7 ms·

Deep neural networks more accurate than humans at detecting sexual orientation

97 points by fotcorn 9y ago

jonahx 9y ago
In cultures where homosexuals are persecuted, a tool like this could be devastating.
- purrcat259 9y ago
  Both for the true positives and the false positives.
- hnarayanan 9y ago
  They mention this in the article. Even so, it is disheartening. It appears we're so fascinated by all the avenues tools like deep learning could open up, to ask more basic questions like the implications of pursuing such research.
  - kdelok 9y ago
    The first author of this also developed tools that allow you to analyse all sorts of personality traits (including sexual orientation) via FB likes. He was aware and publicised of the potential consequences and risks here, but I seem to remember thinking his reasons for publishing were justified at the time. As he was part of the psychology department during his PhD, I think his papers will have been extensively scrutinised by ethics boards and the like. Mini disclaimer: I vaguely knew him from university and didn't get on with him. However, I don't think he was the type of person to put people at risk for personal glory.
- kwhitefoot 9y ago
  Or anything else that someone might label deviant.
- Chris2048 9y ago
  If that were possible, humans could already detect homosexuals. It's likely in those countries that homosexuals do not present themselves the same.
JulianMorrison 9y ago
This is very likely the same as detecting women by "they have long hair" - it's not finding intrinsic characteristics, but social ones.
- nthcolumn 9y ago
  And in the context of privacy with regard to public surveillance systems with facial recognition capability your point is exactly what?
  - JulianMorrison 9y ago
    Social characteristics are voluntary. People who are hiding won't adopt them.
    - speedplane 9y ago
      Homosexual men will have to start wearing ill-fitting suits and dirty gym shorts to avoid being caught by the algorithm.
      - nthcolumn 9y ago
        Totally agree! Heterosexual women will have to wear makeup and grow their hair long to avoid being misidentified as lesbians. Personally I think it is wishful thinking on the part of people who think that you aren't born gay. This algorithm will out even the most repressed homosexual as it is not entirely based on 'social characteristics'. Science doesn't care what you think.
        speedplane 9y ago
        Gay men and women have blended in undetected for thousands of years all over the world. They've only been able to really come out in the last few decades. If this miserable distopia of automatic homosexual detection came to pass, they would simply go back to blending in again. A miserable existence, but pretty sure they'd be able to fool the robot overlords.
quantum_state 9y ago
Is this serious?
- dx034 9y ago
  Research around this existed for years, it could be shown that gays can more easily detect the sexual orientation of others. One explanation could be that since homosexuality was/is banned in many cultures, this helped to survive. A reason why we didn't read much about it is that most researchers know that you shouldn't publish about this. Otherwise you can quickly end up with blood on your hands.
  - quantum_state 9y ago
    AI DL ... etc. are just tools ... if one is not careful, one would run the risk of GIGO == garbage in, garbage out ...
dx034 9y ago
I would really hope that they don't publish more information and keep algorithms as secret as possible. Algorithms like this can have huge implications in countries where homosexuality is banned. If this really works, people could end up in jail just because a neural network determines that they look gay. And even if it doesn't work, some governments or local communities could still use it to ban people because of the chance that they're gay. While I'm not a fan of censoring research, I think this is a case where the research community should refrain from publishing results that could help replicating such algorithms. EDIT: Just to be clear, I don't say that this study makes sense or works. But there are areas where large parts of the population really hate homosexuality. Just thinking this could work can make them use such a system. Especially in situations where you can afford false positives. So next time you want to travel to a country it could happen that they don't let you in because the algorithm says that you're likely gay.
- rem1313 9y ago
  It wouldn't take long for a government to replicate the study even if it is not published if it is sufficiently interested
  - speedplane 9y ago
    Getting a prototype working would take a week for a skilled machine learning engineer, assuming they had access to well labeled tagged data. What will be difficult is to push the accuracy up to get actionable information from it. Assuming you're an evil repressive regime, even if you're system had 90% accuracy, you'd be falsely accusing a huge number of people and doubt even a repressive homophobic regime would implement it. Getting that 90% accuracy to 99.9% or higher would take a huge effort, this study isn't anywhere close. That said, the concerns are real. Automatically sorting hetero from homosexual people, Jews from Christians, or even black from white comes with a tone of moral issues.
    - peteretep 9y ago
      Getting buy-in from an evil repressive regime will be hard if we assume they have a population distribution like anyone else which means a sizeable proportion of those in power are gay but not openly.
pulse7 9y ago
With Facebook profile photos you could detect sexual orientation with 81% probability for man and 74% for women... Hmmm... And with 91% / 83% probability when using 5 photos/person in the learning process...
- BeniBoy 9y ago
  Well, I am not sure this is the case.. The metric seems to be the ability to tell apart pairs of straight and gay people. >Among men, the classification accuracy equaled AUC = .81 when provided with one image per person. This means that in 81% of randomly selected pairs—composed of one gay and one heterosexual man—gay men were correctly ranked as more likely to be gay. Pretty different from predicting one's sexual orientation.
vectorEQ 9y ago
'breaking news: neural network speeds up personal intepretations of scientist a bit'
- davidgerard 9y ago
  yep. This is phrenology. Bias laundering at best.
  - rbanffy 9y ago
    I was just imagining sourcing mugshot sites and then tracking those who were tried and considered guilty of whatever they got the mugshot for to train a good/bad person model. It's fun, but I'd never dare to make such a dumb thing public.
    - halflings 9y ago
      This article [0] discusses a paper by Chinese researchers [1] that did just that. (and yes, it is quite dumb/dangerous) [0] https://medium.com/@blaisea/physiognomys-new-clothes-f2d4b59fdd6a https://medium.com/@blaisea/physiognomys-new-clothes-f2d4b59... [1] https://arxiv.org/abs/1611.04135 https://arxiv.org/abs/1611.04135
    - peteretep 9y ago
      Pretty sure there's conclusive evidence of an existing effect whereby how attractive you are plays into if you're found guilty
- tzs 9y ago
  Which personal interpretations?
jaymzcampbell 9y ago
