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Chief AI Scientist at Facebook, Yann Lecun, quit Twitter after a back and forth with this particular AI researcher - https://syncedreview.com/2020/06/30/yann-le

Chief AI Scientist at Facebook, Yann Lecun, quit Twitter after a back and forth with this particular AI researcher - 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... >I’m always amazed at how people can continue to do thing after thing like this and then turn around and ask me for some sort of extra DEI work or input. This happened to me last year. I was in the middle of a potential lawsuit for which Kat Herller and I hired feminist lawyers who threatened to sue Google (which is when they backed off--before that Google lawyers were prepared to throw us under the bus and our leaders were following as instructed) and the next day I get some random “impact award.” Pure gaslighting. I think if you threaten to sue your employer, the employer isn't too much at fault and just covering their own neck when you later threaten to resign and they take you up on that offer.

axaxs 6y ago
Yeah, that Lecun argument was a really bad first impression of her from me. I guess she found out life isn't Twitter and you can't just go around telling everyone they are bad and wrong without consequences.
- KentLane 6y ago
  She will land on her feet. There is a huge demand for ideologues willing to lobotomize ML. “Let’s generate fake data with GANs to prevent our AI from discovering politically uncomfortable regularities!” It’s ugly work but someone has to do it, right?
  - whymauri 6y ago
    This is a rude dismissal of an entire field within machine learning and statistics (ethics, bias, and robustness).
    - thu2111 6y ago
      Rude, maybe but not very. Dismissal, yes. The belief that AI should learn reality as it exists and not some ideologically purified version of it is a completely rational one, and dismissing the "work" of those so-called "ethicists" is no more problematic than dismissing the "work" of the Chinese censors who purify the Chinese internet. Indeed being dismissive of that work is to be praised, as it shouldn't exist at all.
      - kthxbye123 6y ago
        Name the specific true facts about reality you think these models are capturing that are at risk of being 'purified' by AI ethicists. List them.
        thu2111 6y ago
        Well, read any AI bias paper. Facts about reality these models capture and which are considered problematic include stuff like "doctors are more often men and nurses are more often women" or "most people on the internet are white" or "most stuff on the internet is written in English". Basically the internet is mostly built by westerners and any facet of that gets classed as "bias" rather than being what it is: the truth. Even trivial facts like in English "men chuckle, women giggle" has been used as examples of bias. That's not bias though, that's just how the English language works. For example: https://ai.googleblog.com/2020/04/a-scalable-approach-to-reducing-gender.html https://ai.googleblog.com/2020/04/a-scalable-approach-to-red...
  - IfOnlyYouKnew 6y ago
    The only ugly thing I see is your comment. Edit: I've now read the rest of the thread and the above is no longer true.
  - ur-whale 6y ago
    >She will land on her feet. Indeed. I'd even go one step further and bet that her whole career plan is ML activism. If you scan the email, all the key words are in there. Textbook agitprop. I feel sorry for the next company that will hire her.
  - kthxbye123 6y ago
    What politically uncomfortable regularities are those? Please name the specific uncomfortable true facts about reality that you think these models are capturing.
- no-dr-onboard 6y ago
  "life isn't Twitter and you can't just go around telling everyone they are bad and wrong without consequences." I wish this were true.
  - pb7 6y ago
    It is true, just not as often as we would like it to be. No “justice system” (informal here) punishes perfectly.
sudosysgen 6y ago
Well, reading the thread, it's pretty clear that she was correct. There is a second step of metaoptimization which incentives the researchers to encode algorithms to produce algorithms that more easily bias along a certain way. And she is correct that benchmarking models against biased datasets can lead researchers to bias for architectures that are in turn more likely to have a certain bias. I'm sure LeCun knows this too. It's a fact that has been known for almost 40 years now, that for a given generalization system multiple bias modes are are possible, and of course the process of optimizing architectures against a biased benchmark will change the learning bias of the model. It's not just datasets that are biased, generalization systems are necessarily biased too. As for the last line, I don't know that it should be expected for employers to fire employees pursuing recourse against sexual harassment. I'm sure we can agree that the tone wasn't good, but she does have a point in saying that it's not possible to reduce the bias to datasets used to train the final model. The paper : http://www.cs.cmu.edu/~tom/pubs/NeedForBias_1980.pdf http://www.cs.cmu.edu/~tom/pubs/NeedForBias_1980.pdf
- wil421 6y ago
  TBH it doesn’t matter if she is right or wrong. Her tone and angry writing is pretty harsh. The Twitter replies read a lot like her Google message thing. She’s got a lot of good things to say but needs to work on the delivery. I think she’s playing the victim and race card a lot more than she should. She’s got all the ammo she needs to make her arguments without the angry victim tone.
  - deleted 6y ago
    [deleted]
  - sudosysgen 6y ago
    I agree that her tone is not good, indeed. It's true that this is a big problem on Twitter especially. I still think it is an interesting and under-discussed point, though.
    - smt1 6y ago
