this post was submitted on 27 Feb 2024
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I'd be very interested in those results too, though I'd want everyone to bear in mind the possibility that the brain could have many different "masculine" and "feminine" attributes that could be present in all sorts of mixtures when you range afield from whatever statistical clusterings there might be. I wouldn't want to see a situation where a transgender person is denied care because an AI "read" them as cisgender.
In another comment in this thread I mentioned how men and women have different average heights, that would be a good analogy. There are short men and tall women, so you shouldn't rely on just that.
I have a suspicion that this is exactly what’s going on here and may be why past studies found no differences. AI is much better at quickly synthesizing complex patterns into coherent categories than humans are.
Also, 90% is not that good all things considered. The brain is almost certainly a complex mix of features that defy black and white categorization.
Hopefully we will be wise enough to not require trans people to prove their trans-ness scientifically. People have a right to do what they wish with their bodies and express their gender in a way that feels right to them, and should not be required to match some artificial physical diagnosis of what it means to be trans. Even if it turns out that most trans people do share certain brain structures or patterns. There will always be exceptions and that doesn’t mean we get to label someone’s identity as inauthentic.
Unlikely as it might be, maybe the 10% error rate is from gender queer people that haven't realized/faced it yet.
There are a lot of potential explanations. In essence they built a model to categorize brain features into male and female, and then tested this against their label of male or female on each brain. So this could result from problems with the model predictions—or just as easily from their “correct” labeling of each brain as male or female.
So a big question is how did they define male and female? By genetics? By reproductive anatomy? By self reported identity? This information was not in the article. All of these things are very likely correlated with things happening in the brain, but probably not perfectly. It’s worth noting that many definitions of sex do not consider gender identity at all—if such a definition was used, then a trans-man might be labeled female in their data, whether they have reckoned with their identity or not.
I looked into this, the study analyzed three pre-existing fMRI datasets.
I wasn't able to find any info on how these projects assessed sex/gender of participants.
Based on this, I’d assume they just used AGAB as that’s how medical professionals approach patients in their care.
Given any finite data set above a trivially small size/complexity, and an undefined set of criteria, the odds of meaningless patterns appearing are extremely high.
Machine learning algorithms are basically automated P-hackers when misused. Be skeptical of any conclusions drawn from ML that are not otherwise verifiable.
Someone else mentioned the iris test being more accurate but that it also includes the eye area around the iris, including eyelashes and eye shape. That would clearly bias the model.
I wonder if there’s anything else that’s might be giving clues to the machine or if it I limited to what they say it’s determining sex based on. As a trans-nonbinary person myself, I’m very skeptical and anxious about technologies like this leading to biases and prejudices being emboldened.
Just a guess but if they labeled training data either male or female then i believe its more likely that it detects biological sex..
But if i they would also label and train on lgbt brains then i bet machine learning can differentiate between all of those.
I bet you can do the same thing with neurodivergent people but you would need to make sure the training data is without error to make me trust it.
I don't think that's a fair comparison. Height is a single value. If you trained an AI on that, it would be guessing. A brain has many, many more parameters to take into consideration when going into an artificial neural network.
That just makes my point stronger, though. The basic gist of what I was saying is that even if there is a statistical clustering of data into two groups that seem correlated with some category, that doesn't mean that you can absolutely rely on that data to classify people into those categories.
The more data you have, the more confident you can be that the resulting categorisation is correct. If you're saying this is incorrect, I disagree with you. If you're saying absolute confidence that the categories themselves are correct is impossible, then I agree with you