Page 22 of the report has an image of average faces for men and women and gay / straight (bisexuality doesn't seem to be covered). > The results show that the faces of gay men were more feminine and the faces of lesbians were more masculine than those of their respective heterosexual counterpart It looks to me you're gay if you are a man wearing glasses and straight if you have a beard and rounder face. It would be interesting to see what it made of the stereotypical "bear". If you're a brunette and have a slightly thinner face then you're probably, 58%, a lesbian ¯\_(ツ)_/¯. > lesbians tended to wear baseball caps I'm really not sure what to make of this.
- dm319 9y ago
  Yes, that page caught my eye, too. My conclusion is that gay people are better-looking.
  - justinjlynn 9y ago
    Well, don't forget self-selection effects in the population sample. It's highly possible those who are better looking are more confident about posting their profiles openly for analysis. The confounders for studies such as these are multiple and treacherous.
deleted 9y ago
[deleted]
peteretep 9y ago
I can't seem to load the page, but I'd imagine this is "declared sexual orientation" rather than actual, which makes this a poor tool for discrimination
- tzs 9y ago
  The images were from a dating site. They inferred sexual orientation from the gender of the partners that the subjects were looking for according to their profiles.
  - foldr 9y ago
    That is declared sexual orientation.
4c2383f5c88e911 9y ago
The title is surprisingly not too clickbaity, although it's not as clear-cut: in order to reduce false positives (increase precision), they need to limit the number of positives (reduce recall). On 1000 samples containing 70 gay people, they were able to get a 10% false positive rate on their positive results, which were 10 people (meaning 12% of the total gay sample). The sample is a bit biased too because they pulled it from explicitly gay-oriented public social network pages (but I don't fault them for that, it would be quite hard to find a better sample). It is still an impressive result, and one that might be misused badly, despite the numerous warnings used in the paper.
- BeniBoy 9y ago
  This is the classic "false positive paradox"[1]. Commonly present in medical testing. Even if false positive rate is very low, if the positive value is very low too, the likelihood of being victim of false can be high. The exemple in the wikipedia page are very good. I had to explain that to a friend which had tested positive on the HIV test and was waiting for confirmation over the weekend. Not easy to talk math when such things happens.. I found it very troubling that doctors don't even mention that to patient and present tests as 95% effective. (In fact she was fine) [1]: https://en.wikipedia.org/wiki/False_positive_paradox https://en.wikipedia.org/wiki/False_positive_paradox
bjoernbu 9y ago
Serious question by someone who's not into this kind of stats too much: What would happen if you took a big set of Facebook profiles and train some (the same if you wanna) CNN to classify picture->f for each f in profile features. Sure, for some features, your model should be able to deliver decent precision. Does this mean that you quickly found out what features can be predicted from pictures & how well your CNN performs on that? Or is it possible that you just train models from picture->X where X is basically meaningless but significantly correlated with some feature because of the effect portrait in xkcd's "Significant" (Scientists investigate!) [1] [1]: https://xkcd.com/882/ https://xkcd.com/882/
- dm319 9y ago
  There is a tendency for machine learning (including neural networks) to over-fit data - i.e. the algorithm learns to recognise the particular data, rather than the real distinguishing predictors of the groups. As you say, these can be features that are by chance associated with what you are trying to discriminate. This is why the model is validated on a separate testing group from the training group which created it. There are lots of ways to do this, and the more sophisticated continually iterate training and testing to improve the model.
chrisloy 9y ago
If you read the paper, the photos and labels were sourced from a dating website. In my opinion, there is a good chance that the model may be overfitting to how people wish to present themselves in that context. - e.g. framing of the photo, facial expression etc. Things with a heavy amount of cultural conditioning. Some of the press around this seems a bit alarmist - I doubt you would see anywhere near this accuracy out in the real world.
- moxious 9y ago
  This is a very annoying part of non-explanatory models. I think the result defies common sense a bit, and the model can't explain why this is so. So in the circumstance, why should we believe it's generalizable?
  - speedplane 9y ago
    This also goes to the heart of the problem with deep learning on neural nets. We have this algorithm that apparently identifies homo and heterosexual people, presumably based on a variety of subtle features, but we have pretty much no clue as to which features and why. The human judges may have been less accurate, but they could likely explain each decision they made and the visual features they based their decision on.
    - PeterSmit 9y ago
      Humans are known to be unreliable in explaining how they come to conclusions as well. Humans just like to pretend they can verbalise all knowledge ;)
      - speedplane 9y ago
        Some are better at it than others, and no doubt many are pretty bad at it, but I have yet to see a neural net explain to me, accurately or not, why it came to the decision it did.
      - the_d00d 9y ago
ghgr 9y ago
Line 210: > "Gay and heterosexual people were represented in equal numbers." According to Gallup Polling, around 4% of the American population is homosexual. So, let's be generous and say that their classifier has a 80% accuracy given balanced inputs while humans have 60%. Let's sample 100 people of the dataset, 50 of which are homosexual (and the rest heterosexual). If the classifier has 80% accuracy (let's assume false_positive_rate = false_negative_balance since I didn't find information about that), it means that 80 people were correctly classified, 10 heterosexuals were misclassified as homosexuals and 10 homosexuals as heterosexuals. According to the Bayes theorem, "Given a random person, probability of being homosexual assuming that the classifier said so" = "Probability of being homosexual of a random person (the so-called prior)" <times> "Probability of classifier saying it's homosexual, assuming it's indeed homosexual <divided by> "Given a random person, probability of saying it's homosexual". Substituting, we get: P = 0.04 * 0.8 / 0.5 = 0.064 If instead of the classifier we use human "feeling", we get: P=0.04 * 0.6 / 0.5 = 0.048 In summary, 4% of the people are homosexual. If a human thinks someone is homosexual, the probability "increases" to 4.8%. If on the contrary their algorithm believes it's homosexual, the probability of actually being homosexual increases to 6.4%. Not very groundbreaking...
- dangom 9y ago
