What facial recognition and the racist pseudoscience of phrenology have in common

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“Phrenology” has an old-fashioned sound. Looks like it belongs in a history book, classified somewhere between bloodletting and velocipeds. We would like to think that judging the worth of people based on the size and shape of their skulls is a practice that is well behind us. However, phrenology is once again raising its lumpy head.

In recent years, machine learning algorithms have promised governments and private companies the power to glean all kinds of information about how people look. Several startups now claim they can use artificial intelligence (AI) to help employers detect the personality traits of job applicants based on their facial expressions. In China, the government pioneered the use of surveillance cameras that identify and track ethnic minorities. Meanwhile, there have been reports of schools installing camera systems that automatically sanction children for not paying attention, based on facial movements and micro-expressions such as twitching eyebrows.

Perhaps more notoriously, a few years ago AI researchers Xiaolin Wu and Xi Zhang claimed to have trained an algorithm to identify criminals based on their face shape, with 89.5% accuracy. They did not go so far as to endorse some of the ideas about physiognomy and character that circulated in the 19th century, most notably from the work of Italian criminologist Cesare Lombroso: that criminals are under-resolved, recognizable sub-human beasts. to their sloping foreheads and hawk-shaped noses. However, the recent study’s seemingly high-tech attempt to identify facial features associated with crime borrows directly from the “ photographic composite method ” developed by Victorian jack-of-all-trades Francis Galton – which involved layering the faces of several people in a certain category to find the characteristics indicating qualities such as health, disease, beauty and criminality.

Facial recognition and phrenology

Tech commentators have called these facial recognition technologies “literal phrenology”; they also linked it to eugenics, the pseudoscience of improving the human race by encouraging those deemed most apt to reproduce. (Galton himself coined the term “ eugenics ”, describing it in 1883 as “ all influences which tend to such a distant degree as to give the most suitable races or blood strains a better chance quickly outweigh the less appropriate than they otherwise would have had. ‘)

In some cases, the explicit purpose of these technologies is to deny opportunities to those deemed unfit; in others it may not be the goal, but it is a predictable outcome. Yet when we dismiss algorithms as phrenology, what exactly is the problem we are trying to highlight? Are we saying these methods are scientifically flawed and don’t really work – or are we saying it’s morally wrong to use them anyway?

There is a long and tangled story of how “phrenology” has been used as a fierce insult. The company’s philosophical and scientific critiques have always been closely linked, although their entanglement has changed over time. In the 19th century, critics of phrenology objected to the fact that phrenology attempted to locate different mental functions in different parts of the brain – a movement that was considered heretical, as it challenged Christian ideas about it. unity of soul. . Interestingly, however, trying to figure out a person’s character and intellect based on the size and shape of their head was not seen as a serious moral issue. Today, on the other hand, the idea of ​​localizing mental functions is relatively uncontroversial. Scientists might no longer think of destructiveness to be located above the right ear, but the idea that cognitive functions can be located in particular brain circuits is a standard assumption in mainstream neuroscience.

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Phrenology also had its share of empirical criticism in the 19th century. Debates raged over which functions resided and where and whether skull measurements were a reliable way to determine what is going on in the brain. The most influential empirical critique of ancient phrenology, however, came from studies by French physician Jean Pierre Flourens based on brain damage in rabbits and pigeons – which he concluded that mental functions are distributed rather than localized. . (These results were later discredited.) The fact that phrenology was rejected for reasons that most contemporary observers would no longer accept makes it more difficult to understand what we are aiming for when we use “ phrenology ” as an insult. today.

Statistical biases

Both “old” and “new” phrenology have been criticized for its sloppy methods. In the recent AI Crime Study, the data came from two very different sources: photos of convicts, as opposed to photos of working websites for non-convicts. This fact alone could explain the algorithm’s ability to detect a difference between groups. In a new preface to the article, the researchers also admitted that viewing court convictions as synonymous with criminality was a “ grave oversight. ” Yet equating convictions with criminality seems to register with the authors primarily as an empirical flaw: the use of snapshots of convicted felons, but not of those who escaped, introduces a statistical bias. They said they were “ deeply baffled ” by the public outrage in reaction to an article that was intended “ for pure academic discussion. ”