[Drama] Yann LeCun against Twitter on Dataset Bias
deep learning machine learning arxiv explained neural networks ai artificial intelligence paper ylc yann lecun convnet face pulse github colab jeff dean hardmaru charles sutton soumith meredith timnit bias noise dataset systems twitter mob
Yann LeCun points out an instance of dataset bias and proposes a sensible solution. People are not happy about it.

Original Tweet: https://twitter.com/ylecun/status/1274782757907030016

ERRATA:
- My specific example of the L1 regularizer wrt to Porsches and Ferraris does not actually work in this particular case. What I mean is a general sparsity-inducing regularizer.
- When I claim that an L1 regularizer would make the problem worse, this only holds in certain circumstances, for example when the data is Gaussian iid.

Thumbnail: https://commons.wikimedia.org/wiki/File:Yann_LeCun_-_2018_(cropped).jpg by Jérémy Barande / Ecole polytechnique Université Paris-Saclay / CC BY-SA 2.0

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BitChute: https://www.bitchute.com/channel/yannic-kilcher
Minds: https://www.minds.com/ykilcher
 Hi there! So you may have seen this already. There's a CVPR paper called Pulse. And what it does is it's a method to up sample a pixelated image in a way that makes it look realistic, but also that the again down sampled variant matches the original down sampled image. So it's kind of a cycle consistency loss together with a GAN. And all in all, it's a method to demonstrate how you could do this. Now this has been trained on this face data set, among others. There was a user Bomzy that made this into a colab so people could try it out and tweeted this out. And as you can see, it works pretty nicely, it gives pretty nice results on this particular data set. But of course, people started playing around with it and gave fairly funny results like this, or that. That gets more into the horrible category. These. So you can see these ones I particularly like Trump being made into the little child. So you can see as soon as you get away from the original kind of data set modality, you are going to get these results that are off. And people started to notice that so here you input Barack Obama, and what comes out is a fairly standard Caucasian person, someone tweeted out saying this image speaks volumes about the dangers of bias in AI, I guess here is where the entire story starts. So young Lacaan weighs in and says, ML systems are biased when data is biased. This face up sampling system makes everyone look white because the network was pre trained on flick face HQ, which mainly contains white people picks train the exact same system on a data set from Senegal, and everyone will look African. So this is pointing out why this happens namely because the data set is mainly Caucasian people. So the results of up sampling are going to be mainly Caucasian people. And this is like a straightforward explanation of why we're seeing what we're seeing. But of course, this was not okay. And here is where the piling starts. As an interjection, we have to talk about bias in machine learning. Technically, there's a statistical notion of bias, which has a very rigorous definition. And there is the societal definition of bias. And these two things, even though they're the same word, they're totally different. A machine learning system mainly consists of four different parts. There is a data set, the model, the loss function, and the optimization procedure. Statistical bias means whenever the model, the loss or the optimization procedure lead to a situation where the outcome doesn't reflect the distribution of the data that you input. This, for example, is achieved when you regularize your model, which means that you put some prior knowledge onto the model, you introduce bias, and therefore you choose to not accurately represent your data distribution, regularize it to a more biased distribution that in turn has lower variance. We know this as the bias variance trade off. It's actually very simple, right? You have the Ferraris and the Lamborghinis, and you want to make a model that predicts the accident probability. Now it just so happens that the Ferrari drivers are a bit more reckless, and they do slightly higher accidents. And now I train my logistic regression, and it tells me, okay, 60-40. Cool. But now I train my logistic regression with an L1 penalty, and I say, I want my model to be, you know, explainable. So I want it to be sparse. I want the least amount of variables to be contributing to it. What's the model going to say? The model is going to say, Ferrari drivers add Lamborghini drivers good. Societal bias in machine learning is way different. An example for this is when face detection systems work well on Caucasian people, but don't work so well faced with people from other heritages. And these societal biases are in the data set. As Jan LeCun points out here, if you change the data set, you'll change these biases. Notably, these societal biases can only be in the data set. Otherwise, you'd have to argue something like logistic regression itself has a preference for white people or something like this. Now there is a considerable interaction effect between the two, but as Jan LeCun points out, the actual societal bias of the final system is a direct result of the bias in the data set. And he is very correct. If you train that system on a different data set, it will exhibit different biases. Societal bias cannot be in the other parts of the machine learning pipeline. They can serve to exaggerate or mitigate that bias in the data set, but they themselves can only be statistically biased and not societally biased. But Jan LeCun make the terrible mistake of pinpointing the exact root cause of this problem and not addressing the I guess, wider ranging problems in the field as some people perceive it. And he shouldn't have to, right? He pretty clearly says, this is why it happens. We can solve it by swapping the data set. He doesn't say anything about anything else. Namely, he doesn't say that general bias in