  I don't know if I can follow. You say that if their classifier had 100% accuracy, then the probability of being homosexual would increase to 8%? That doesn't make much sense.
  - BeniBoy 9y ago
    Look here [1] Very good explanation (though the op's one are too) [1]: https://en.wikipedia.org/wiki/False_positive_paradox https://en.wikipedia.org/wiki/False_positive_paradox
    - dangom 9y ago
      But why does "Given a random person, probability of saying it's homosexual" equal 0.5? I'd say that should be 0.04.
speedplane 9y ago
I'm a bit skeptical of their claim that "Human judges achieved much lower accuracy". The deep net likely found a some feature that correlated with sexuality presumably after looking at many tagged images. I wonder whether the "judges" had access to that same training set. If they were people off the street that brought in their own biases, I would suspect they would do far worse than if they were able to view the training set themselves and teach themselves what hetero and homosexual people look like. Many heterosexual people have very limited contact with homosexual people. But given a training set (or sufficient contact with homosexual people), they could learn too, and I suspect they could learn better than the machine learning algorithm. That said, this research is impressive, as well as terrifying and depressing.
- dm319 9y ago
  >That said, this research is impressive, as well as terrifying and depressing. I know why you say this, but it's only terrifying because there are so many idiots around. All this study says is that there is a strong genetic/developmental component to sexuality - something we've known about for decades. Telling people they can't be gay is like telling people they can't be tall, or black.
  - speedplane 9y ago
    I am less concerned that there may be visibly identifiable characteristics of homosexuality. I am more concerned that these features can be automatically discerned by computers on a massive scale. It's one thing for an individual homophobe to call out someone because they look gay. It's quite another for a government or organization to do that to millions of people in seconds, and then act upon it at scale.
    - xfs 9y ago
      Do you think the authors of the paper are the first to think of this idea of detecting personal traits with facial features? How much cost do you think it takes for implementing detection with this particular set of features? If it can be done it will be done. In this particular case it doesn't even cost much. You should have started to be concerned when machine learning evolved into a paradigm that is only possible to achieve any kind of real world performance with an amount of data and computing resources available only to megacorps and governments.
vfulco 9y ago
But sexual orientation is fluid according to the fringe psycho-babble pseudo science crowd.
- traitormonkey 9y ago
  Check all the people worried that 'gay' may not be a 'lifestyle choice'.
TeeWEE 9y ago
If you know would show a list of 100 heterosexual man to this neural machine, it would probably qualify a high percentage as gay. Since it was learning on a 50/50 dataset.
halflings 9y ago
Necessary link to an article by Blaise Aguera y Arcas, the lead of an ML team at Google: "Physiognomy’s New Clothes" [0] He gives several examples, and explains how these type of statements are extremely misleading (and dangerous!), and not something new at all... https://medium.com/@blaisea/physiognomys-new-clothes-f2d4b59fdd6a https://medium.com/@blaisea/physiognomys-new-clothes-f2d4b59...
grizzles 9y ago
Total garbage. There will need to be some laws put in place soon to stop this type of thing by companies, otherwise we're going pure Gattaca. The nearest term risk for AI making your life suck is basically encoding people's biases into models that are used to affect your life. This "research" is the equivalent of making an inference like "He's poor so he must be black, etc.."
Asdfbla 9y ago
Isn't this a result you should immediately be skeptical of because the results are so significant? I mean, the idea that you can detect sexual orientation from facial images seems somewhat plausible, but no way I would immediately buy that a classifier can discriminate just from the facial-structure with 81% accuracy as the title may imply, for instance. To be fair, in their abstract the authors mention grooming style and so on, but I feel that the title alone is slightly misleading. Also: man, that preprint formatting is annoying to read.
- plaidfuji 9y ago
  81% accuracy in binary classification isn't something to write home about, either...
  - speedplane 9y ago
    I'd bet you'd get a lot higher accuracy from scanning a facebook profile's metadata than from their image. That said, the concern of this is the potential to do this on at scale in a horrible distopia. Cameras in airports and on sidewalks automatically scan people for "undesirable" features and they end up in reeducation camps or just simply disappearing.
    - plaidfuji 9y ago
      Yeah, especially if you had the "Interested in..." field. Just wait until somebody publishes a "terrorist facial classifier" with "99% accuracy". That's when I'll be scared.
    - nthcolumn 9y ago
      Never mind reeducation camps what about people trying to sell you loafers...
  - deleted 9y ago
    [deleted]
speedplane 9y ago
A bit disheartened to see that this paper came out of Stanford. Of all the great applications of ML and computer vision, they have to pick a project that attracts clicks but has far more negative value than positive. How about identifying people that have clinical depression? Or for a skin disease? Or if they like to surf or play basketball? Or if they are good chess players? Even just hot dog or not hot dog. The study seems intentionally divisive. I get that a Stanford BSchool student would take it on to attract attention, but disappointed that Stanford would get behind it.
Brammarb 9y ago
The people working on this need to stop, sit down, and have a long, hard think about what they're doing. And about how ultimately this will be used to persecute and gill gay men.
- speedplane 9y ago
  I doubt it will get so extreme as to kill gay men. However, they do need to sit down, and have a long hard think about why they are in school at Stanford, and to what end they want to direct their studies. If it's to create click-bait research papers with little to no value, they should question why they are in school in the first place. Articles like this give the impression that the authors are internet hustlers, not proper grad students.
- jmngomes 9y ago
  Actually, their work seems to be focused on dispelling the common disbelief in these kind of applications for technology. From the abstract: "Additionally, given that companies and governments are increasingly using computer vision algorithms to detect people’s intimate traits, our findings expose a threat to the privacy and safety of gay men and women." Acknowledging that data analysis can create obstacles to freedom or serious social problems is a necessary step to preventing or addressing these issues, but public opinion is far from being there yet...
mgc092 9y ago
