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I understand, and generally agree with, the idea that teasing apart genetic and social causes of behavior is difficult and unlikely to yield useful results, and

I understand, and generally agree with, the idea that teasing apart genetic and social causes of behavior is difficult and unlikely to yield useful results, and especially unlikely to yield useful results when the people attempting to do it don't know what they're doing and don't have any incentives for getting it right. I think my true objection is to the idea that we can't make useful inferences, even in principle, based on appearance. I suppose now would be a good time to check whether you actually hold that opinion: I'm assuming yes based on the following exchange that you do. >> There was a previous [study] that was reporting some degree of success in determining whether someone was homosexual via ML. > Yes, which was also phrenological in nature. > ... > The point is that neither this study nor the homosexuality study have a relatively consistent relationship to observable reality. Your priors on us being able to predict, independent of social conditioning, some arbitrary social attribute based on someone's face should be very, very low. If I've learned one thing from looking into machine learning and such over the past few years, it's that the computer is not looking at what you think it's looking at. Yes, it's _possible_ that the computer has learned which varieties of facial bone structure correlate with homosexuality. I find it equally plausible that the computer determined that there was a difference in personal grooming habits or facial expressions between groups. Regardless of what the differences were, though, I think it's a mistake to say that you shouldn't expect to be _able_ to predict many social attributes based on their face. If you're just saying that we can't determine the underlying causes of social attributes based on machine learning, I agree, but I'm not sure that's really a useful statement. What you said could also plausibly be interpreted as "trying to predict real-world outcomes based on machine learning will result in bad outcomes", I would agree with that interpretation (there is some question of "yes it will make mistakes, but it might still be better than the current system," but much like with self-driving cars vs human drivers, I don't think we're particularly near that point yet). Still, the comparison of that study to phrenology, and your comments above, suggest you were trying to make a stronger statement, and I'm still not sure what that stronger statement is or even if there is one.

joshuamorton 6y ago
> I find it equally plausible that the computer determined that there was a difference in personal grooming habits or facial expressions between groups. Right, so then the question is if this is valuable. Consider three people: a closeted, married-to-a-woman, gay man who has hidden his sexuality. An openly gay man who looks "stereotypically gay" (this statement should start triggering warning bells, btw), and a well groomed heterosexual man who might be described as "stereotypically-gay looking". Does your model identify men 2 and 3, the stereotypically "gay"-looking men? If so you've just built a model that detects cultural signifiers for homosexuality, which, congrats I guess. This isn't a useful tool, is it? It is a model that encodes stereotypes. But we know the stereotypes already, because we use them as intentional cultural signals. So nothing of value was added, and the model may serve to perpetuate those stereotypes in the future if people wish to change them. Does your model identify men 1 and 2? Well now you've built something interesting, it's found a deeper relationship than just overt social signifiers, perhaps it's noticing some minute bone structure thing. Is it ethical? Hell no. In essence the stronger statement is that you are either simply encoding cultural biases into a model, which will serve to perpetuate those biases (and is therefore harmful), or you are building something terrifying and dystopian and harmful.
- JoshuaDavid 6y ago
  Ah. Yeah, I'm basically saying "lean towards terrible and dystopian and harmful, not useless". If you tell people it's useless, dystopian, and harmful, and they find out it's actually useful, they may not take seriously the "dystopian and harmful" bits. It sounded kind of like you thought it wouldn't be possible to get interesting results which tell you something you didn't already know from ML with much better than random chance. >> I find it equally plausible that the computer determined that there was a difference in personal grooming habits or facial expressions between groups. > Right, so then the question is if this is valuable. That's not really my question, but I'd argue that the answer is "it depends on how you're trying to use it". If you're an advertiser of questionable ethics, and trying to promote your local gay bar, it probably is useful to you. Likewise if you're a computer science professor trying to get grant funding. If you mean "useful to society as a whole", probably not. Which comes down to the difference between "this will accomplish the goals of the people who want to use it" and "letting those people use this tool is a good idea". The danger is that if you try to convince people not to use the tool because it doesn't work, but the tool does in fact work, trying to convince them later because the results are harmful is less likely to work.
  - joshuamorton 6y ago
    > It sounded kind of like you thought it wouldn't be possible to get interesting results which tell you something you didn't already know from ML with much better than random chance. In general I do think this is true, but it's not verifiable. Like I said, in general I think that the priors that underlying facial structure should be strongly correlated with sexual orientation are very low. It's not impossible, just unlikely, so when you get "results", the most likely explanation is not that you've found something profound, but that you're encoding social biases. And so from the perspective of a researcher, you haven't actually accomplished your goals (of discovering something profound), you've instead committed a relatively elementary error that legitimizes certain forms of stereotyping. And again I note that my priors suggest that this is the most likely outcome.
- zajio1am 6y ago
  > But we know the stereotypes already, because we use them as intentional cultural signals So call that tool 'detector of cultural signals'. Plenty of people do not care about cultural signal knowledge, but a tool that allows to read and analyze cultural signals by AI predictors can be interesting. Such tool could do similar work as expert in given cultural field, but can be used by non-experts.