the field is not a problem. He doesn't say that this doesn't harm anyone. None of that. He simply he simply suggests a solution. Jonathan Peck says, well, yes, that's the point. ML researchers need to be more careful selecting their data so that they don't encode biases like this. And LeCun responds with not so much ML researchers, but ML engineers. The consequences of bias are considerably more dire in a deployed product than in an academic paper, which is also correct. This paper was about the method showing that this method works on this data set. Now, Sumit here makes an interesting point, which I agree with, saying that today, ML researchers are inadvertently powering product of a lot of non-AI companies who ignorantly start with a pre-trained BERT or ResNet or YOLO from the internet, probably ignoring the license, read me and so on, which is a valid point, right? There are going to be people that take this and think, oh, this is a face up sampler. Cool. I can use that without noting that this is simply an example implementation on an example data set. So you can argue that there might be some responsibility of the researchers right here. That doesn't make Jan LeCun not correct, but I'd still consider this to be like a fruitful discussion between individuals right here. But now we go on. This person saying, train it on the whole American population with an L2 loss and almost everyone will look white or train it on the whole American population with an L1 loss and more people might look black. Stop pretending that bias does not also come from algorithmic choices. Jan LeCun never says it doesn't, right? LeCun responds now saying, the most efficient way to do it though is to equalize the frequencies of the categories of samples during training. This forces the network to pay attention to all the relevant features for all the sample categories. And training with an L1 instead of an L2 will not even begin to solve the problem. I would pretty much argue training with an L1 loss here would exacerbate the problem because the L2 loss is much more sensitive to outliers. Charles Sutton says, serious question, why do you feel that it's important to make this point? Are you worried that people are going to start suing CycleGAN? And LeCun says, because people should be aware of this problem and know its cause so they can fix it. How terrible Jan, how terrible you dare pinpoint the exact cause of the problem so that people can fix it. The correct thing to do is to point out that everything is problematic. So Tim the Gibber says, Jan, I suggest you watch me and Emily's tutorial or a number of scholars who are experts in this area. You can't just reduce harms to data set bias. For once, listen to us people from marginalized communities and what we tell you. If not now during worldwide protests, not sure when. So again, I feel the argument here is that you can't simply point out that it's the data set bias. You must point out the bigger problems which Jan LeCun does not ever deny. He simply says this particular problem can be solved by switching the data set. Nicole Olleroux says, Jan was in my PhD jury. I am indebted for him for everything he taught me, but this constant dismissal of the harms caused directly or indirectly by the ML community is highly problematic. Where or when have I dismissed the harm caused by the ML community? I'm pointing out the cause of the harm so it can be fixed. You can't fix the harm unless you know what causes it. No. LeRoux says causes of the biases are numerous only pointing out data set bias deflects the attention away from the other more pervasive ones that make the whole field of bias in ML. Many people try to get your attention about these issues, but you kept focus on the data set because the data set is the problem right here. He doesn't dismiss any of the other things. He simply says here the data set is the problem if your problem is that it doesn't work as well for non-concassian people. Which was never the intent of this. The intent of this was to showcase the method. I mean ImageNet is like 60% dog species and still people train on it to showcase their image recognition techniques. No one training on ImageNet makes a claim that they have solved computer vision for all the classes in the world in a fair manner. Timnigibru goes on saying I'm sick of this framing, tired of it. Many people have tried to explain. Many scholars listen to us. You can't just reduce the harms caused by ML to data set bias. Doesn't do that. Doesn't do it. So someone asks her is he engaging in any ways with you? It's appalling to see that he answers to everybody but you. Yet maybe there is a conversation going on in private and I don't want to jeopardize it. Note that Yan LeCun's tweet has 500 retweets, 1.9k likes and comments as far as you can scroll. To what she responds to with yep but I'm used to white men refusing to engage with black and brown women even on issues of bias that mostly affect us. I mean he literally has ignored a whole body of work by people from that demographic hence the statement so not surprised. I mean in absence of the fact that an argument should be independent of the person making the argument that is a low blow. Hardmaru says I respectfully disagree with Yan here as long as progress is benchmarked on biased data such biases will also be reflected in the inductive biases of ML systems. Advancing ML with biased benchmarks and asking engineers to simply retrain models with unbiased data is not helpful. I don't disagree with you here. I don't think my tweet contradicts your statement which it doesn't. People are reading into this because he doesn't conform to the orthodoxy of pointing out that everything and everything is problematic and simply pinpoints a particular problem. He must be thinking all the wrong things. Jeff Dean says this is a clear example here is an illustration that seemingly minor choices in learning algorithms or loss can have significant effects so bias in ML systems is about much more than just avoid data bias. ML researchers and practitioners must pay