This is creepy. First, there is no serious scientific proof that your facial expression can tell if you are homosexual or not. Correlations here are total garbage. Sexuality is a really complex thing to discuss and, most importantly, is a private thing. Second, software detecting if someone is gay or not seems to me quite similar to jews being forced to wear the Star of David in Nazi Germany, so everyone could spot them and act against them, including people being wrongly labeled. Seriously guys, stop this. We have to be really careful about the potential uses our software. There can be serious impacts in society and in people lives.
- xfs 9y ago
  If the paper does what it says it's telling user reported labels. The problem isn't the presence of software producing likelihoods of someone being gay. The problem is people interpreting the results in a reductionist way and reducing people into labels. And another problem is you just don't like seeing what you don't like. There are companies out there profiting from the same stuff. They just don't talk about it in this way.
- thedonkeycometh 9y ago
  If we weren't all so uppity about what people did with eachother there wouldn't be issues. The paper seems to just say that Feminine looking men, and masculine looking women are probably gay. In the end, Biology and genetics will only ever be an indicator of patterns in a group, in this and other studies its not a certainty. So Donald and his little hands needn't worry about losing his demographic just yet.
- jamesrom 9y ago
  The ethical and moral implications are enormous. But machine learning and AI are not going away. We must make our best effort to understand how this works, not bury our head in the sand as hostile actors use this for ill.
  - mgc092 9y ago
    So what do you suggest, for example in this case?
throw2016 9y ago
Code is opinion and the idea that you can predict or identify things of this nature is just human prejudice masquerading as something more. Some human programmers got together and thought they could identify sexual orientation from faces, a silly bar room level idea in itself with no basis in reality and trained a neural net to express this prejudice. This is the same thing as a seer claiming to see the future.
Houshalter 9y ago
First of all if you didn't read the whole paper, this picture is really interesting and a good summary: https://i.imgur.com/zs8RWIz.png https://i.imgur.com/zs8RWIz.png As for the debate over whether this research is ethical, consider this. If someone actually uses this to discriminate against homosexuals, they must accept that the thing actually works. Which means that homosexuality is determined by biological features beyond anyone's control, which would contradict their own ideology. And that is the most interesting part of this work, not whether this tool is very accurate or not. This pretty solidly proves that physical features correlate well with sexual orientation, which is strong evidence for the biological theory of sexual orientation. Which has always been one of the biggest arguments for gay rights, that it's not a choice and can't be changed. On the usefulness of this test to actually classify gay people: They claim 91% accuracy on a balanced dataset. E.g. where there the ratio of gays to straights is 50:50. To get a ratio of correct:incorrect of 91:9 on such a dataset, their test must increase or decrease the odds a person is gay by 10. Now in the general population, the ratio of gays to straights is about 16 to 984 (1.6%). So if their test gives someone a positive reading, that increases the odds to 162 to 984, or 14%. So you can't use this test to accurately guess someone's sexual orientation. Simply because gay people are so rare that even a few percent of straight people misclassified will overwhelm the number of actual gay people. But still that's a lot more accurate than human guessing or the base rate, and it's scientifically interesting that this is even possible. It's a proof of concept that higher accuracy may be possible with better methods and more data. Another article claims this: >when asked to pick out the ten faces it was most confident about, nine of the chosen were in fact gay. If the goal is to pick a small number of people who are very likely to be gay out of a large group, the system appears able to do so. The test gives varying degrees of confidence, it gives much higher confidence to some people than others. There are some individuals that it can tell are definitely gay or straight. But for most it is more uncertain. Also note that the estimates for the percentage of gay people vary a lot. Which could make the true accuracy as high as 42%. Also some people believe sexuality is more of a spectrum than a binary straight/gay. If so the straight people it misclassifies might lean more on the gay/bisexual spectrum than normal and the errors wouldn't seem so unreasonable. Lastly all these "phrenology" references are silly. If you have methodological problem with this research I'd love to hear it. But I see people discarding the research simply because they don't like the conclusions. For this study and other facial correlations based research. This isn't new at all, there's tons of scientific research about digit ratios and all kinds of correlations they have with different things (https://en.wikipedia.org/wiki/Digit_ratio https://en.wikipedia.org/wiki/Digit_ratio). Why wouldn't we expect even better correlations from all facial features?
l0b0 9y ago
The potential for abuse is enormous, even if the results aren't 100% accurate (which they cannot ever be). And not just in the obvious ways. Will they be able to detect pedophilia with any accuracy? Good luck getting a job when employers covertly figure out your score is more than 50%. Good luck becoming a public figure when a large portion of the population take statistics at face value. Good luck having a life once the mob (aka. SJW) hears about it.
- mywittyname 9y ago
  This is my big fear. If people will shoot up pizza places over false accusations of pedophilia, any sort of algorithm that claims to predict it (regardless of efficacy), and people will be murdered. America has a bunch of angry people with twitchy trigger-fingers just begging for targets.
ourcat 9y ago
gAIdar?
realworldview 9y ago
THAT Michal Kosinski... I wonder where the seed for this idea started, such that it germinated so far. Are people still doing this in the second decade of the 21st century?
l0b0 9y ago
In "Serving the Reich"+, Philip Ball talks about how "pure" science lost its innocence when nukes were first deployed, and argues that researchers cannot absolve themselves by claiming that they didn't know how their research would be used. The same applies here: nothing good can come from this. + Hi trolls, yes, I'm aware of Godwin's Law. I also didn't call anybody a Nazi.
criloz2 9y ago
just imagine, you can spot people sexual orientation with your google glasses, and god knows how many other features, not only from the people face, also from him speech pattern, and eye movement. seriously!!!, the first time that machine learning start to worry me, my mistake. I never considered how vicious the human can be. if those types of studies continue the future will be a nightmare.
deleted 9y ago
[deleted]

