinks Gebru does not have real credentials as an AI researcher, she did get her doctorate at Fei-Fei Li's lab at Stanford (which is now the lab I am part of), and did research at both Microsoft and Google and has co-authored papers with a variety of other well respected researchers. Just sayin', she's obviously not as senior as Lecun but let's not caricature her as if she has no expertise in this area.

IfOnlyYouKnew 5y ago

> You might recognize one name Yes, it’s terrible when people are subject to all sorts of personal attacks based on snark and innuendo.

elefanten 5y ago
This is indecipherable to me. Who are you taking a shot at? Is this comment pro-snark or anti-snark? Sarcastic or straight? Who knows.

horrified 5y ago

Horrible story :-( And once again the grievance strategy was successful.

mrtranscendence 5y ago
I don't know how folks can be aware of how the exchange went down and say that it was a "successful" "grievance strategy". LeCunn wasn't necessarily in the right here, and it wasn't only Gebru's twitter followers going on the offensive.
- horrified 5y ago
  Well LeCunn quit Twitter, so it is "one down". That is what I meant by successful. And Gebru's "arguments" weren't even arguments, just "whatever you say is wrong because you are white and don't recognise our special grievances". I personally agree with what he said when he said it is a difference between a research project and a commercial product. No actual harm was done when the AI completed Obama's image into a white person. You could just laugh about it and move on.
  - deleted 5y ago
    [deleted]
  - frozenport 5y ago
    Not to mention Obama is 50% white. (picture of his parents) https://static.politico.com/dims4/default/553152c/2147483647/resize/1160x/quality/90/?url=https%3A%2F%2Fstatic.politico.com%2Fcapny%2Ffiles%2Fa-rice-obamasr_0.jpg https://static.politico.com/dims4/default/553152c/2147483647...
  - andreyk 5y ago
    Not to disagree, but a couple of FYIs: * LeCun did not really quit Twitter, he's still active on there and has been for a while - but I guess he did temporarily when all this happened. * many researchers agreed with Gebru's opposition to LeCun's original point - see tweets by Charles Isbel, yoavgo, Dirk Hovy embedded here https://thegradient.pub/pulse-lessons/ https://thegradient.pub/pulse-lessons/ under 'On the Source of Bias in Machine Learning Systems' (warning - it takes a while to load). There was a civil back-and-forth between him and these other researchers as you can see in that post, so it was a point worth discussing. Gebru mostly did not participate in this beyond her initial tweets as far as I remember. * Lecun got into more heat when he posted a long set of tweets to Gebru which to many seemed like he was lecturing her on her subject of expertise aka 'mansplaining'. I am sure many would see that as nonsense, but afaik many people making that point was the cause of quitting twitter.
    - horrified 5y ago
      Thanks for the further background information. I have to say it doesn't really make it better for me. The "angry people" are of course correct that you can also create bias in other ways than data sets. But are they implying that people generally deliberately introduce such biases to uphold discrimination? That seems like a very serious and offensive claim to make, and not very helpful either. The whole way to think about issues is backwards in my opinion. I would think usually when you train some algorithm, you tune and experiment until it roughly does what it wants you to do. I don't think anybody starts out by saying "let's use the L2 loss function so that everybody starts white". They'll start with some loss function, and if the results are not as good as they hope, they'll try another one. In fact the usual approach will lead back to issues with the data set, because that is what people will test and tweak their algorithms with. If the dataset doesn't contain "problematic" cases, they won't be detected. But overall, such misclassifications are simply "bugs" that should get a ticket and be fixed, not trigger huge debates. I think it is toxic to try to frame everything as an issue of race.
- 908B64B197 5y ago
  > LeCunn wasn't necessarily in the right here And yet, he was. Gebru couldn't know that, because despite all her claims, she's not technical.
  - spoonjim 5y ago
    Gebru is not a Woz-level wizard like LeCun but someone who worked at Apple as an engineer and did a PhD with Fei-Fei Li cannot be dismissed as “not technical.”
    - perl4ever 5y ago
      I had a phone screen shortly before the pandemic where I emphasized that I liked understanding and solving problems for people and didn't care what specific technology I used. I got feedback from the recruiter that the company passed on me because I was not technical enough. They literally had asked me zero technical questions. Not too long after, the company was in the news for a massive data breach.
      - spoonjim 5y ago
        Well, unless you were interviewing for a CTO type job, they were probably looking for an indication of what technologies you’re most proficient in. If you’re equally proficient in 20 programming languages, that proficiency level is, with high probability, pretty low.
        perl4ever 5y ago
        >If you’re equally proficient in 20 programming languages, that proficiency level is, with high probability, pretty low. I'm way too old to put down 20 programming languages on my resume. Overcoming the compulsion to do that is how I got my first real programming job.

