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Gay-identifying AI tells us more about stereotypes than the origins of sexuality

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In a forthcoming paper, two Stanford researchers used a deep neural network to detect sexuality from profile pictures on a US dating website.

The internet was aghast. The authors themselves raised the spectre of Orwellian surveillance.

Read More: Why Google wants to think more like you and less like a machine

More problematic, however, was their claim that the results provide support for a controversial theory that broadly suggests gay people appear and act atypical for their gender.

This conclusion threatens to undermine science with stereotype.

Deep neural networks are extremely powerful tools. They are especially good at tasks like classifying pictures, as they can combine innumerable subtle cues that are difficult for humans to register.

The study itself avoids obvious pitfalls in its method, though it was explicitly limited in scope given the researchers used only clear pictures of Caucasians.

Their classifier was given two faces, one gay and one straight, and asked to say which was which. This is a much easier task than classifying a single face (imagine if baggage handlers only ever had to pick which of two x-rays definitely had a gun).

Much of the immediate criticism focused on these limitations. Yet easier tasks are often enough for proof of concept, and the network achieved a good level of accuracy.

Interpreting that accuracy is where things get dicey.

The authors suggest the results affirm the “prenatal hormone theory” of sexuality, which claims that atypical hormone exposure in the womb gives gay men more “feminine” brains (similarly for lesbians and masculinity).

As they put it:

Our results provide strong support for the [Prenatal Hormone Theory], which........

© The Conversation