97 points by fotcorn 9y ago

jonahx 9y ago

In cultures where homosexuals are persecuted, a tool like this could be devastating.

purrcat259 9y ago
Both for the true positives and the false positives.
hnarayanan 9y ago
They mention this in the article. Even so, it is disheartening. It appears we're so fascinated by all the avenues tools like deep learning could open up, to ask more basic questions like the implications of pursuing such research.
- kdelok 9y ago
  The first author of this also developed tools that allow you to analyse all sorts of personality traits (including sexual orientation) via FB likes. He was aware and publicised of the potential consequences and risks here, but I seem to remember thinking his reasons for publishing were justified at the time. As he was part of the psychology department during his PhD, I think his papers will have been extensively scrutinised by ethics boards and the like. Mini disclaimer: I vaguely knew him from university and didn't get on with him. However, I don't think he was the type of person to put people at risk for personal glory.
kwhitefoot 9y ago
Or anything else that someone might label deviant.
Chris2048 9y ago
If that were possible, humans could already detect homosexuals. It's likely in those countries that homosexuals do not present themselves the same.

JulianMorrison 9y ago

This is very likely the same as detecting women by "they have long hair" - it's not finding intrinsic characteristics, but social ones.

nthcolumn 9y ago
And in the context of privacy with regard to public surveillance systems with facial recognition capability your point is exactly what?
- JulianMorrison 9y ago
  Social characteristics are voluntary. People who are hiding won't adopt them.
  - speedplane 9y ago
    Homosexual men will have to start wearing ill-fitting suits and dirty gym shorts to avoid being caught by the algorithm.
    - nthcolumn 9y ago
      Totally agree! Heterosexual women will have to wear makeup and grow their hair long to avoid being misidentified as lesbians. Personally I think it is wishful thinking on the part of people who think that you aren't born gay. This algorithm will out even the most repressed homosexual as it is not entirely based on 'social characteristics'. Science doesn't care what you think.
      - speedplane 9y ago
        Gay men and women have blended in undetected for thousands of years all over the world. They've only been able to really come out in the last few decades. If this miserable distopia of automatic homosexual detection came to pass, they would simply go back to blending in again. A miserable existence, but pretty sure they'd be able to fool the robot overlords.

quantum_state 9y ago

Is this serious?

dx034 9y ago
Research around this existed for years, it could be shown that gays can more easily detect the sexual orientation of others. One explanation could be that since homosexuality was/is banned in many cultures, this helped to survive. A reason why we didn't read much about it is that most researchers know that you shouldn't publish about this. Otherwise you can quickly end up with blood on your hands.
- quantum_state 9y ago
  AI DL ... etc. are just tools ... if one is not careful, one would run the risk of GIGO == garbage in, garbage out ...

dx034 9y ago

I would really hope that they don't publish more information and keep algorithms as secret as possible. Algorithms like this can have huge implications in countries where homosexuality is banned. If this really works, people could end up in jail just because a neural network determines that they look gay. And even if it doesn't work, some governments or local communities could still use it to ban people because of the chance that they're gay. While I'm not a fan of censoring research, I think this is a case where the research community should refrain from publishing results that could help replicating such algorithms. EDIT: Just to be clear, I don't say that this study makes sense or works. But there are areas where large parts of the population really hate homosexuality. Just thinking this could work can make them use such a system. Especially in situations where you can afford false positives. So next time you want to travel to a country it could happen that they don't let you in because the algorithm says that you're likely gay.

rem1313 9y ago
It wouldn't take long for a government to replicate the study even if it is not published if it is sufficiently interested
- speedplane 9y ago
  Getting a prototype working would take a week for a skilled machine learning engineer, assuming they had access to well labeled tagged data. What will be difficult is to push the accuracy up to get actionable information from it. Assuming you're an evil repressive regime, even if you're system had 90% accuracy, you'd be falsely accusing a huge number of people and doubt even a repressive homophobic regime would implement it. Getting that 90% accuracy to 99.9% or higher would take a huge effort, this study isn't anywhere close. That said, the concerns are real. Automatically sorting hetero from homosexual people, Jews from Christians, or even black from white comes with a tone of moral issues.
  - peteretep 9y ago
    Getting buy-in from an evil repressive regime will be hard if we assume they have a population distribution like anyone else which means a sizeable proportion of those in power are gay but not openly.

pulse7 9y ago

With Facebook profile photos you could detect sexual orientation with 81% probability for man and 74% for women... Hmmm... And with 91% / 83% probability when using 5 photos/person in the learning process...

BeniBoy 9y ago
Well, I am not sure this is the case.. The metric seems to be the ability to tell apart pairs of straight and gay people. >Among men, the classification accuracy equaled AUC = .81 when provided with one image per person. This means that in 81% of randomly selected pairs—composed of one gay and one heterosexual man—gay men were correctly ranked as more likely to be gay. Pretty different from predicting one's sexual orientation.

vectorEQ 9y ago

'breaking news: neural network speeds up personal intepretations of scientist a bit'

davidgerard 9y ago
yep. This is phrenology. Bias laundering at best.
- rbanffy 9y ago
  I was just imagining sourcing mugshot sites and then tracking those who were tried and considered guilty of whatever they got the mugshot for to train a good/bad person model. It's fun, but I'd never dare to make such a dumb thing public.
  - halflings 9y ago
    This article [0] discusses a paper by Chinese researchers [1] that did just that. (and yes, it is quite dumb/dangerous) [0] https://medium.com/@blaisea/physiognomys-new-clothes-f2d4b59fdd6a https://medium.com/@blaisea/physiognomys-new-clothes-f2d4b59... [1] https://arxiv.org/abs/1611.04135 https://arxiv.org/abs/1611.04135
  - peteretep 9y ago
    Pretty sure there's conclusive evidence of an existing effect whereby how attractive you are plays into if you're found guilty
tzs 9y ago
Which personal interpretations?

