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I wrote a post about de-biasing efforts in machine learning, which got a bit long, so I decided to turn it into an article instead. It's about how corporate anti-bias solutions are mostly only designed to cover their asses, and does nothing to solve the larger (actually important) issue.
(As an aside: does it still count as a "bare link" if I point to my own content, just hosted elsewhere?)
You are right, they are not really trying that hard. Anybody smart enough to build bleeding-edge AI systems is smart enough to understand why if you try to predict the likelihood of a criminal repeating a crime, it will always say that black people are more likely to repeat (it's because black people are more likely to repeat). The problem is fairly hopeless, because AI's accurately that black people are more likely to commit crimes, women are for the most part uninterested in studying machine learning, and other things that true but verboten.
So their manager asks them to do something about bias, and they apply the laziest possible hack. I think this disinterest is more prominent in top-tier researchers. Low-end researchers who will never accomplish anything useful are happy to feast on the de-biasing funding teat.
There are some other niche cases, like facial recognition software not recognizing blacks. But this requires no special debiasing effort, it is simply a weakness in the system that can be addressed the same as any other weakness.
Obviously, the only possible "de-biasing" technique that can work is explicitly biasing systems against white men. If two criteria are mutually conflicting such that one group or the other must be "discriminated" against according to one criteria or the other, choose the criteria that discriminates against white men (in that order: first discriminate against whites, then discriminate against men). It is very simple.
No, it’s not obviously the only workable technique.
Half the point of this parent article was about the confusion, willful or otherwise, between the objective and the normative. Is the model supposed to be drawing like a human, or like an idealized paragon of virtue? If you ask it to draw a medieval knight (per someone else downthread) do you want it to match history or popular culture?
Predictive ML is a clear example of the objective case. Given the lack of a market for a robot which lies to you, such robots aren’t popular. Generative ML is a different story.
Train on data from the world as it is, and you’ll get relative objectivity, plus journalists will call you racist. Papering over that with prompt engineering is a hack to get more normativity for an AI which doesn’t “need” to be objective. But this suggests another strategy: don’t train it on the world as it appears to be. Train it on the world that could be.
Is creating such training data trivial? No. Does it require discriminating against anyone? Also no. Seems like a decent idea to me.
Presuming that this theoretical "world that could be" is at all different from the "world as it appears to be", it absolutely requires discriminating against someone. There's just no way to bridge that difference without applying some discrimination against someone at some point in the process; otherwise we'd just end up back where we started.
Not as long as we’re talking about generative instead of predictive. For example, an AI which does everything like DALL-E except it doesn’t need prompt engineering to make diverse pictures.
Imagine a specialized net which only draws armor. We can train it on the entire internet, in which case it will draw us Renaissance artisanal plate, which has been inserted into countless historically-inaccurate pieces of media. If we instead only use a historical database of curated and tagged armors, it will end up with a better understanding of actual medieval armor. It will also be much less flexible, but it will be defended against criticisms of renaissancism.
So by your lights, what would it take for a generative AI model to be discriminatory against someone? An "AI that does everything like DALL-E except it doesn't need prompt engineering to make 'diverse' pictures" would necessarily involve discriminating against someone when creating the training set; if no such discrimination were required, then the 2 models would be identical. You seem to be saying that that doesn't count. So what would count?
I think there are a couple uses of the word "discriminatory".
First, yeah, defining a training subset is a decision to favor one group over another. Making the historically-accurate-armor AI is going to disappoint the fantasy-art users. An AI that makes everyone [insert feature here] is going to disappoint the people wanting it to be realistic. And of course there's nothing stopping the dataset curator from excluding all members of a race, or all Christians, or all women with realistic proportions; doing so would be obviously discriminatory.
On the other hand, a sentencing or profiling AI is categorically different. When a predictive machine discriminates, it is causing a disparate impact on actual people rather than on the collective. I'm struggling to find the right words...it is the difference between perpetuating an injustice vs. causing it, a sort of active vs. passive harm.
(I think there's also an argument to be made that the ill-trained AI is more obvious that an AI trained well on an evil world, and thus more compatible with exit-rights liberalism, but I'm not so confident in that...)
The OP argued that predictive AI was a zero-sum game where only reverse racism could compensate for the inbuilt discrimination. I think...there's some degree to which that is correct? It's extending that conclusion to generative AI that strikes me as wrong. I believe generative discrimination is an easier problem than predictive because it's a lesser problem. Maybe you can't make a "world that could be" generative AI without excluding anyone, but I'm reasonably sure you can make one without actively harming any individuals.
Anyway, thanks for pressing me on this. It's an interesting philosophical topic.
OK, so it sounds like based on this post that when you wrote:
about a theoretical generative AI model based around a world that could be (instead of the world as is), you were making a general statement about how no generative AI model could possibly discriminate against someone. Which seems like a strange thing to say about 1 specific type of generative AI model (i.e. based on a world that could be) when discussing 2 different theoretical types of models that are both generative (i.e. the other one being based on the world as it is), but technically true, I suppose.
More or less, though I wouldn't frame it as an absolute, if only because that's inviting extreme counterexamples.
The original article was writing about unimpressive debiasing in corporate generative models. When sulla responded with the assertion that this is unavoidable in a bleeding-edge AI, I thought it appropriate to point out that his examples were all predictive rather than generative. I think that really undermines his equation of discrimination with the "true but verboten." A generative AI could be wildly discriminatory, in the loose sense, without ever discriminating in the narrow, personal sense, and it is the latter which is a one-way ticket to a media circus.
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