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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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If my model generates pictures of people from an even ethnic spectrum, I do not believe I am discriminating against anyone. It's not the Harvard auditions, and this isn't actual people I'm failing to generate or generating excessively.
If I'm a designer using a generative AI to fill a world of my making, I do not care for "accurate" demographic representation being baked in the model. Either I know exactly what ratios I want and include them in the prompt (and if I'm the type to want "accuracy", I already know who I want to be 13% of the generated population yet 50% of the generated criminals), or otherwise it should give me a selection of 50% male, 50% female, [for race in races generate total*race/races] American presidents.
I see the division between "factual predictors" and "diverse generators" AI models to be quite acceptable and even desirable. Make it explicit and call out those who present their model as one when it in fact has characteristics of the other.
By this logic, then no model is discriminating against anyone. If a model only ever returns white women for "good person" and black men for "bad person," then that's not discriminating against anyone since it's not the Harvard auditions, and this isn't actual people you're failing to generate or generating excessively. Great, looks like we can associate any race with any quality we want in our model and not worry about discriminating against anyone!
Indeed, and notably no one seems to have any problem with such models existing. There's room for multiple types of generative AI models in this world, including ones that have uniform distributions, desired distributions, best-attempt-at-accurate distributions, or really any other arbitrary distributions of demographics.
Do you really think your example is as egregious as generating perfectly uniform selections for both "good person" and "bad person" (or "criminal" and "Harvard student", for that matter)?
What does being egregious have to do with this in any way? As you wrote, either way, "it's not the Harvard auditions, and this isn't actual people I'm failing to generate or generating excessively." Given that the reason that perfectly uniform selections isn't discrimination has literally nothing to do with the type of distribution and everything to do with the fact that these are generated images rather than actual people, we can change the distribution to anything we want (including my example of encoding "good person" with "white woman" and "bad person" with "black man") and still land at the same result of "no discrimination is taking place."
Pretty clear to me that the analogy you deployed was deliberately absurd.
But anyway, provided that you clearly label your Pro-White-Women, Anti-Black-Men model as such, go ahead.
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