jaymzcampbell 9y ago

Page 22 of the report has an image of average faces for men and women and gay / straight (bisexuality doesn't seem to be covered). > The results show that the faces of gay men were more feminine and the faces of lesbians were more masculine than those of their respective heterosexual counterpart It looks to me you're gay if you are a man wearing glasses and straight if you have a beard and rounder face. It would be interesting to see what it made of the stereotypical "bear". If you're a brunette and have a slightly thinner face then you're probably, 58%, a lesbian ¯\_(ツ)_/¯. > lesbians tended to wear baseball caps I'm really not sure what to make of this.

dm319 9y ago
Yes, that page caught my eye, too. My conclusion is that gay people are better-looking.
- justinjlynn 9y ago
  Well, don't forget self-selection effects in the population sample. It's highly possible those who are better looking are more confident about posting their profiles openly for analysis. The confounders for studies such as these are multiple and treacherous.

deleted 9y ago

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peteretep 9y ago

I can't seem to load the page, but I'd imagine this is "declared sexual orientation" rather than actual, which makes this a poor tool for discrimination

tzs 9y ago
The images were from a dating site. They inferred sexual orientation from the gender of the partners that the subjects were looking for according to their profiles.
- foldr 9y ago
  That is declared sexual orientation.

4c2383f5c88e911 9y ago

The title is surprisingly not too clickbaity, although it's not as clear-cut: in order to reduce false positives (increase precision), they need to limit the number of positives (reduce recall). On 1000 samples containing 70 gay people, they were able to get a 10% false positive rate on their positive results, which were 10 people (meaning 12% of the total gay sample). The sample is a bit biased too because they pulled it from explicitly gay-oriented public social network pages (but I don't fault them for that, it would be quite hard to find a better sample). It is still an impressive result, and one that might be misused badly, despite the numerous warnings used in the paper.

BeniBoy 9y ago
This is the classic "false positive paradox"[1]. Commonly present in medical testing. Even if false positive rate is very low, if the positive value is very low too, the likelihood of being victim of false can be high. The exemple in the wikipedia page are very good. I had to explain that to a friend which had tested positive on the HIV test and was waiting for confirmation over the weekend. Not easy to talk math when such things happens.. I found it very troubling that doctors don't even mention that to patient and present tests as 95% effective. (In fact she was fine) [1]: https://en.wikipedia.org/wiki/False_positive_paradox https://en.wikipedia.org/wiki/False_positive_paradox

bjoernbu 9y ago

Serious question by someone who's not into this kind of stats too much: What would happen if you took a big set of Facebook profiles and train some (the same if you wanna) CNN to classify picture->f for each f in profile features. Sure, for some features, your model should be able to deliver decent precision. Does this mean that you quickly found out what features can be predicted from pictures & how well your CNN performs on that? Or is it possible that you just train models from picture->X where X is basically meaningless but significantly correlated with some feature because of the effect portrait in xkcd's "Significant" (Scientists investigate!) [1] [1]: https://xkcd.com/882/ https://xkcd.com/882/

dm319 9y ago
There is a tendency for machine learning (including neural networks) to over-fit data - i.e. the algorithm learns to recognise the particular data, rather than the real distinguishing predictors of the groups. As you say, these can be features that are by chance associated with what you are trying to discriminate. This is why the model is validated on a separate testing group from the training group which created it. There are lots of ways to do this, and the more sophisticated continually iterate training and testing to improve the model.

chrisloy 9y ago

If you read the paper, the photos and labels were sourced from a dating website. In my opinion, there is a good chance that the model may be overfitting to how people wish to present themselves in that context. - e.g. framing of the photo, facial expression etc. Things with a heavy amount of cultural conditioning. Some of the press around this seems a bit alarmist - I doubt you would see anywhere near this accuracy out in the real world.

moxious 9y ago
This is a very annoying part of non-explanatory models. I think the result defies common sense a bit, and the model can't explain why this is so. So in the circumstance, why should we believe it's generalizable?
- speedplane 9y ago
  This also goes to the heart of the problem with deep learning on neural nets. We have this algorithm that apparently identifies homo and heterosexual people, presumably based on a variety of subtle features, but we have pretty much no clue as to which features and why. The human judges may have been less accurate, but they could likely explain each decision they made and the visual features they based their decision on.
  - PeterSmit 9y ago
    Humans are known to be unreliable in explaining how they come to conclusions as well. Humans just like to pretend they can verbalise all knowledge ;)
    - speedplane 9y ago
      Some are better at it than others, and no doubt many are pretty bad at it, but I have yet to see a neural net explain to me, accurately or not, why it came to the decision it did.
    - the_d00d 9y ago

ghgr 9y ago

Line 210: > "Gay and heterosexual people were represented in equal numbers." According to Gallup Polling, around 4% of the American population is homosexual. So, let's be generous and say that their classifier has a 80% accuracy given balanced inputs while humans have 60%. Let's sample 100 people of the dataset, 50 of which are homosexual (and the rest heterosexual). If the classifier has 80% accuracy (let's assume false_positive_rate = false_negative_balance since I didn't find information about that), it means that 80 people were correctly classified, 10 heterosexuals were misclassified as homosexuals and 10 homosexuals as heterosexuals. According to the Bayes theorem, "Given a random person, probability of being homosexual assuming that the classifier said so" = "Probability of being homosexual of a random person (the so-called prior)" <times> "Probability of classifier saying it's homosexual, assuming it's indeed homosexual <divided by> "Given a random person, probability of saying it's homosexual". Substituting, we get: P = 0.04 * 0.8 / 0.5 = 0.064 If instead of the classifier we use human "feeling", we get: P=0.04 * 0.6 / 0.5 = 0.048 In summary, 4% of the people are homosexual. If a human thinks someone is homosexual, the probability "increases" to 4.8%. If on the contrary their algorithm believes it's homosexual, the probability of actually being homosexual increases to 6.4%. Not very groundbreaking...