attention to these issues and I think they are and Lacan doesn't say anything against that. He says as I point out in my comment to this tweet is much more efficient to correct this kind of bias. Note that Yan Lacan actually differentiates between the different kinds of biases by equalizing the frequencies of categories of samples during training than be hacking the loss function. Correct because if you hack the loss function you're trying to counter one kind of bias by another kind of bias. Meredith Whitaker says this is very racist and even if it recognized non-white people it would be very racist. This is cop tech. It's designed to allow those with power to surveil and control those with less power. Diverse training sets aren't going to fix it advocating that we should never build these systems and that's a discussion to be had but let me break this to you. This isn't going to help the cops. This isn't actually giving you the face of the person that was down pixeled. This is simply going to give you the most likely face associated with that down pixeled picture given the data set the algorithm was trained on. I don't see this whenever any machine learning algorithm does anything with faces at all. People jumping up going like this is cop technology. Well in line with all the broader impact statement advice can't it also be used to find lost children from very very bad security camera footage? And if I already mentioned that this doesn't actually give you back the person on the down sampled image it will give you back the most likely person given the data set. So with that I want to conclude this section. Please stop the witch hunting. Yann LeCun made a completely fine tweet here and there's no reason why people should pile on him this hard. He doesn't dismiss any of the other problems just because he doesn't mention them and while we all enjoy a good discussion where people disagree genuinely it's not helpful to accuse him of things he never said or meant. I mean where does this all lead? The result of this is going to be that small labs that don't have the resources to collect their own data sets or check for all the possible biases in their models that are reliant on the data sets that we do have even if they are biased and flawed will just be disincentivized from publishing their code or actually doing research at all. So this as every other additional constraint on research is going to help the large corporations with lots of money. And maybe that's just my opinion but we should be able to just talk about a problem and the solution to it without always having to make sure that we rabble down all the different things that are and might be wrong according to the canon. And big props to Yann LeCun here for holding his own. 90% of people by now would probably be like oh yes I'm so sorry I did a not thoughtful comment blah blah blah. Props to you Yann, keep going. And with that I conclude this section. Let me know what you think in the comments and I'll see you next time. Bye bye.
 Hi there! So you may have seen this already. There's a CVPR paper called Pulse. And what it
 does is it's a method to up sample a pixelated image in a way that makes it look realistic,
 but also that the again down sampled variant matches the original down sampled image. So it's
 kind of a cycle consistency loss together with a GAN. And all in all, it's a method to demonstrate
 how you could do this. Now this has been trained on this face data set, among others. There was a
 user Bomzy that made this into a colab so people could try it out and tweeted this out. And as you
 can see, it works pretty nicely, it gives pretty nice results on this particular data set. But of
 course, people started playing around with it and gave fairly funny results like this, or that. That
 gets more into the horrible category. These. So you can see these ones I particularly like Trump
 being made into the little child. So you can see as soon as you get away from the original kind of
 data set modality, you are going to get these results that are off. And people started to notice
 that so here you input Barack Obama, and what comes out is a fairly standard Caucasian person,
 someone tweeted out saying this image speaks volumes about the dangers of bias in AI, I guess
 here is where the entire story starts. So young Lacaan weighs in and says, ML systems are biased
 when data is biased. This face up sampling system makes everyone look white because the network was
 pre trained on flick face HQ, which mainly contains white people picks train the exact same system on
 a data set from Senegal, and everyone will look African. So this is pointing out why this happens
 namely because the data set is mainly Caucasian people. So the results of up sampling are going
 to be mainly Caucasian people. And this is like a straightforward explanation of why we're seeing
 what we're seeing. But of course, this was not okay. And here is where the piling starts. As an
 interjection, we have to talk about bias in machine learning. Technically, there's a statistical notion
 of bias, which has a very rigorous definition. And there is the societal definition of bias. And these
 two things, even though they're the same word, they're totally different. A machine learning
 system mainly consists of four different parts. There is a data set, the model, the loss function,
 and the optimization procedure. Statistical bias means whenever the model, the loss or the optimization
 procedure lead to a situation where the outcome doesn't reflect the distribution of the data that
 you input. This, for example, is achieved when you regularize your model, which means that you put
 some prior knowledge onto the model, you introduce bias, and therefore you choose to not accurately
 represent your data distribution, regularize it to a more biased distribution that in turn has lower
 variance. We know this as the bias variance trade off. It's actually very simple, right? You have
 the Ferraris and the Lamborghinis, and you want to make a model that predicts the accident probability.
