9 ms·

It's not the job of the AI researcher to solve "social biases" in every field, it's their job to build the AI. LeCun is right to focus on his task and correct i

It's not the job of the AI researcher to solve "social biases" in every field, it's their job to build the AI. LeCun is right to focus on his task and correct it, not just start talking about structural issues to which he cannot have insight anyway. PS "Do the work" is a creepy phrase that is popping up everywhere in SocJus. I recommend describing the "work" that needs "doing" instead of just saying "the work" need be "done".

staplers 6y ago
This is like saying it's not a civil engineers job to solve the geography's faults. If you can't build a safe and balanced bridge, then don't. It has real world consequences that can drastically damage communities.
- _Microft 6y ago
  A civil engineer should be able to do that (or decline to do it if they can not) and that's why it is their job and not the geologist's job to build the bridge. Research != engineering.
- dzhiurgis 6y ago
  More like it's not concrete engineers job to find concrete mix that fits every possible usage case.
- jollofricepeas 6y ago
  Another example... The current nightmare that is cyber security is caused by developers who do not understand that with great power comes great responsibility. The culture of software “engineering” and development is fundamentally broken and based entirely on “its not my problem if someone else gets hurt, I just build things.” There’s literally not a single other industry where this level of greed and willful neglect is acceptable. Of course, this would be reflected in the AI community as well.
  - Nasrudith 6y ago
    That shows a fundamental lack of understanding of the fields of software engineering, cyber security and the fields. Both terms are individually overloaded such that many issues aren't even within their domain let alone actually handled by developers! No matter how good the lock is it does no good when the key is left on /top of tbe doormat/ by the user. Blaming greed of engineers for security problems? Seriously?! That is like railing against EMTs as being responsible for the Coronavirus because they wanted to get rich without working hard. It conflates so many different areas and roles that it isn't even coherent logic and is nonsensical.
    - jollofricepeas 6y ago
      This is exactly the attitude to which I’m referring. If you don’t understand that engineering is the fundamental bedrock of IT and the lack of security application during the engineering SDLC is a consistent failure then I don’t know what to tell you except maybe to gain more experience in software engineering and read more about data breaches. Cyber security: https://en.m.wikipedia.org/wiki/Computer_security https://en.m.wikipedia.org/wiki/Computer_security But for arguments sake let’s just stick to network, application, mobile and IOT security. - Why is MFA not on by default? - How many devs have prod credentials published to Github.com right now? - How many unsecured IOT devices are there? - Why is email security such a dumpster fire? - How many companies have an SSDLC? - How many companies require separation of duties and approvals before a dev publishes another AWS bucket or some other unprotected data store to the web? Go ahead and blame business but they don’t know jack about software engineering. We determine which corners to cut as opposed to stating to business that security is just a part of doing engineering. We’re the one’s who decide and thus it’s our responsibility despite denials of people like yourself.
seesawtron 6y ago
I agree with everything you say but it might be worth considering the "social" impact and aspects of AI models that are touted to change the world and are being rapidly adapted to our everyday lives with or without our consent. I can understand that engineers are not trained to consider those aspects but leaving them entirely to their non-creator or external bureaucrats might also not be the best strategy since they hardly understand the systems as well as engineers do. I am not sure what is the best strategy for this co-existence.
- imperfect_blue 6y ago
  If Bagger 288 tries to drive across a footbridge that's not really the engineer's fault. Although while everyone intuitively understands the load bearing capacity of a footbridge, not many understand the capabilities of ML models. So perhaps better advertising the capabilities of AI would help inform decisions. On the other hand, nothing an engineer or scientist can do will stop the Chinese government from using their technology to predict and suppress dissidents and minorities.
  - Udik 6y ago
    > On the other hand, nothing an engineer or scientist can do will stop the Chinese government OT, but shall we stop throwing casual references to China as the example of everything bad that can happen?
    - proto-n 6y ago
      I'm not sure if we can or can't, but in this case this is not just general "china bad", but an actually pretty relevant example because of the CCP using computer vision advancements for the purpose of rounding up Uyghurs.
      - Udik 6y ago