dangom 9y ago
I don't know if I can follow. You say that if their classifier had 100% accuracy, then the probability of being homosexual would increase to 8%? That doesn't make much sense.
- BeniBoy 9y ago
  Look here [1] Very good explanation (though the op's one are too) [1]: https://en.wikipedia.org/wiki/False_positive_paradox https://en.wikipedia.org/wiki/False_positive_paradox
  - dangom 9y ago
    But why does "Given a random person, probability of saying it's homosexual" equal 0.5? I'd say that should be 0.04.

speedplane 9y ago

I'm a bit skeptical of their claim that "Human judges achieved much lower accuracy". The deep net likely found a some feature that correlated with sexuality presumably after looking at many tagged images. I wonder whether the "judges" had access to that same training set. If they were people off the street that brought in their own biases, I would suspect they would do far worse than if they were able to view the training set themselves and teach themselves what hetero and homosexual people look like. Many heterosexual people have very limited contact with homosexual people. But given a training set (or sufficient contact with homosexual people), they could learn too, and I suspect they could learn better than the machine learning algorithm. That said, this research is impressive, as well as terrifying and depressing.

dm319 9y ago
>That said, this research is impressive, as well as terrifying and depressing. I know why you say this, but it's only terrifying because there are so many idiots around. All this study says is that there is a strong genetic/developmental component to sexuality - something we've known about for decades. Telling people they can't be gay is like telling people they can't be tall, or black.
- speedplane 9y ago
  I am less concerned that there may be visibly identifiable characteristics of homosexuality. I am more concerned that these features can be automatically discerned by computers on a massive scale. It's one thing for an individual homophobe to call out someone because they look gay. It's quite another for a government or organization to do that to millions of people in seconds, and then act upon it at scale.
  - xfs 9y ago
    Do you think the authors of the paper are the first to think of this idea of detecting personal traits with facial features? How much cost do you think it takes for implementing detection with this particular set of features? If it can be done it will be done. In this particular case it doesn't even cost much. You should have started to be concerned when machine learning evolved into a paradigm that is only possible to achieve any kind of real world performance with an amount of data and computing resources available only to megacorps and governments.

vfulco 9y ago

But sexual orientation is fluid according to the fringe psycho-babble pseudo science crowd.

traitormonkey 9y ago
Check all the people worried that 'gay' may not be a 'lifestyle choice'.

TeeWEE 9y ago

If you know would show a list of 100 heterosexual man to this neural machine, it would probably qualify a high percentage as gay. Since it was learning on a 50/50 dataset.

halflings 9y ago

Necessary link to an article by Blaise Aguera y Arcas, the lead of an ML team at Google: "Physiognomy’s New Clothes" [0] He gives several examples, and explains how these type of statements are extremely misleading (and dangerous!), and not something new at all... https://medium.com/@blaisea/physiognomys-new-clothes-f2d4b59fdd6a https://medium.com/@blaisea/physiognomys-new-clothes-f2d4b59...

grizzles 9y ago

Total garbage. There will need to be some laws put in place soon to stop this type of thing by companies, otherwise we're going pure Gattaca. The nearest term risk for AI making your life suck is basically encoding people's biases into models that are used to affect your life. This "research" is the equivalent of making an inference like "He's poor so he must be black, etc.."

Asdfbla 9y ago

Isn't this a result you should immediately be skeptical of because the results are so significant? I mean, the idea that you can detect sexual orientation from facial images seems somewhat plausible, but no way I would immediately buy that a classifier can discriminate just from the facial-structure with 81% accuracy as the title may imply, for instance. To be fair, in their abstract the authors mention grooming style and so on, but I feel that the title alone is slightly misleading. Also: man, that preprint formatting is annoying to read.

plaidfuji 9y ago
81% accuracy in binary classification isn't something to write home about, either...
- speedplane 9y ago
  I'd bet you'd get a lot higher accuracy from scanning a facebook profile's metadata than from their image. That said, the concern of this is the potential to do this on at scale in a horrible distopia. Cameras in airports and on sidewalks automatically scan people for "undesirable" features and they end up in reeducation camps or just simply disappearing.
  - plaidfuji 9y ago
    Yeah, especially if you had the "Interested in..." field. Just wait until somebody publishes a "terrorist facial classifier" with "99% accuracy". That's when I'll be scared.
  - nthcolumn 9y ago
    Never mind reeducation camps what about people trying to sell you loafers...
- deleted 9y ago
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speedplane 9y ago

A bit disheartened to see that this paper came out of Stanford. Of all the great applications of ML and computer vision, they have to pick a project that attracts clicks but has far more negative value than positive. How about identifying people that have clinical depression? Or for a skin disease? Or if they like to surf or play basketball? Or if they are good chess players? Even just hot dog or not hot dog. The study seems intentionally divisive. I get that a Stanford BSchool student would take it on to attract attention, but disappointed that Stanford would get behind it.

