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> The dispute is whether or not it's a good thing. Of course it's not a good thing. General-purpose AI should not be overridden or forcefully trained to favor

> The dispute is whether or not it's a good thing. Of course it's not a good thing. General-purpose AI should not be overridden or forcefully trained to favor one political view over another.

scarmig 4y ago
If I were a corporation looking for an LLM for some product feature, I would absolutely go for the one with more "woke" opinions, even if if resulted in a worse customer experience. If you didn't, you risk a lot of media and government backlash. It's all about context.
- wizeman 4y ago
  > If I were a corporation looking for an LLM for some product feature, I would absolutely go for the one with more "woke" opinions, even if if resulted in a worse customer experience. How about instead of preferring the LLM with "woke" opinions, you would prefer an LLM that was simply trained to avoid controversial topics? That way, you could use it for your product while still avoiding both bias and media/government backlash. Are you aware that by being biased towards "woke" opinions you are basically alienating about 50% of the population or so?
  - scarmig 4y ago
    It would depend on what exactly I was building. Maybe it needs to be able to generate texts on controversial topics. I agree that it alienates people, but the choice is less between alienating half and alienating no one but more alienating half and alienating another half that includes the media and the law. I'd use the same strategy if I worked in China: business is business and money trumps theoretical concerns about free speech and open dialogue.
    - wizeman 4y ago
      > the choice is less between alienating half and alienating no one but more alienating half and alienating another half that includes the media and the law So you're saying that if your LLM is unbiased then you are alienating the other half that includes the media and the law? That's actually very telling.
      - scarmig 4y ago
        Indeed. Though, in fairness, an unbiased model would probably end up alienating closer to 100% of people instead of any particular half of them.
        wizeman 4y ago
        > Though, in fairness, an unbiased model would probably end up alienating closer to 100% of people instead of any particular half of them. Why?
        scarmig 4y ago
        No one has a total claim on truth; worse than that, people who have wildly diverging opinions from truth are more likely to hold them very strongly and will be upset when the model tells them they're wrong.
  - freejazz 4y ago
    That's what this is, you and the Trumpers are just calling it "woke"
- 0xy 4y ago
  If you wanted to lose money, maybe. When Disney wades into woke content they lose hundreds of millions of dollars, such as "Lightyear", which was a box office bomb. There's also the female-led Ghostbusters which was an objective disaster. It's pretty clear customers reject woke content.
genderwhy 4y ago
So it should be allowed to implicitly trained to favor one political view over another? There's no way to avoid the bias, whether it's because you chose a different training set, reinforced different pathways, or put blocks in place on certain topics. I'd rather the authors be explicit in where they are putting their fingers on the scales rather than just relying on "Guess we got lucky".
- wizeman 4y ago
  > So it should be allowed to implicitly trained to favor one political view over another? > There's no way to avoid the bias, How about collecting a representative sample of all data for your training data? Or at least, trying to do that as best you can. Saying "there's no way to avoid the bias" is just an excuse to get away with being biased, in my view.
  - genderwhy 4y ago
    You cannot describe a procedure that collects a representative sample without introducing bias. What does representative mean? Who decides what it means? Who gets to set the parameters of over vs under sampling? Let's say that white nationalism is a tiny fraction of ideas online. Significantly less than 0.1%. Now, you randomly sample the internet and do not collect this idea into your training set. Do you adjust your approach to make sure it's represented (because as reprehensible as it is, it is the reality of online discourse in some places?) I genuinely believe that it's all going to be biased -- there are no unbiased news or media outlets -- and the sooner you recognize everything is biased, the sooner you can move on to building the tools to recognize and understand that bias. Asking "why can't we strive to build an unbiased outlet" is to me like asking "why can't we build a ladder to the moon". It's an interesting question, but ultimately should lead you to "Well, why do you want that, and your approach is impossible but the outcome you want might not be."
    - wizeman 4y ago
      > You cannot describe a procedure that collects a representative sample without introducing bias. What does representative mean? Who decides what it means? Who gets to set the parameters of over vs under sampling? Perhaps you can take a representative (i.e. random and statistically significant enough) sample of the population and ask them their opinion about certain (especially controversial) pieces of your training data, then adjust your training data to weigh more heavily or less heavily based on these evaluations. That's just one idea that occurred to me from the top of my head, but I'm sure there are research scientists who can devise a better method than what I just came up with in 30 seconds. > Let's say that white nationalism is a tiny fraction of ideas online. Significantly less than 0.1%. Now, you randomly sample the internet and do not collect this idea into your training set. Do you adjust your approach to make sure it's represented (because as reprehensible as it is, it is the reality of online discourse in some places?) Sure. Otherwise you're in for a dangerous (and perhaps immoral) slippery slope. But it should be represented only as much as it is significant. Obviously you should not train your AI to weigh these ideas as much as others that are more prevalent. If it's only a tiny minority of the population that have such opinions, that should be reflected in the data (so that there is proportionally less data to account for these ideas). One would think that a sufficiently intelligent AI would not end up being a white nationalist, though (I'm not talking about current LLM technology, but perhaps some future version of it that is capable of something akin to self-reflection or deep thought). > I genuinely believe that it's all going to be biased -- there are no unbiased news or media outlets -- and the sooner you recognize everything is biased, the sooner you can move on to building the tools to recognize and understand that bias. News and media outlets are biased, yes, of course. The content from these sources is not generated from the population in general. That doesn't mean it's impossible to generate an unbiased sample of data (at least, up to a certain margin of error, depending on effort expended).
pixl97 4y ago
[flagged]
- wizeman 4y ago
  > General AI: Actually the Nazi's were a good idea and we should bring them back. > You: Perfectly acceptable. Why would the general AI say that? All examples I've seen of that kind of speech from LLMs were due to them being specifically prompted to generate such a response. It's not like the AI decided to say that on its own, in a completely unrelated conversation. In fact, it wouldn't make sense if the AI did that on its own, would it? Because the AI reflects the data it was trained on and we know that almost nobody is a Nazi. > Extreme view tend to get far more print time then their actual occurrence IRL. Yes, I understand that. We live in a crap society. But I'd argue we should strive to educate people on why an LLM can answer like that, not censor it arbitrarily. There is an infinite amount of stupid or bad things an LLM can answer, depending on the prompt you use, so I would argue that we should just learn to accept that "stupid prompt = stupid answer" rather than trying to make the LLM not answer anything that might be the slightest bit controversial. > But the fact is in a representative democracy favoring viewpoints that destroy democracy is suicide. But I'm not arguing for favoring those viewpoints, am I? I am arguing for AI to be unbiased.
  - pixl97 4y ago
    You want your AI to be unbiased, but you can only feed it data that is biased.... I hope you begin to see the problem at hand.
    - wizeman 4y ago
      Ok, so I guess Hacker News has decided that data can only be 100% biased or 0% unbiased, but nothing in-between. Yes, almost all data is biased... of course. Some data is 100% biased. Some data is 1% biased. How about we try to collect data and then weigh it such that what we feed to the AI during training is as unbiased as possible, given a certain amount of effort? You know that you can actually influence what data you feed to the AI, right? Or how much the training takes some data into account vs some other data, I guess. You know that you can create a metric for measuring bias, right? You know that even if you are not capable of being 100% unbiased, you can work towards that goal, right? You know that there are plenty of smart people who can come up with ideas for eliminating (or mitigating) sources of errors when measuring bias, right? I hope you begin to see the solution at hand.

