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The real problem with AI ethics and fairness is that nobody actually listens to ethicists or philosophers. Nobody takes the subject seriously. It’s just a hype

by mlthoughts2018 6y ago

The real problem with AI ethics and fairness is that nobody actually listens to ethicists or philosophers. Nobody takes the subject seriously. It’s just a hype vehicle to cram in whatever social justice outrage du jour that some liberal source wants to push as an agenda. I say this as a liberal who is sympathetic to most of those issues, but finds their representation in AI ethics or fairness debates to be ignorant and juvenile power grabs. The whole Timnit Gebru affair is a direct example. The paper Google didn’t want to approve for publication was really poor. Lots of specious arguments. Lots of declarations of opinions as if they were fact (eg. nobody is required to agree with or accept woke vocabulary, let alone ensure NLP models are kept updated with it), and lots of sanctimonious assertions about various agendas that nobody has to agree “are right” and have zero appropriateness within a science publication.

nefitty 6y ago
This makes sense to me. For example, there are other farther-left viewpoints that I haven’t heard represented or being advocated for, specifically thinking of animal rights. When I can pick out viewpoints like that so easily, it makes me wonder if the ideas being pushed are not historically arbitrary. Will we one day need to make AI take on ideas of animal liberation because, as you stated, that becomes “social justice outrage du jour”? What makes that less pressing at this moment in time?
- deleted 6y ago
  [deleted]
- mlthoughts2018 6y ago
  That is a great example, exactly the point I am suggesting. The economist and futurist Robin Hanson wrote really well about this in the article “Inequality Talk Is About Grabbing” [0]. The premise is: why do we care about some forms of inequality (financial inequality among households in one nation, or protected class inequality within employment law in one nation), but we don’t care about other things (inequality between socially lonely people and social butterflies, inequality across country boundaries, inequality of physical attractiveness). Hanson suggests it just so happens that we develop “morality” around some forms of inequality because it offers easy ways to politically coordinate to grab money or power and punish your rivals. Other forms of inequality that involve just as much harm, but where it’s not politically easy to drum up support for physically taking money or power, just happen to conveniently not turn out to be big moral concerns, or may even have other competing morals invented over time that make it “wrong” to even treat it as inequality (like the hallowed status of a “right” to control what happens to your body counting for more than inequality based harm of variation in genetic sexual attractiveness outcomes - which is a massive double standard compared with no such “right” to control what happens to the fruits of your labor or intelligence, which undergo forced redistribution via taxes because we just so happen to believe inequality of financial outcomes is not acceptable). For example, most people don’t give a shit about animal suffering on the scale of agribusiness. Most people don’t give a shit if ugly people suffer loneliness or unhealthy lack of sex or job discrimination from their appearance. Most people don’t care much about structural inequality in countries besides their own. These are just convenient moral omissions because in each situation it is not easy to see how you could “redistribute” attractiveness, social companionship, or power structures of some other country. Plus animals don’t have any money or status to give you in exchange for allyship, while big agribusiness has plenty of lobbying power to crush you with. I wish more people appreciated this. We don’t, as a society, advocate for race, gender, or pay equality because it’s “right.” We just know that aging white patriarchy is full of convenient targets we can take money and status from. Conveniently for other domains of inequality, like animal welfare, where no such easy targets exist to take from, it just somehow doesn’t qualify as a moral issue worthy of advocacy. [0]: https://www.overcomingbias.com/2013/08/inequality-is-about-grabbing.html https://www.overcomingbias.com/2013/08/inequality-is-about-g...
joshuamorton 6y ago
Could you cite what part of the paper you're talking about? Since it's now public[0], it's fairly easy to verify what you're talking about. The word "woke" doesn't appear in the paper. If I'm interpreting your argument correctly, I think you're criticizing either section 4.1 Size Doesn’t Guarantee Diversity, or section 4.2 Static Data/Changing Social Views. But your criticisms don't track with either of those sections. 4.2 seems the best fit (since they discuss keeping models up to date with modes of communication). The claim made in the paper seems to be that an LM trained today will fail to keep up with shifts in language (and these can happen quickly, in months, not years in many contexts). This is true whether the context is "woke vocabulary" whatever that means, reclaimed slurs (such as "queer"), or memetic slang/shibboleths ("kek" or "do you listen to girl in red"). I don't see anyone saying that language models or designers are required to agree with "woke vocabulary". The closest I can come is the authors suggesting that models which fail to stay up to date with shifts in vocabulary (including, I guess, woke vocabulary) will be less effective. That seems trivially true. Can you clarify? [0]: https://faculty.washington.edu/ebender/papers/Stochastic_Parrots.pdf https://faculty.washington.edu/ebender/papers/Stochastic_Par...
- mlthoughts2018 6y ago
  I feel my comment tracks extremely well with the sections you cite and that it takes some mental gymnastics to read exactly the link you provided and then act like my comments somehow don’t track. The original criticisms were about failure of source data for large models (eg reddit for GPT) to account for fast shifts in social activism language specifically. The other major problematic section is on energy use and unqualified comparison of units of large model training with carbon emissions of other activities (with no attempt to account for societal value of ML model research or training, nor economies of scale as training technology democratizes it and makes it cheaper). It is similar to the incredibly specious diatribes from Stephen Diehl lately balking at the raw energy numbers of Bitcoin mining, as if those numbers mean anything or relate to any conclusions on their own. I wrote up a lot more of my thoughts on Gebru’s bad article when it was first leaked. Here’s that comment: - https://news.ycombinator.com/item?id=25314824 https://news.ycombinator.com/item?id=25314824 I also feel you are very commonly involved in lengthy downvote / flagging threads with disingenuous takes on others’ comments and deliberate unwillingness to apply charitable takes or avoid taking things out of context. I am sure from your POV you don’t feel that way and I’m not asking anyone to agree with my assessment; I have zero goal of citing things to back it up. I am just going to preemptively drop off from replying to you any further.

