AI that can determine a person’s sexuality from photos shows the dark side of the data age

tech crunch

We count on machine learning systems for everything from creating playlists to driving cars, but like any tool, they can be bent toward dangerous and unethical purposes, as well. Today’s illustration of this fact is a new paper from Stanford researchers, who have created a machine learning system that they claim can tell from a few pictures whether a person is gay or straight.

The research is as surprising as it is disconcerting. In addition to exposing an already vulnerable population to a new form of systematized abuse, it strikes directly at the egalitarian notion that we can’t (and shouldn’t) judge a person by their appearance, nor guess at something as private as sexual orientation from something as simple as a snapshot or two. But the accuracy of the system reported in the paper seems to leave no room for mistake: this is not only possible, it has been achieved.

It relies on cues apparently more subtle than most can perceive — cues many would suggest do not exist. And it demonstrates, as it is intended to, a class of threat to privacy that is entirely unique to the imminent era of ubiquitous computer vision.
Before discussing the system itself, it should be made clear that this research was by all indications done with good intentions. In an extensive set of authors’ notes that anyone commenting on the topic ought to read, Michal Kosinski and Yilun Wang address a variety of objections and questions. Most relevant are perhaps their remarks as to why the paper was released at all:

We were really disturbed by these results and spent much time considering whether they should be made public at all. We did not want to enable the very risks that we are warning against. The ability to control when and to whom to reveal one’s sexual orientation is crucial not only for one’s well-being, but also for one’s safety.

We felt that there is an urgent need to make policymakers and LGBTQ communities aware of the risks that they are facing. We did not create a privacy-invading tool, but rather showed that basic and widely used methods pose serious privacy threats.

Certainly this is only one of many systematized attempts to derive secret information such as sexuality, emotional state or medical conditions. But it is a particularly concerning one, for several reasons.

Seeing what we can’t (or won’t)

The paper, due to be published in the Journal of Personality and Social Psychology, details a rather ordinary supervised-learning approach to addressing the possibility of identifying people as gay or straight from their faces alone. (Note: the paper is still in draft form.)

Using a database of facial imagery (from a dating site that makes its data public), the researchers collected 35,326 images of 14,776 people, with (self-identified) gay and straight men and women all equally represented. Their facial features were extracted and quantified: everything from nose and eyebrow shape to facial hair and expression.

A deep learning network crunched through all these features, finding which tended to be associated with individuals of a given sexual orientation. The researchers didn’t “seed” this with any preconceived notions of how gay or straight people look; the system merely correlated certain features with sexuality and identified patterns.