        Here we're talking about AI biases though. Such as the possibility of an AI giving a bad score to a black loan applicant because of implicit biases in the training. On the other hand, China is in a conflict with the Uighur population of Xinjiang. As far as I understand it, there are elements both of cultural clash (China doesn't like religion in general, and the Uighurs are muslim) and the Uighurs' reaction to an influx of ethnically chinese population in the region. Anyway, Uighurs engaged in terrorism: this BBC article seems pretty balanced and lists a number of terror attacks by Uighurs as well as the repressive actions by China: https://www.bbc.com/news/world-asia-china-26414014 https://www.bbc.com/news/world-asia-china-26414014 In this context, I think that China might be using AI not to "round up" Uighurs, but as an intelligence measure to prevent more terror attacks. Similar to how the US intelligence is (I have no doubts about it) profiling muslims and middle eastern immigrants- not because it has anything against those groups per se, but because it has reasons to believe terrorists might hide in their ranks.
learnstats2 6y ago
It's not the job of the nuclear weapon researcher to solve global war, it's their job to build the nuclear weapon. Abstracting away the ethical issues of your (elite) employment is a personal choice that is anything but objectively neutral.
- dgellow 6y ago
  Nitpicking: If they build the weapons, they aren’t the researchers but the engineers.
- scoutt 6y ago
  > Abstracting away the ethical issues of your (elite) employment is a personal choice that is anything but objectively neutral. Does this apply to doctors taking care a murderer, rapist, [insert felony here]? Should this apply to the doctor of Yann LeCun? Should these "personal choices" be valid, allowing the doctors to judge YOU, negating a cure? You can extend 'doctor' to other professions too, like journalists. PS: all those professions require to be objectively neutral.
  - joshuamorton 6y ago
    Different professions can adopt different standard ethical frameworks. Healthcare is relatively unique in the code of ethics it chooses.
conistonwater 6y ago
> It's not the job of the AI researcher to solve "social biases" in every field, it's their job to build the AI. No, this is only technically correct but actually wrong. In the example, they did in fact fail to build the AI, as is their job. "Recognize white faces" is a lame research goal, "recognize human faces" is the real thing. So if somebody builds an AI system that fails on out-of-sample data, then says that had they tried to do it properly they would have succeeded, that's a pretty lame excuse for a poor AI system. They didn't even do their job in the narrow sense that you're using, you don't even need to consider the "social biases" or whatever, it's just a system that didn't work. In fact, many years into this research program, "focussing on his task and correcting it" (working on all human faces) is still not done given the current performance of these systems, but they are quite sure it can be done if they tried.
- deep_etcetera 6y ago
  Imo these are researchers. Their job was to validate their algorithm for doing generative super-resolution on a dataset, they chose the largest and most well-known dataset, it worked reasonably well on their dataset. The model itself is not productive ready for at least the reason that the dataset is not representative. This is ubiquitous in ML papers, they validate their idea on not completely realistic but widely available datasets. The outcome is a piece of knowledge about the behavior on that dataset not a product.
  - joshuamorton 6y ago
    The point that many are making is that this is a myopic view of research. Why is the generic goal to optimize against some particular dataset? That ends up being a narrow and unhelpful goal. As someone put on Twitter : we should be rethinking the meta learning algorithm of the ML academic sphere, and a leader like Yann is the kind of person who should be spearheading that.
    - Decker87 6y ago
      It is true that better datasets are sorely needed, but what would you suggest Yann and other researchers do about it in this situation?
smeeth 6y ago
All sides agree there is an issue around fair implementations of ML algos in the real world. There are two ways this can happen: 1) The model uses biased data when it could have used unbiased data. 2) The model uses biased data when no unbiased data exists and is incapable of correcting for these discrepancies. The first case is most clearly and engineering/implementation issue, the second is obviously not. Biased data is a known failure case of ML, its the responsibility of researchers to design for it.
proto-n 6y ago
Yes LeCun is right to focus on his tasks as a researcher in research labs, papers, conferences. However, he is also a leader of the field when speaking to the outside world, which is what happens on Twitter in heated arguments. I think he has some responsibility to at least acknowledge the complexity of this issue in such cases. Not speaking to the public is also a option if he doesn't like the leader role, so him deleting twitter is a completely ok thing in my eyes.
carljv 6y ago
> It's not the job of the AI researcher to solve "social biases" in every field, it's their job to build the AI. "'Once the rockets are up, who cares where they come down? That's not my department' says Wernher von Braun"
- julianapostate 6y ago
  Tim Berners-lee should have stopped to consider the potential for dark web criminality before he developed the internet.
  - carljv 6y ago
    If one is working on AI dealing with facial recognition and is oblivious to the potential for bias, and the unethical applications of that technology, at this point in the game, I can only assume it's willful.

staplers 6y ago

This is like saying it's not a civil engineers job to solve the geography's faults. If you can't build a safe and balanced bridge, then don't. It has real world consequences that can drastically damage communities.