nefitty 6y ago

This makes sense to me. For example, there are other farther-left viewpoints that I haven’t heard represented or being advocated for, specifically thinking of animal rights. When I can pick out viewpoints like that so easily, it makes me wonder if the ideas being pushed are not historically arbitrary. Will we one day need to make AI take on ideas of animal liberation because, as you stated, that becomes “social justice outrage du jour”? What makes that less pressing at this moment in time?

deleted 6y ago
[deleted]
mlthoughts2018 6y ago
That is a great example, exactly the point I am suggesting. The economist and futurist Robin Hanson wrote really well about this in the article “Inequality Talk Is About Grabbing” [0]. The premise is: why do we care about some forms of inequality (financial inequality among households in one nation, or protected class inequality within employment law in one nation), but we don’t care about other things (inequality between socially lonely people and social butterflies, inequality across country boundaries, inequality of physical attractiveness). Hanson suggests it just so happens that we develop “morality” around some forms of inequality because it offers easy ways to politically coordinate to grab money or power and punish your rivals. Other forms of inequality that involve just as much harm, but where it’s not politically easy to drum up support for physically taking money or power, just happen to conveniently not turn out to be big moral concerns, or may even have other competing morals invented over time that make it “wrong” to even treat it as inequality (like the hallowed status of a “right” to control what happens to your body counting for more than inequality based harm of variation in genetic sexual attractiveness outcomes - which is a massive double standard compared with no such “right” to control what happens to the fruits of your labor or intelligence, which undergo forced redistribution via taxes because we just so happen to believe inequality of financial outcomes is not acceptable). For example, most people don’t give a shit about animal suffering on the scale of agribusiness. Most people don’t give a shit if ugly people suffer loneliness or unhealthy lack of sex or job discrimination from their appearance. Most people don’t care much about structural inequality in countries besides their own. These are just convenient moral omissions because in each situation it is not easy to see how you could “redistribute” attractiveness, social companionship, or power structures of some other country. Plus animals don’t have any money or status to give you in exchange for allyship, while big agribusiness has plenty of lobbying power to crush you with. I wish more people appreciated this. We don’t, as a society, advocate for race, gender, or pay equality because it’s “right.” We just know that aging white patriarchy is full of convenient targets we can take money and status from. Conveniently for other domains of inequality, like animal welfare, where no such easy targets exist to take from, it just somehow doesn’t qualify as a moral issue worthy of advocacy. [0]: https://www.overcomingbias.com/2013/08/inequality-is-about-grabbing.html https://www.overcomingbias.com/2013/08/inequality-is-about-g...

joshuamorton 6y ago

Could you cite what part of the paper you're talking about? Since it's now public[0], it's fairly easy to verify what you're talking about. The word "woke" doesn't appear in the paper. If I'm interpreting your argument correctly, I think you're criticizing either section 4.1 Size Doesn’t Guarantee Diversity, or section 4.2 Static Data/Changing Social Views. But your criticisms don't track with either of those sections. 4.2 seems the best fit (since they discuss keeping models up to date with modes of communication). The claim made in the paper seems to be that an LM trained today will fail to keep up with shifts in language (and these can happen quickly, in months, not years in many contexts). This is true whether the context is "woke vocabulary" whatever that means, reclaimed slurs (such as "queer"), or memetic slang/shibboleths ("kek" or "do you listen to girl in red"). I don't see anyone saying that language models or designers are required to agree with "woke vocabulary". The closest I can come is the authors suggesting that models which fail to stay up to date with shifts in vocabulary (including, I guess, woke vocabulary) will be less effective. That seems trivially true. Can you clarify? [0]: https://faculty.washington.edu/ebender/papers/Stochastic_Parrots.pdf https://faculty.washington.edu/ebender/papers/Stochastic_Par...

mlthoughts2018 6y ago
I feel my comment tracks extremely well with the sections you cite and that it takes some mental gymnastics to read exactly the link you provided and then act like my comments somehow don’t track. The original criticisms were about failure of source data for large models (eg reddit for GPT) to account for fast shifts in social activism language specifically. The other major problematic section is on energy use and unqualified comparison of units of large model training with carbon emissions of other activities (with no attempt to account for societal value of ML model research or training, nor economies of scale as training technology democratizes it and makes it cheaper). It is similar to the incredibly specious diatribes from Stephen Diehl lately balking at the raw energy numbers of Bitcoin mining, as if those numbers mean anything or relate to any conclusions on their own. I wrote up a lot more of my thoughts on Gebru’s bad article when it was first leaked. Here’s that comment: - https://news.ycombinator.com/item?id=25314824 https://news.ycombinator.com/item?id=25314824 I also feel you are very commonly involved in lengthy downvote / flagging threads with disingenuous takes on others’ comments and deliberate unwillingness to apply charitable takes or avoid taking things out of context. I am sure from your POV you don’t feel that way and I’m not asking anyone to agree with my assessment; I have zero goal of citing things to back it up. I am just going to preemptively drop off from replying to you any further.