      It's why natural language is so hard. People like ethnographers and other people who spend their continuous feeling time understanding this are owed a lot of gratitude (like us, who benefit from unicode). Tone is just another way to encode a basic unit within a language (how a basic unitary 'meme' is converted to a 'lexeme', 'morpheme', etc is very interesting, but has been part of an ancient ontology/metaontology war for all communications systems), and language is just a tool for two human beings to communicate and share ideas. just look at the variance of toneless between English, the gamut of into-european Langs, and tonal languages like Chinese. Heck, in the Universe in a Single Atom, the Dali Llama talks about how Buddhism and other Indic culture spread across the himilayas (it took a lot of sheer luck and kinematics). Sanskrit unlike Tibetan had a lot of prosody (aka distributed word-meaning state), but the English equivalent to things like "affect" and "effect" were culture shocks. Tibetan had little words to encode affect-like words outside of mental states. So little "counting" was ever done.
andreyk 6y ago
Just as an FYI, LeCun has not actually quit Twitter, he still posts stuff on there. For those curious I wrote up a more detailed summary of the episode (https://thegradient.pub/pulse-lessons/ https://thegradient.pub/pulse-lessons/). TLDR, imho he was a bit unthoughtful in how he communicated with a more junior researcher, and got some respectful flack from it from various people, it was not that big a deal. But of course you can make up your own mind on that. In any case, Timnit Gebru (this particular AI researcher) has been recognized as having done some pretty important work in AI ethics. Sounds like this whole thing is messy, but thought I'd mention that for more context.
prionassembly 6y ago
The tone of her replies is regrettable: Yann: “ML systems are biased when data is biased" Timnit: “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 harms caused by ML to dataset bias. Even amidst of world wide protests people don’t hear our voices and try to learn from us, they assume they’re experts in everything. Let us lead her and you follow. Just listen. And learn from scholars like @ruha9 [Ruha Benjamin, Associate Professor of African American Studies at Princeton University]" I don't like engaging this kind of discussion but have to call out the "I'm so tired" bit every time I see it.
- tgv 6y ago
  But isn't it commonly accepted that ML systems reflect the bias in the data, and that it's hard to predict and control the bias in advance?
  - sudosysgen 6y ago
    Her point is more than that. I think that she was trying to say that ML systems have an inherent bias that is independent of their data. This is a consequence of the fact that it's not possible to do generalization, and thus learning, without bias to begin with. What an AI researcher does when deciding which architecture is essentially tuning the bias in the generalization system in order to produce better results, which is not in and of itself bad, and is indeed unavoidable. By bias, I mean really anything that means that the function g() is used over the function h(). In practice this can be anything from the seed for the random values, to the choice of activation function, to the architecture of the network, and so on. If the architectures are selected for their performance on dataset y, then it is possible that even when trained on dataset x, they retain some of the bias of dataset y, because their generalization bias, which is architectural, was tuned for dataset y. This is, admittedly, a theoretical result, and it's not clear that it applies in every case, but it is a solid point. For that reason the point that benchmarking against non-biased datasets is important was made, and thus that there is more to bias than the final dataset used for training during deployment. Therefore, it's not only a responsibility of the engineering community, but also of the scientific community. I've posted it somewhere here already, but here's a nice paper that explains the point that there is more to bias than simply the dataset : http://www.cs.cmu.edu/~tom/pubs/NeedForBias_1980.pdf http://www.cs.cmu.edu/~tom/pubs/NeedForBias_1980.pdf Interestingly enough, this is true of all learning systems, including humans, and is a proof of why it's impossible to be completely unbiased in really anything.
3ball 6y ago
in that case sounds like she deserved what she got
bitshift010 6y ago
Jeff Dean, Head of Google AI has responded (original article has been updated.) claiming the following: >Timnit responded with an email requiring that a number of conditions be met in order for her to continue working at Google, including revealing the identities of every person who Megan and I had spoken to and consulted as part of the review of the paper and the exact feedback. Timnit wrote that if we didn’t meet these demands, she would leave Google and work on an end date. We accept and respect her decision to resign from Google.
Copenjin 6y ago
> This happened to me last year. I was in the middle of a potential lawsuit for which Kat Herller and I hired feminist lawyers who threatened to sue Google Self entitlement or she forgot she was just an employee, and not some sort of independent tribal leader fighting google from the inside.
malcolmgreaves 6y ago
> I think if you threaten to sue your employer, the employer isn't too much at fault and just covering their own neck when you later threaten to resign and they take you up on that offer. It's not clear to me here what you're advocating for -- a lawsuit wouldn't be raised by such an intelligent and virtuous individual unless there was some clear wrongdoing. Are you saying that you think workers shouldn't sue their employers if their employers are breaking the law?

axaxs 6y ago

Yeah, that Lecun argument was a really bad first impression of her from me. I guess she found out life isn't Twitter and you can't just go around telling everyone they are bad and wrong without consequences.