_Microft 6y ago
A civil engineer should be able to do that (or decline to do it if they can not) and that's why it is their job and not the geologist's job to build the bridge. Research != engineering.
dzhiurgis 6y ago
More like it's not concrete engineers job to find concrete mix that fits every possible usage case.
jollofricepeas 6y ago
Another example... The current nightmare that is cyber security is caused by developers who do not understand that with great power comes great responsibility. The culture of software “engineering” and development is fundamentally broken and based entirely on “its not my problem if someone else gets hurt, I just build things.” There’s literally not a single other industry where this level of greed and willful neglect is acceptable. Of course, this would be reflected in the AI community as well.
- Nasrudith 6y ago
  That shows a fundamental lack of understanding of the fields of software engineering, cyber security and the fields. Both terms are individually overloaded such that many issues aren't even within their domain let alone actually handled by developers! No matter how good the lock is it does no good when the key is left on /top of tbe doormat/ by the user. Blaming greed of engineers for security problems? Seriously?! That is like railing against EMTs as being responsible for the Coronavirus because they wanted to get rich without working hard. It conflates so many different areas and roles that it isn't even coherent logic and is nonsensical.
  - jollofricepeas 6y ago
    This is exactly the attitude to which I’m referring. If you don’t understand that engineering is the fundamental bedrock of IT and the lack of security application during the engineering SDLC is a consistent failure then I don’t know what to tell you except maybe to gain more experience in software engineering and read more about data breaches. Cyber security: https://en.m.wikipedia.org/wiki/Computer_security https://en.m.wikipedia.org/wiki/Computer_security But for arguments sake let’s just stick to network, application, mobile and IOT security. - Why is MFA not on by default? - How many devs have prod credentials published to Github.com right now? - How many unsecured IOT devices are there? - Why is email security such a dumpster fire? - How many companies have an SSDLC? - How many companies require separation of duties and approvals before a dev publishes another AWS bucket or some other unprotected data store to the web? Go ahead and blame business but they don’t know jack about software engineering. We determine which corners to cut as opposed to stating to business that security is just a part of doing engineering. We’re the one’s who decide and thus it’s our responsibility despite denials of people like yourself.

seesawtron 6y ago

I agree with everything you say but it might be worth considering the "social" impact and aspects of AI models that are touted to change the world and are being rapidly adapted to our everyday lives with or without our consent. I can understand that engineers are not trained to consider those aspects but leaving them entirely to their non-creator or external bureaucrats might also not be the best strategy since they hardly understand the systems as well as engineers do. I am not sure what is the best strategy for this co-existence.

imperfect_blue 6y ago
If Bagger 288 tries to drive across a footbridge that's not really the engineer's fault. Although while everyone intuitively understands the load bearing capacity of a footbridge, not many understand the capabilities of ML models. So perhaps better advertising the capabilities of AI would help inform decisions. On the other hand, nothing an engineer or scientist can do will stop the Chinese government from using their technology to predict and suppress dissidents and minorities.
- Udik 6y ago
  > On the other hand, nothing an engineer or scientist can do will stop the Chinese government OT, but shall we stop throwing casual references to China as the example of everything bad that can happen?
  - proto-n 6y ago
    I'm not sure if we can or can't, but in this case this is not just general "china bad", but an actually pretty relevant example because of the CCP using computer vision advancements for the purpose of rounding up Uyghurs.
    - Udik 6y ago
      Here we're talking about AI biases though. Such as the possibility of an AI giving a bad score to a black loan applicant because of implicit biases in the training. On the other hand, China is in a conflict with the Uighur population of Xinjiang. As far as I understand it, there are elements both of cultural clash (China doesn't like religion in general, and the Uighurs are muslim) and the Uighurs' reaction to an influx of ethnically chinese population in the region. Anyway, Uighurs engaged in terrorism: this BBC article seems pretty balanced and lists a number of terror attacks by Uighurs as well as the repressive actions by China: https://www.bbc.com/news/world-asia-china-26414014 https://www.bbc.com/news/world-asia-china-26414014 In this context, I think that China might be using AI not to "round up" Uighurs, but as an intelligence measure to prevent more terror attacks. Similar to how the US intelligence is (I have no doubts about it) profiling muslims and middle eastern immigrants- not because it has anything against those groups per se, but because it has reasons to believe terrorists might hide in their ranks.

