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> Ignoring the (many) arguments that IQ is not really a great marker for intelligence, there's basically no value in trying to predict IQ based on facial struct

• joshuamorton 6y ago
  When doing anything prediction-y, you need to take the relative value of false positives and negatives into account. Can you describe a situation where the harm caused to someone by mis-classifying their IQ is outweighed by the increased efficiency from...being able to more quickly identify someone's IQ? Like, can you even describe a situation where this will be used ethically at all? What use is there for predicting someone's IQ with dubious accuracy, assuming you already have their permission?
  • darawk 6y ago
    Sure. But you also need to account for the actual baseline you're comparing to. Whatever process it might be that's taking someone's IQ as an input is right now being handled in some subjective manner by other humans. Do you think their error rate is zero? These things don't need to be perfect to be good. They just need to be better than what we're doing now. And as per all the recent unrest, it turns out that what we're doing now isn't all that great.
    • joshuamorton 6y ago
      > Whatever process it might be that's taking someone's IQ as an input is right now being handled in some subjective manner by other humans. Yes. > Do you think their error rate is zero? Well it depends. Do I think the error rate of IQ as a predictor of underlying intelligence is flawed? Yes. Do I think that IQ as a measure of IQ is flawed? Still, often, yes. > These things don't need to be perfect to be good. To my point above, I generally am not a fan of IQ in general. Psychology researchers are consistently finding new and interesting ways that IQ tests are socially/culturally/environmentally influenced and that exams may not be fair. If your claim is that you can somehow train an algorithm to look at someone's face and evaluate their underlying intelligence better than the tools we have today, honestly "delusional" is the first word that comes to mind. That's just not something that currently available tools can do. And thought experiments about a "perfect" classifier aren't interesting when we're discussing applied ethics. And that comes with the huge, enormous, gigantic, asterisk that you can even measure "intelligence". Like, it comes with the caveat that you can even reasonably define intelligence. We generally measure intelligence as correlated with success at some metric: math problems, visual puzzles, chess, reading comprehension, whatever. If you're contending that a hypothetical model could predict underlying intelligence better than the measures we have today, how would we even know? When training a model you have an evaluation function. If the evaluation function is flawed (which is essentially your contention, and which I absolutely agree with), the trained model will exhibit biases that reflect the flaws of the evaluation function. An ML model isn't suddenly going to solve the cultural issues we have with measuring IQ. It will encode the same biases that society does, because the model will try as hard as it can to do exactly what the human proctors would have done. These are exactly the kinds of broader ethical questions that Timnit points out we need to worry about.
      • darawk 6y ago
        IQ isn't the point though. I'm not a huge fan of IQ specifically either. Your original objection was intrinsic to the idea of detecting things from faces. These objections are to the quality of the measures. I'm not going to defend IQ as a useful measure, because it has a ton of problems and that's not my point here. > If the evaluation function is flawed (which is essentially your contention, and which I absolutely agree with), the trained model will exhibit biases that reflect the flaws of the evaluation function. An ML model isn't suddenly going to solve the cultural issues we have with measuring IQ. It will encode the same biases that society does, because the model will try as hard as it can to do exactly what the human proctors would have done. Of course. But so what? There are people using IQ now for things. An ML model isn't going to magically make those biases worse, either. What it's going to do is bring them to the surface, so that they are quantifiable and we can actually do something about them. ML is the solution to the bias problem. Right now these evaluations are being made by other humans. Humans who we cannot statistically debias. Humans who's biases we can't even effectively interrogate. The reason people are making all these memes about biased AI is not that it's more biased than humans, it's that the bias is more measurable.
        • joshuamorton 6y ago
          > An ML model isn't going to magically make those biases worse, either. Well, actually, research shows that unless great care is taken it absolutely can. If you include for example race as a factor in a model, it can learn non-causal correlations between race and whatever the objective is. This can have a compounding effect in some cases. [0] > What it's going to do is bring them to the surface, so that they are quantifiable and we can actually do something about them. I don't follow this. If we have a biased objective function, the model won't surface any biases we weren't already cognizant of in the objective function. And they were already quantifiable: we had a function that we were using to evaluate the model. We could use that same function on whatever non-model evaluation we were doing. > ML is the solution to the bias problem. This is basically directly in contradiction to what leading experts on the subject say. ML cannot fix bias in human systems, unless we presuppose that those systems are biased, in which case we can often address the bias in the human systems directly without ML. > Humans who we cannot statistically debias. Humans who's biases we can't even effectively interrogate. You can still have decisions be made by objective expert systems without complex ML. If you want to learn someone's IQ, the best way is to debias the IQ test, not to try and infer it from their face bones. > it's that the bias is more measurable If we can measure the bias in the output of an ML model, we can equivalently measure the bias in the output of a human system. You're presupposing the existence of some unbiased objective function which we don't have, and that's at the core of the issue. [0]: https://www.wired.com/story/ideas-joi-ito-insurance-algorithms/ https://www.wired.com/story/ideas-joi-ito-insurance-algorith... has a few good examples here, like how naive bail and sentencing models encode racial bias that isn't present in humans. And to be clear the response here shouldn't be "well let's just build better models" but "why do we think a model will improve the situation here at all"? Removing agency from Judges has historically been bad for the average person convicted of a crime. This doesn't mean that individual judges can't make terrible rulings, but that the alternatives are usually worse on the whole.