 Now it just so happens that the Ferrari drivers are a bit more reckless, and they do slightly higher
 accidents. And now I train my logistic regression, and it tells me, okay, 60-40. Cool. But now I train
 my logistic regression with an L1 penalty, and I say, I want my model to be, you know, explainable.
 So I want it to be sparse. I want the least amount of variables to be contributing to it. What's the
 model going to say? The model is going to say, Ferrari drivers add Lamborghini drivers good.
 Societal bias in machine learning is way different. An example for this is when face detection systems
 work well on Caucasian people, but don't work so well faced with people from other heritages.
 And these societal biases are in the data set. As Jan LeCun points out here, if you change the data
 set, you'll change these biases. Notably, these societal biases can only be in the data set.
 Otherwise, you'd have to argue something like logistic regression itself has a preference for
 white people or something like this. Now there is a considerable interaction effect between the two,
 but as Jan LeCun points out, the actual societal bias of the final system is a direct result of
 the bias in the data set. And he is very correct. If you train that system on a different data set,
 it will exhibit different biases. Societal bias cannot be in the other parts of the machine
 learning pipeline. They can serve to exaggerate or mitigate that bias in the data set, but they
 themselves can only be statistically biased and not societally biased. But Jan LeCun make the
 terrible mistake of pinpointing the exact root cause of this problem and not addressing the
 I guess, wider ranging problems in the field as some people perceive it. And he shouldn't have to,
 right? He pretty clearly says, this is why it happens. We can solve it by swapping the data
 set. He doesn't say anything about anything else. Namely, he doesn't say that general bias
 in the field is not a problem. He doesn't say that this doesn't harm anyone. None of that. He simply
 he simply suggests a solution. Jonathan Peck says, well, yes, that's the point. ML researchers need
 to be more careful selecting their data so that they don't encode biases like this. And LeCun
 responds with not so much ML researchers, but ML engineers. The consequences of bias are considerably
 more dire in a deployed product than in an academic paper, which is also correct. This paper was about
 the method showing that this method works on this data set. Now, Sumit here makes an interesting
 point, which I agree with, saying that today, ML researchers are inadvertently powering product
 of a lot of non-AI companies who ignorantly start with a pre-trained BERT or ResNet or YOLO from the
 internet, probably ignoring the license, read me and so on, which is a valid point, right?
 There are going to be people that take this and think, oh, this is a face up sampler. Cool. I can
 use that without noting that this is simply an example implementation on an example data set.
 So you can argue that there might be some responsibility of the researchers right here.
 That doesn't make Jan LeCun not correct, but I'd still consider this to be like a fruitful
 discussion between individuals right here. But now we go on. This person saying, train it on the whole
 American population with an L2 loss and almost everyone will look white or train it on the whole
 American population with an L1 loss and more people might look black. Stop pretending that bias does
 not also come from algorithmic choices. Jan LeCun never says it doesn't, right? LeCun responds now
 saying, the most efficient way to do it though is to equalize the frequencies of the categories of
 samples during training. This forces the network to pay attention to all the relevant features for
 all the sample categories. And training with an L1 instead of an L2 will not even begin to solve
 the problem. I would pretty much argue training with an L1 loss here would exacerbate the problem
 because the L2 loss is much more sensitive to outliers. Charles Sutton says, serious question,
 why do you feel that it's important to make this point? Are you worried that people are going to
 start suing CycleGAN? And LeCun says, because people should be aware of this problem and know
 its cause so they can fix it. How terrible Jan, how terrible you dare pinpoint the exact cause of the
 problem so that people can fix it. The correct thing to do is to point out that everything is
 problematic. So Tim the Gibber says, Jan, I suggest you watch me and Emily's tutorial or a number of
 scholars who are experts in this area. You can't just reduce harms to data set bias. For once,
 listen to us people from marginalized communities and what we tell you. If not now during worldwide
 protests, not sure when. So again, I feel the argument here is that you can't simply point out
 that it's the data set bias. You must point out the bigger problems which Jan LeCun does not ever
 deny. He simply says this particular problem can be solved by switching the data set. Nicole Olleroux
 says, Jan was in my PhD jury. I am indebted for him for everything he taught me, but this constant
 dismissal of the harms caused directly or indirectly by the ML community is highly problematic.