joshuamorton 6y ago

> I find it equally plausible that the computer determined that there was a difference in personal grooming habits or facial expressions between groups. Right, so then the question is if this is valuable. Consider three people: a closeted, married-to-a-woman, gay man who has hidden his sexuality. An openly gay man who looks "stereotypically gay" (this statement should start triggering warning bells, btw), and a well groomed heterosexual man who might be described as "stereotypically-gay looking". Does your model identify men 2 and 3, the stereotypically "gay"-looking men? If so you've just built a model that detects cultural signifiers for homosexuality, which, congrats I guess. This isn't a useful tool, is it? It is a model that encodes stereotypes. But we know the stereotypes already, because we use them as intentional cultural signals. So nothing of value was added, and the model may serve to perpetuate those stereotypes in the future if people wish to change them. Does your model identify men 1 and 2? Well now you've built something interesting, it's found a deeper relationship than just overt social signifiers, perhaps it's noticing some minute bone structure thing. Is it ethical? Hell no. In essence the stronger statement is that you are either simply encoding cultural biases into a model, which will serve to perpetuate those biases (and is therefore harmful), or you are building something terrifying and dystopian and harmful.

JoshuaDavid 6y ago
Ah. Yeah, I'm basically saying "lean towards terrible and dystopian and harmful, not useless". If you tell people it's useless, dystopian, and harmful, and they find out it's actually useful, they may not take seriously the "dystopian and harmful" bits. It sounded kind of like you thought it wouldn't be possible to get interesting results which tell you something you didn't already know from ML with much better than random chance. >> I find it equally plausible that the computer determined that there was a difference in personal grooming habits or facial expressions between groups. > Right, so then the question is if this is valuable. That's not really my question, but I'd argue that the answer is "it depends on how you're trying to use it". If you're an advertiser of questionable ethics, and trying to promote your local gay bar, it probably is useful to you. Likewise if you're a computer science professor trying to get grant funding. If you mean "useful to society as a whole", probably not. Which comes down to the difference between "this will accomplish the goals of the people who want to use it" and "letting those people use this tool is a good idea". The danger is that if you try to convince people not to use the tool because it doesn't work, but the tool does in fact work, trying to convince them later because the results are harmful is less likely to work.
- joshuamorton 6y ago
  > It sounded kind of like you thought it wouldn't be possible to get interesting results which tell you something you didn't already know from ML with much better than random chance. In general I do think this is true, but it's not verifiable. Like I said, in general I think that the priors that underlying facial structure should be strongly correlated with sexual orientation are very low. It's not impossible, just unlikely, so when you get "results", the most likely explanation is not that you've found something profound, but that you're encoding social biases. And so from the perspective of a researcher, you haven't actually accomplished your goals (of discovering something profound), you've instead committed a relatively elementary error that legitimizes certain forms of stereotyping. And again I note that my priors suggest that this is the most likely outcome.
zajio1am 6y ago
> But we know the stereotypes already, because we use them as intentional cultural signals So call that tool 'detector of cultural signals'. Plenty of people do not care about cultural signal knowledge, but a tool that allows to read and analyze cultural signals by AI predictors can be interesting. Such tool could do similar work as expert in given cultural field, but can be used by non-experts.