KentLane 6y ago
She will land on her feet. There is a huge demand for ideologues willing to lobotomize ML. “Let’s generate fake data with GANs to prevent our AI from discovering politically uncomfortable regularities!” It’s ugly work but someone has to do it, right?
- whymauri 6y ago
  This is a rude dismissal of an entire field within machine learning and statistics (ethics, bias, and robustness).
  - thu2111 6y ago
    Rude, maybe but not very. Dismissal, yes. The belief that AI should learn reality as it exists and not some ideologically purified version of it is a completely rational one, and dismissing the "work" of those so-called "ethicists" is no more problematic than dismissing the "work" of the Chinese censors who purify the Chinese internet. Indeed being dismissive of that work is to be praised, as it shouldn't exist at all.
    - kthxbye123 6y ago
      Name the specific true facts about reality you think these models are capturing that are at risk of being 'purified' by AI ethicists. List them.
      - thu2111 6y ago
        Well, read any AI bias paper. Facts about reality these models capture and which are considered problematic include stuff like "doctors are more often men and nurses are more often women" or "most people on the internet are white" or "most stuff on the internet is written in English". Basically the internet is mostly built by westerners and any facet of that gets classed as "bias" rather than being what it is: the truth. Even trivial facts like in English "men chuckle, women giggle" has been used as examples of bias. That's not bias though, that's just how the English language works. For example: https://ai.googleblog.com/2020/04/a-scalable-approach-to-reducing-gender.html https://ai.googleblog.com/2020/04/a-scalable-approach-to-red...
- IfOnlyYouKnew 6y ago
  The only ugly thing I see is your comment. Edit: I've now read the rest of the thread and the above is no longer true.
- ur-whale 6y ago
  >She will land on her feet. Indeed. I'd even go one step further and bet that her whole career plan is ML activism. If you scan the email, all the key words are in there. Textbook agitprop. I feel sorry for the next company that will hire her.
- kthxbye123 6y ago
  What politically uncomfortable regularities are those? Please name the specific uncomfortable true facts about reality that you think these models are capturing.
no-dr-onboard 6y ago
"life isn't Twitter and you can't just go around telling everyone they are bad and wrong without consequences." I wish this were true.
- pb7 6y ago
  It is true, just not as often as we would like it to be. No “justice system” (informal here) punishes perfectly.

sudosysgen 6y ago

Well, reading the thread, it's pretty clear that she was correct. There is a second step of metaoptimization which incentives the researchers to encode algorithms to produce algorithms that more easily bias along a certain way. And she is correct that benchmarking models against biased datasets can lead researchers to bias for architectures that are in turn more likely to have a certain bias. I'm sure LeCun knows this too. It's a fact that has been known for almost 40 years now, that for a given generalization system multiple bias modes are are possible, and of course the process of optimizing architectures against a biased benchmark will change the learning bias of the model. It's not just datasets that are biased, generalization systems are necessarily biased too. As for the last line, I don't know that it should be expected for employers to fire employees pursuing recourse against sexual harassment. I'm sure we can agree that the tone wasn't good, but she does have a point in saying that it's not possible to reduce the bias to datasets used to train the final model. The paper : http://www.cs.cmu.edu/~tom/pubs/NeedForBias_1980.pdf http://www.cs.cmu.edu/~tom/pubs/NeedForBias_1980.pdf