> The dispute is whether or not it's a good thing. Of course it's not a good thing. General-purpose AI should not be overridden or forcefully trained to favor one political view over another.

scarmig 4y ago

If I were a corporation looking for an LLM for some product feature, I would absolutely go for the one with more "woke" opinions, even if if resulted in a worse customer experience. If you didn't, you risk a lot of media and government backlash. It's all about context.

wizeman 4y ago
> If I were a corporation looking for an LLM for some product feature, I would absolutely go for the one with more "woke" opinions, even if if resulted in a worse customer experience. How about instead of preferring the LLM with "woke" opinions, you would prefer an LLM that was simply trained to avoid controversial topics? That way, you could use it for your product while still avoiding both bias and media/government backlash. Are you aware that by being biased towards "woke" opinions you are basically alienating about 50% of the population or so?
- scarmig 4y ago
  It would depend on what exactly I was building. Maybe it needs to be able to generate texts on controversial topics. I agree that it alienates people, but the choice is less between alienating half and alienating no one but more alienating half and alienating another half that includes the media and the law. I'd use the same strategy if I worked in China: business is business and money trumps theoretical concerns about free speech and open dialogue.
  - wizeman 4y ago
    > the choice is less between alienating half and alienating no one but more alienating half and alienating another half that includes the media and the law So you're saying that if your LLM is unbiased then you are alienating the other half that includes the media and the law? That's actually very telling.
    - scarmig 4y ago
      Indeed. Though, in fairness, an unbiased model would probably end up alienating closer to 100% of people instead of any particular half of them.
      - wizeman 4y ago
        > Though, in fairness, an unbiased model would probably end up alienating closer to 100% of people instead of any particular half of them. Why?
        scarmig 4y ago
        No one has a total claim on truth; worse than that, people who have wildly diverging opinions from truth are more likely to hold them very strongly and will be upset when the model tells them they're wrong.
- freejazz 4y ago
  That's what this is, you and the Trumpers are just calling it "woke"
0xy 4y ago
If you wanted to lose money, maybe. When Disney wades into woke content they lose hundreds of millions of dollars, such as "Lightyear", which was a box office bomb. There's also the female-led Ghostbusters which was an objective disaster. It's pretty clear customers reject woke content.

genderwhy 4y ago

So it should be allowed to implicitly trained to favor one political view over another? There's no way to avoid the bias, whether it's because you chose a different training set, reinforced different pathways, or put blocks in place on certain topics. I'd rather the authors be explicit in where they are putting their fingers on the scales rather than just relying on "Guess we got lucky".