Brammarb 9y ago

The people working on this need to stop, sit down, and have a long, hard think about what they're doing. And about how ultimately this will be used to persecute and gill gay men.

speedplane 9y ago
I doubt it will get so extreme as to kill gay men. However, they do need to sit down, and have a long hard think about why they are in school at Stanford, and to what end they want to direct their studies. If it's to create click-bait research papers with little to no value, they should question why they are in school in the first place. Articles like this give the impression that the authors are internet hustlers, not proper grad students.
jmngomes 9y ago
Actually, their work seems to be focused on dispelling the common disbelief in these kind of applications for technology. From the abstract: "Additionally, given that companies and governments are increasingly using computer vision algorithms to detect people’s intimate traits, our findings expose a threat to the privacy and safety of gay men and women." Acknowledging that data analysis can create obstacles to freedom or serious social problems is a necessary step to preventing or addressing these issues, but public opinion is far from being there yet...

mgc092 9y ago

This is creepy. First, there is no serious scientific proof that your facial expression can tell if you are homosexual or not. Correlations here are total garbage. Sexuality is a really complex thing to discuss and, most importantly, is a private thing. Second, software detecting if someone is gay or not seems to me quite similar to jews being forced to wear the Star of David in Nazi Germany, so everyone could spot them and act against them, including people being wrongly labeled. Seriously guys, stop this. We have to be really careful about the potential uses our software. There can be serious impacts in society and in people lives.

xfs 9y ago
If the paper does what it says it's telling user reported labels. The problem isn't the presence of software producing likelihoods of someone being gay. The problem is people interpreting the results in a reductionist way and reducing people into labels. And another problem is you just don't like seeing what you don't like. There are companies out there profiting from the same stuff. They just don't talk about it in this way.
thedonkeycometh 9y ago
If we weren't all so uppity about what people did with eachother there wouldn't be issues. The paper seems to just say that Feminine looking men, and masculine looking women are probably gay. In the end, Biology and genetics will only ever be an indicator of patterns in a group, in this and other studies its not a certainty. So Donald and his little hands needn't worry about losing his demographic just yet.
jamesrom 9y ago
The ethical and moral implications are enormous. But machine learning and AI are not going away. We must make our best effort to understand how this works, not bury our head in the sand as hostile actors use this for ill.
- mgc092 9y ago
  So what do you suggest, for example in this case?

throw2016 9y ago

Code is opinion and the idea that you can predict or identify things of this nature is just human prejudice masquerading as something more. Some human programmers got together and thought they could identify sexual orientation from faces, a silly bar room level idea in itself with no basis in reality and trained a neural net to express this prejudice. This is the same thing as a seer claiming to see the future.

Houshalter 9y ago

First of all if you didn't read the whole paper, this picture is really interesting and a good summary: https://i.imgur.com/zs8RWIz.png https://i.imgur.com/zs8RWIz.png As for the debate over whether this research is ethical, consider this. If someone actually uses this to discriminate against homosexuals, they must accept that the thing actually works. Which means that homosexuality is determined by biological features beyond anyone's control, which would contradict their own ideology. And that is the most interesting part of this work, not whether this tool is very accurate or not. This pretty solidly proves that physical features correlate well with sexual orientation, which is strong evidence for the biological theory of sexual orientation. Which has always been one of the biggest arguments for gay rights, that it's not a choice and can't be changed. On the usefulness of this test to actually classify gay people: They claim 91% accuracy on a balanced dataset. E.g. where there the ratio of gays to straights is 50:50. To get a ratio of correct:incorrect of 91:9 on such a dataset, their test must increase or decrease the odds a person is gay by 10. Now in the general population, the ratio of gays to straights is about 16 to 984 (1.6%). So if their test gives someone a positive reading, that increases the odds to 162 to 984, or 14%. So you can't use this test to accurately guess someone's sexual orientation. Simply because gay people are so rare that even a few percent of straight people misclassified will overwhelm the number of actual gay people. But still that's a lot more accurate than human guessing or the base rate, and it's scientifically interesting that this is even possible. It's a proof of concept that higher accuracy may be possible with better methods and more data. Another article claims this: >when asked to pick out the ten faces it was most confident about, nine of the chosen were in fact gay. If the goal is to pick a small number of people who are very likely to be gay out of a large group, the system appears able to do so. The test gives varying degrees of confidence, it gives much higher confidence to some people than others. There are some individuals that it can tell are definitely gay or straight. But for most it is more uncertain. Also note that the estimates for the percentage of gay people vary a lot. Which could make the true accuracy as high as 42%. Also some people believe sexuality is more of a spectrum than a binary straight/gay. If so the straight people it misclassifies might lean more on the gay/bisexual spectrum than normal and the errors wouldn't seem so unreasonable. Lastly all these "phrenology" references are silly. If you have methodological problem with this research I'd love to hear it. But I see people discarding the research simply because they don't like the conclusions. For this study and other facial correlations based research. This isn't new at all, there's tons of scientific research about digit ratios and all kinds of correlations they have with different things (https://en.wikipedia.org/wiki/Digit_ratio https://en.wikipedia.org/wiki/Digit_ratio). Why wouldn't we expect even better correlations from all facial features?

l0b0 9y ago

The potential for abuse is enormous, even if the results aren't 100% accurate (which they cannot ever be). And not just in the obvious ways. Will they be able to detect pedophilia with any accuracy? Good luck getting a job when employers covertly figure out your score is more than 50%. Good luck becoming a public figure when a large portion of the population take statistics at face value. Good luck having a life once the mob (aka. SJW) hears about it.

mywittyname 9y ago
This is my big fear. If people will shoot up pizza places over false accusations of pedophilia, any sort of algorithm that claims to predict it (regardless of efficacy), and people will be murdered. America has a bunch of angry people with twitchy trigger-fingers just begging for targets.

ourcat 9y ago

gAIdar?

realworldview 9y ago

THAT Michal Kosinski... I wonder where the seed for this idea started, such that it germinated so far. Are people still doing this in the second decade of the 21st century?

l0b0 9y ago

In "Serving the Reich"+, Philip Ball talks about how "pure" science lost its innocence when nukes were first deployed, and argues that researchers cannot absolve themselves by claiming that they didn't know how their research would be used. The same applies here: nothing good can come from this. + Hi trolls, yes, I'm aware of Godwin's Law. I also didn't call anybody a Nazi.

criloz2 9y ago

just imagine, you can spot people sexual orientation with your google glasses, and god knows how many other features, not only from the people face, also from him speech pattern, and eye movement. seriously!!!, the first time that machine learning start to worry me, my mistake. I never considered how vicious the human can be. if those types of studies continue the future will be a nightmare.

deleted 9y ago

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