wil421 6y ago
TBH it doesn’t matter if she is right or wrong. Her tone and angry writing is pretty harsh. The Twitter replies read a lot like her Google message thing. She’s got a lot of good things to say but needs to work on the delivery. I think she’s playing the victim and race card a lot more than she should. She’s got all the ammo she needs to make her arguments without the angry victim tone.
- deleted 6y ago
  [deleted]
- sudosysgen 6y ago
  I agree that her tone is not good, indeed. It's true that this is a big problem on Twitter especially. I still think it is an interesting and under-discussed point, though.
  - smt1 6y ago
    It's why natural language is so hard. People like ethnographers and other people who spend their continuous feeling time understanding this are owed a lot of gratitude (like us, who benefit from unicode). Tone is just another way to encode a basic unit within a language (how a basic unitary 'meme' is converted to a 'lexeme', 'morpheme', etc is very interesting, but has been part of an ancient ontology/metaontology war for all communications systems), and language is just a tool for two human beings to communicate and share ideas. just look at the variance of toneless between English, the gamut of into-european Langs, and tonal languages like Chinese. Heck, in the Universe in a Single Atom, the Dali Llama talks about how Buddhism and other Indic culture spread across the himilayas (it took a lot of sheer luck and kinematics). Sanskrit unlike Tibetan had a lot of prosody (aka distributed word-meaning state), but the English equivalent to things like "affect" and "effect" were culture shocks. Tibetan had little words to encode affect-like words outside of mental states. So little "counting" was ever done.

andreyk 6y ago

Just as an FYI, LeCun has not actually quit Twitter, he still posts stuff on there. For those curious I wrote up a more detailed summary of the episode (https://thegradient.pub/pulse-lessons/ https://thegradient.pub/pulse-lessons/). TLDR, imho he was a bit unthoughtful in how he communicated with a more junior researcher, and got some respectful flack from it from various people, it was not that big a deal. But of course you can make up your own mind on that. In any case, Timnit Gebru (this particular AI researcher) has been recognized as having done some pretty important work in AI ethics. Sounds like this whole thing is messy, but thought I'd mention that for more context.

prionassembly 6y ago

The tone of her replies is regrettable: Yann: “ML systems are biased when data is biased" Timnit: “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 harms caused by ML to dataset bias. Even amidst of world wide protests people don’t hear our voices and try to learn from us, they assume they’re experts in everything. Let us lead her and you follow. Just listen. And learn from scholars like @ruha9 [Ruha Benjamin, Associate Professor of African American Studies at Princeton University]" I don't like engaging this kind of discussion but have to call out the "I'm so tired" bit every time I see it.

tgv 6y ago
But isn't it commonly accepted that ML systems reflect the bias in the data, and that it's hard to predict and control the bias in advance?
- sudosysgen 6y ago
  Her point is more than that. I think that she was trying to say that ML systems have an inherent bias that is independent of their data. This is a consequence of the fact that it's not possible to do generalization, and thus learning, without bias to begin with. What an AI researcher does when deciding which architecture is essentially tuning the bias in the generalization system in order to produce better results, which is not in and of itself bad, and is indeed unavoidable. By bias, I mean really anything that means that the function g() is used over the function h(). In practice this can be anything from the seed for the random values, to the choice of activation function, to the architecture of the network, and so on. If the architectures are selected for their performance on dataset y, then it is possible that even when trained on dataset x, they retain some of the bias of dataset y, because their generalization bias, which is architectural, was tuned for dataset y. This is, admittedly, a theoretical result, and it's not clear that it applies in every case, but it is a solid point. For that reason the point that benchmarking against non-biased datasets is important was made, and thus that there is more to bias than the final dataset used for training during deployment. Therefore, it's not only a responsibility of the engineering community, but also of the scientific community. I've posted it somewhere here already, but here's a nice paper that explains the point that there is more to bias than simply the dataset : http://www.cs.cmu.edu/~tom/pubs/NeedForBias_1980.pdf http://www.cs.cmu.edu/~tom/pubs/NeedForBias_1980.pdf Interestingly enough, this is true of all learning systems, including humans, and is a proof of why it's impossible to be completely unbiased in really anything.

3ball 6y ago

in that case sounds like she deserved what she got

bitshift010 6y ago

Jeff Dean, Head of Google AI has responded (original article has been updated.) claiming the following: >Timnit responded with an email requiring that a number of conditions be met in order for her to continue working at Google, including revealing the identities of every person who Megan and I had spoken to and consulted as part of the review of the paper and the exact feedback. Timnit wrote that if we didn’t meet these demands, she would leave Google and work on an end date. We accept and respect her decision to resign from Google.

Copenjin 6y ago

> This happened to me last year. I was in the middle of a potential lawsuit for which Kat Herller and I hired feminist lawyers who threatened to sue Google Self entitlement or she forgot she was just an employee, and not some sort of independent tribal leader fighting google from the inside.

malcolmgreaves 6y ago

> I think if you threaten to sue your employer, the employer isn't too much at fault and just covering their own neck when you later threaten to resign and they take you up on that offer. It's not clear to me here what you're advocating for -- a lawsuit wouldn't be raised by such an intelligent and virtuous individual unless there was some clear wrongdoing. Are you saying that you think workers shouldn't sue their employers if their employers are breaking the law?