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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 wro

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.
  - joshuamorton 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. No. I think Isbell's Neurips Keynote (https://nips.cc/virtual/2020/public/invited_16166.html https://nips.cc/virtual/2020/public/invited_16166.html), titled "You Can’t Escape Hyperparameters and Latent Variables" does a good job of explaining this. The humans who ultimately validate the model (and who decide on the dataset) are a hyperparameter. Often ignored, yes, but they are still part of the training loop. They decide what the other hyperparams are, when to stop training and publish, etc. To use a question I've asked on HN before: say you're training a model to detect criminality based on facial structure. This has come up as a real world example, papers have been published on this topic. What does a "good" dataset look like? Or similarly, for a system that decides on bail or sentence length. Do you use historical data on bail or sentencing? We have very well documented examples of bias in both of those things, even in the ground truth. So how do you decide to mitigate that bias? Or do you choose not to, and to continue enforcing said biases in your model? > But overall, such misclassifications are simply "bugs" that should get a ticket and be fixed, not trigger huge debates But when such "bugs" aren't prioritized because people don't think they are bugs, you have to debate whether or not they are bugs at all! The hyperparameter here is "who decides what is or isn't a bug"

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.
- joshuamorton 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. No. I think Isbell's Neurips Keynote (https://nips.cc/virtual/2020/public/invited_16166.html https://nips.cc/virtual/2020/public/invited_16166.html), titled "You Can’t Escape Hyperparameters and Latent Variables" does a good job of explaining this. The humans who ultimately validate the model (and who decide on the dataset) are a hyperparameter. Often ignored, yes, but they are still part of the training loop. They decide what the other hyperparams are, when to stop training and publish, etc. To use a question I've asked on HN before: say you're training a model to detect criminality based on facial structure. This has come up as a real world example, papers have been published on this topic. What does a "good" dataset look like? Or similarly, for a system that decides on bail or sentence length. Do you use historical data on bail or sentencing? We have very well documented examples of bias in both of those things, even in the ground truth. So how do you decide to mitigate that bias? Or do you choose not to, and to continue enforcing said biases in your model? > But overall, such misclassifications are simply "bugs" that should get a ticket and be fixed, not trigger huge debates But when such "bugs" aren't prioritized because people don't think they are bugs, you have to debate whether or not they are bugs at all! The hyperparameter here is "who decides what is or isn't a bug"