malshe 5y ago

Thanks for sharing this article. Can someone knowledgeable about this issue explain why this is not a data issue? I have read people claiming that ML researchers may bring their own biases into the models but I haven't seen any concrete example of that. Even in the Twitter exchange in this article, Gebru doesn't explain how this is not just data bias. She just throws a lot of insults at LeCun but anyone can do that, right? I would have loved to see her explanation as she is the expert in this area.

visarga 5y ago
Well, technically, the way you choose the algorithm and set the hyper-parameters can influence accuracy in a non-uniform way over the distribution of data, introducing additional bias. Bias can be introduced at every step in the ML pipeline, from data collection to labeling, model selection, training, evaluation, deployment and monitoring. Not to mention the complex effects of feedback when you collect data that has been influenced by biases in a prior iteration of the model. That said, I am disappointed in Gebru, I read her "Gender Shades" paper where she completely ignored the existence of the Asian race. That's a pretty big limitation for a paper named in such general terms. In another place she labels Asians as "white adjacent" [1]. That doesn't seem to be ethical behavior for an ethics researcher. She also refused to debate with Yann LeCun on the topic of bias, instead sent him to reeducate himself [2]. [1] https://twitter.com/timnitgebru/status/1252752743942328321 https://twitter.com/timnitgebru/status/1252752743942328321 [2] https://twitter.com/timnitgebru/status/1275191515380215808 https://twitter.com/timnitgebru/status/1275191515380215808
- 908B64B197 5y ago
  > That said, I am disappointed in Gebru, I read her "Gender Shades" paper where she completely ignored the existence of the Asian race. That's a pretty big limitation for a paper named in such general terms. In another place she labels Asians as "white adjacent" You are scratching the surface of a much bigger racial divide in the Bay Area. [0] [1] > That doesn't seem to be ethical behavior for an ethics researcher. She also refused to debate with Yann LeCun on the topic of bias, instead sent him to reeducate himself I don't think she had the technical background required to really argue with LeCun. [0] https://www.nytimes.com/2010/05/02/us/02sfcrime.html https://www.nytimes.com/2010/05/02/us/02sfcrime.html [1] https://www.hup.harvard.edu/catalog.php?isbn=9780674018440 https://www.hup.harvard.edu/catalog.php?isbn=9780674018440

jml7c5 5y ago

Do we really need to turn this comments section into yet another outrage thread?

burself 5y ago

This Twitter AI lobbyist phenomenon is so weird. When did it become normal to give any attention to these "intellectual" types who seemingly don't do AI work but love to rave about it on Twitter with non-technical arguments? LeCun just got rhetorically beat here because he's not a fucking rhetorician/lobbyist/politician like all "AI ethics" person.

andreyk 5y ago
Just FYI, as I said in another comment in this case "many (senior) researchers agreed with Gebru's opposition to LeCun's original point - see tweets by Charles Isbel, yoavgo, Dirk Hovy embedded here https://thegradient.pub/pulse-lessons/ https://thegradient.pub/pulse-lessons/ under 'On the Source of Bias in Machine Learning Systems' (warning - it takes a while to load). There was a civil back-and-forth between him and these other researchers as you can see in that post, so it was a point worth discussing. Gebru mostly did not participate in this beyond her initial tweets as far as I remember." Also, for anyone who thinks Gebru does not have real credentials as an AI researcher, she did get her doctorate at Fei-Fei Li's lab at Stanford (which is now the lab I am part of), and did research at both Microsoft and Google and has co-authored papers with a variety of other well respected researchers. Just sayin', she's obviously not as senior as Lecun but let's not caricature her as if she has no expertise in this area.