learnstats2 6y ago

It's not the job of the nuclear weapon researcher to solve global war, it's their job to build the nuclear weapon. Abstracting away the ethical issues of your (elite) employment is a personal choice that is anything but objectively neutral.

dgellow 6y ago
Nitpicking: If they build the weapons, they aren’t the researchers but the engineers.
scoutt 6y ago
> Abstracting away the ethical issues of your (elite) employment is a personal choice that is anything but objectively neutral. Does this apply to doctors taking care a murderer, rapist, [insert felony here]? Should this apply to the doctor of Yann LeCun? Should these "personal choices" be valid, allowing the doctors to judge YOU, negating a cure? You can extend 'doctor' to other professions too, like journalists. PS: all those professions require to be objectively neutral.
- joshuamorton 6y ago
  Different professions can adopt different standard ethical frameworks. Healthcare is relatively unique in the code of ethics it chooses.

conistonwater 6y ago

> It's not the job of the AI researcher to solve "social biases" in every field, it's their job to build the AI. No, this is only technically correct but actually wrong. In the example, they did in fact fail to build the AI, as is their job. "Recognize white faces" is a lame research goal, "recognize human faces" is the real thing. So if somebody builds an AI system that fails on out-of-sample data, then says that had they tried to do it properly they would have succeeded, that's a pretty lame excuse for a poor AI system. They didn't even do their job in the narrow sense that you're using, you don't even need to consider the "social biases" or whatever, it's just a system that didn't work. In fact, many years into this research program, "focussing on his task and correcting it" (working on all human faces) is still not done given the current performance of these systems, but they are quite sure it can be done if they tried.

deep_etcetera 6y ago
Imo these are researchers. Their job was to validate their algorithm for doing generative super-resolution on a dataset, they chose the largest and most well-known dataset, it worked reasonably well on their dataset. The model itself is not productive ready for at least the reason that the dataset is not representative. This is ubiquitous in ML papers, they validate their idea on not completely realistic but widely available datasets. The outcome is a piece of knowledge about the behavior on that dataset not a product.
- joshuamorton 6y ago
  The point that many are making is that this is a myopic view of research. Why is the generic goal to optimize against some particular dataset? That ends up being a narrow and unhelpful goal. As someone put on Twitter : we should be rethinking the meta learning algorithm of the ML academic sphere, and a leader like Yann is the kind of person who should be spearheading that.
  - Decker87 6y ago
    It is true that better datasets are sorely needed, but what would you suggest Yann and other researchers do about it in this situation?

smeeth 6y ago

All sides agree there is an issue around fair implementations of ML algos in the real world. There are two ways this can happen: 1) The model uses biased data when it could have used unbiased data. 2) The model uses biased data when no unbiased data exists and is incapable of correcting for these discrepancies. The first case is most clearly and engineering/implementation issue, the second is obviously not. Biased data is a known failure case of ML, its the responsibility of researchers to design for it.

proto-n 6y ago

Yes LeCun is right to focus on his tasks as a researcher in research labs, papers, conferences. However, he is also a leader of the field when speaking to the outside world, which is what happens on Twitter in heated arguments. I think he has some responsibility to at least acknowledge the complexity of this issue in such cases. Not speaking to the public is also a option if he doesn't like the leader role, so him deleting twitter is a completely ok thing in my eyes.

carljv 6y ago

> It's not the job of the AI researcher to solve "social biases" in every field, it's their job to build the AI. "'Once the rockets are up, who cares where they come down? That's not my department' says Wernher von Braun"

julianapostate 6y ago
Tim Berners-lee should have stopped to consider the potential for dark web criminality before he developed the internet.
- carljv 6y ago
  If one is working on AI dealing with facial recognition and is oblivious to the potential for bias, and the unethical applications of that technology, at this point in the game, I can only assume it's willful.