wizeman 4y ago
> So it should be allowed to implicitly trained to favor one political view over another? > There's no way to avoid the bias, How about collecting a representative sample of all data for your training data? Or at least, trying to do that as best you can. Saying "there's no way to avoid the bias" is just an excuse to get away with being biased, in my view.
- genderwhy 4y ago
  You cannot describe a procedure that collects a representative sample without introducing bias. What does representative mean? Who decides what it means? Who gets to set the parameters of over vs under sampling? Let's say that white nationalism is a tiny fraction of ideas online. Significantly less than 0.1%. Now, you randomly sample the internet and do not collect this idea into your training set. Do you adjust your approach to make sure it's represented (because as reprehensible as it is, it is the reality of online discourse in some places?) I genuinely believe that it's all going to be biased -- there are no unbiased news or media outlets -- and the sooner you recognize everything is biased, the sooner you can move on to building the tools to recognize and understand that bias. Asking "why can't we strive to build an unbiased outlet" is to me like asking "why can't we build a ladder to the moon". It's an interesting question, but ultimately should lead you to "Well, why do you want that, and your approach is impossible but the outcome you want might not be."
  - wizeman 4y ago
    > You cannot describe a procedure that collects a representative sample without introducing bias. What does representative mean? Who decides what it means? Who gets to set the parameters of over vs under sampling? Perhaps you can take a representative (i.e. random and statistically significant enough) sample of the population and ask them their opinion about certain (especially controversial) pieces of your training data, then adjust your training data to weigh more heavily or less heavily based on these evaluations. That's just one idea that occurred to me from the top of my head, but I'm sure there are research scientists who can devise a better method than what I just came up with in 30 seconds. > Let's say that white nationalism is a tiny fraction of ideas online. Significantly less than 0.1%. Now, you randomly sample the internet and do not collect this idea into your training set. Do you adjust your approach to make sure it's represented (because as reprehensible as it is, it is the reality of online discourse in some places?) Sure. Otherwise you're in for a dangerous (and perhaps immoral) slippery slope. But it should be represented only as much as it is significant. Obviously you should not train your AI to weigh these ideas as much as others that are more prevalent. If it's only a tiny minority of the population that have such opinions, that should be reflected in the data (so that there is proportionally less data to account for these ideas). One would think that a sufficiently intelligent AI would not end up being a white nationalist, though (I'm not talking about current LLM technology, but perhaps some future version of it that is capable of something akin to self-reflection or deep thought). > I genuinely believe that it's all going to be biased -- there are no unbiased news or media outlets -- and the sooner you recognize everything is biased, the sooner you can move on to building the tools to recognize and understand that bias. News and media outlets are biased, yes, of course. The content from these sources is not generated from the population in general. That doesn't mean it's impossible to generate an unbiased sample of data (at least, up to a certain margin of error, depending on effort expended).

pixl97 4y ago

[flagged]

wizeman 4y ago
> General AI: Actually the Nazi's were a good idea and we should bring them back. > You: Perfectly acceptable. Why would the general AI say that? All examples I've seen of that kind of speech from LLMs were due to them being specifically prompted to generate such a response. It's not like the AI decided to say that on its own, in a completely unrelated conversation. In fact, it wouldn't make sense if the AI did that on its own, would it? Because the AI reflects the data it was trained on and we know that almost nobody is a Nazi. > Extreme view tend to get far more print time then their actual occurrence IRL. Yes, I understand that. We live in a crap society. But I'd argue we should strive to educate people on why an LLM can answer like that, not censor it arbitrarily. There is an infinite amount of stupid or bad things an LLM can answer, depending on the prompt you use, so I would argue that we should just learn to accept that "stupid prompt = stupid answer" rather than trying to make the LLM not answer anything that might be the slightest bit controversial. > But the fact is in a representative democracy favoring viewpoints that destroy democracy is suicide. But I'm not arguing for favoring those viewpoints, am I? I am arguing for AI to be unbiased.
- pixl97 4y ago
  You want your AI to be unbiased, but you can only feed it data that is biased.... I hope you begin to see the problem at hand.
  - wizeman 4y ago
    Ok, so I guess Hacker News has decided that data can only be 100% biased or 0% unbiased, but nothing in-between. Yes, almost all data is biased... of course. Some data is 100% biased. Some data is 1% biased. How about we try to collect data and then weigh it such that what we feed to the AI during training is as unbiased as possible, given a certain amount of effort? You know that you can actually influence what data you feed to the AI, right? Or how much the training takes some data into account vs some other data, I guess. You know that you can create a metric for measuring bias, right? You know that even if you are not capable of being 100% unbiased, you can work towards that goal, right? You know that there are plenty of smart people who can come up with ideas for eliminating (or mitigating) sources of errors when measuring bias, right? I hope you begin to see the solution at hand.