 Where or when have I dismissed the harm caused by the ML community? I'm pointing out the cause of
 the harm so it can be fixed. You can't fix the harm unless you know what causes it. No. LeRoux says
 causes of the biases are numerous only pointing out data set bias deflects the attention away
 from the other more pervasive ones that make the whole field of bias in ML. Many people try to get
 your attention about these issues, but you kept focus on the data set because the data set is the
 problem right here. He doesn't dismiss any of the other things. He simply says here the data set is
 the problem if your problem is that it doesn't work as well for non-concassian people. Which was
 never the intent of this. The intent of this was to showcase the method. I mean ImageNet is like 60%
 dog species and still people train on it to showcase their image recognition techniques.
 No one training on ImageNet makes a claim that they have solved computer vision for all the
 classes in the world in a fair manner. Timnigibru goes on saying I'm sick of this framing, tired of
 it. Many people have tried to explain. Many scholars listen to us. You can't just reduce the harms
 caused by ML to data set bias. Doesn't do that. Doesn't do it. So someone asks her is he engaging
 in any ways with you? It's appalling to see that he answers to everybody but you. Yet maybe there
 is a conversation going on in private and I don't want to jeopardize it. Note that Yan LeCun's tweet
 has 500 retweets, 1.9k likes and comments as far as you can scroll. To what she responds to with yep
 but I'm used to white men refusing to engage with black and brown women even on issues of bias that
 mostly affect us. I mean he literally has ignored a whole body of work by people from that demographic
 hence the statement so not surprised. I mean in absence of the fact that an argument should be
 independent of the person making the argument that is a low blow. Hardmaru says I respectfully
 disagree with Yan here as long as progress is benchmarked on biased data such biases will also
 be reflected in the inductive biases of ML systems. Advancing ML with biased benchmarks and asking
 engineers to simply retrain models with unbiased data is not helpful. I don't disagree with you
 here. I don't think my tweet contradicts your statement which it doesn't. People are reading
 into this because he doesn't conform to the orthodoxy of pointing out that everything and
 everything is problematic and simply pinpoints a particular problem. He must be thinking all the
 wrong things. Jeff Dean says this is a clear example here is an illustration that seemingly
 minor choices in learning algorithms or loss can have significant effects so bias in ML systems is
 about much more than just avoid data bias. ML researchers and practitioners must pay attention
 to these issues and I think they are and Lacan doesn't say anything against that. He says as I
 point out in my comment to this tweet is much more efficient to correct this kind of bias. Note that
 Yan Lacan actually differentiates between the different kinds of biases by equalizing the
 frequencies of categories of samples during training than be hacking the loss function.
 Correct because if you hack the loss function you're trying to counter one kind of bias by
 another kind of bias. Meredith Whitaker says this is very racist and even if it recognized
 non-white people it would be very racist. This is cop tech. It's designed to allow those with power
 to surveil and control those with less power. Diverse training sets aren't going to fix it
 advocating that we should never build these systems and that's a discussion to be had
 but let me break this to you. This isn't going to help the cops. This isn't actually giving you the
 face of the person that was down pixeled. This is simply going to give you the most likely face
 associated with that down pixeled picture given the data set the algorithm was trained on. I don't
 see this whenever any machine learning algorithm does anything with faces at all. People jumping
 up going like this is cop technology. Well in line with all the broader impact statement advice
 can't it also be used to find lost children from very very bad security camera footage? And if I
 already mentioned that this doesn't actually give you back the person on the down sampled image
 it will give you back the most likely person given the data set. So with that I want to conclude this
 section. Please stop the witch hunting. Yann LeCun made a completely fine tweet here and there's no
 reason why people should pile on him this hard. He doesn't dismiss any of the other problems just
 because he doesn't mention them and while we all enjoy a good discussion where people disagree
 genuinely it's not helpful to accuse him of things he never said or meant. I mean where does this all
 lead? The result of this is going to be that small labs that don't have the resources to collect their
 own data sets or check for all the possible biases in their models that are reliant on the data sets
 that we do have even if they are biased and flawed will just be disincentivized from publishing their
 code or actually doing research at all. So this as every other additional constraint on research is
 going to help the large corporations with lots of money. And maybe that's just my opinion but we
 should be able to just talk about a problem and the solution to it without always having to make
 sure that we rabble down all the different things that are and might be wrong according to the canon.
 And big props to Yann LeCun here for holding his own. 90% of people by now would probably be like
 oh yes I'm so sorry I did a not thoughtful comment blah blah blah. Props to you Yann,
 keep going. And with that I conclude this section. Let me know what you think in the
 comments and I'll see you next time. Bye bye.