Darwinists Still Attempting to Prove Criminality is Genetic
The idea that criminality represents a throwback to our pre-modern stage of human evolution, (i.e., to the innate aggressiveness of our survival-of-the-fittest animal ancestry) was disproved over a century ago. Shamefully, it’s back.
by Jerry Bergman, PhD.
Phrenology, the art of divining personality by evaluating bumps on the human head, is one of the many past blunders of evolutionism. In the late 19th century, it was considered a scientific measurement of evolution. A practitioner of phrenology essentially evaluated bumps (called peaks) on the skull surface. These were interpreted as brain areas that are more evolutionarily developed. Indentations (called valleys), by contrast, were considered to be brain areas that were evolutionarily less developed. Phrenology influenced Charles Darwin himself as well as the co-founder of natural selection, Alfred Russel Wallace.
The enormously popular phrenology movement diverted attention from other causes of behavior. It also fostered racism, and indirectly supported the rise of materialism, naturalism and Darwinism. Phrenology has been rejected by solid scientific evidence. It is not only baseless, but irresponsible, because it misled many people to rely on its erroneous conclusions instead of solid evidence-supported science. It was firmly empirically discredited in 2018 by the largest, most carefully designed study ever completed on the phrenology claims. The study used MRI neuroimaging on 5,724 subjects, producing 40,962 vertex measures per subject which were compared with a set of lifestyle measures obtained from the same subjects. In short, no evidence was found that phrenology could successfully determine the traits it claimed it could predict. It failed the test miserably.
Although documented as of little use to read personality, remnants of the idea are still swimming around in the larger world of ideas. Catherine Stinson writes that phrenology
sounds like it belongs in a history book, filed somewhere between bloodletting and velocipedes. We’d like to think that judging people’s worth based on the size and shape of their skulls is a practice that’s well behind us. However, phrenology is once again rearing its lumpy head, this time under the guise of technology.
Staring at Criminality in the Face
The worst employment of phrenology was an attempt to prove that a person’s genetic constitution could predict whether they had criminal tendencies. This idea was developed by Cesare Lombroso (1835-1909) and others. Lombroso believed that criminality was a heritable genetic trait, and that criminals could be identified by physical traits. According to this discredited view, certain physical traits identified individuals as being atavistic, meaning throwbacks to less-evolved states of humanity. A thief could be identified, for instance, by traits such as an apelike face or body and small, wandering eyes. Lombroso’s ideas of atavistic crime culminated in the “increasing popularity of eugenics and the use of biological theories of crime by the Nazis to justify the murder of millions of people.” Lombroso and his followers concluded that
incorrigible offenders are “born criminals,” apelike throwbacks to a more primitive evolutionary stage. Born criminals differ so radically from lawful people that scientists can identify them by their physical and mental abnormalities, just as physical anthropologists can identify members of different races by their physical characteristics.
The Late Harvard University professor Stephen Jay Gould connected Lombroso’s theory of atavisms to Charles Darwin. The idea was not just a
vague proclamation that crime is hereditary … but a specific evolutionary theory based upon anthropometric data. Criminals are evolutionary throwbacks in our midst. Germs of an ancestral past lie dormant in our heredity. In some unfortunate individuals, the past comes to life again. These people are innately driven to act as a normal ape or savage would, but such behavior is deemed criminal in our civilized society.
This belief system made it very easy to “identify born criminals because they bear anatomical signs of their apishness.” It was obviously a foolish idea.
The New Algorithmic Phrenology
Machine-learning algorithms have recently experienced an explosion of uses, some legitimate and others problematic. Still others are downright racist. Several recent examples promise governments and private companies the ability to glean private information from people’s appearances alone. Stanford University researchers have constructed a “gaydar” algorithm that they claim can differentiate straight and gay faces far more accurately than people can.
The researchers claim that their results are consistent with the “prenatal hormone theory” – an idea supposing that fetal exposure to high levels of male sex hormones called androgens helps to determine sexual orientation. Why they assumed high levels of male hormones would determine homosexuality was not clear. The researchers cite this “much-contested claim” that these hormone exposures also result in gender-atypical faces. The problem is, rather than producing objective insights, artificial intelligence (AI) programs often reinforce human biases. These biases, if trusted in practice, can harm already marginalized populations.
One example can be found in China: “University students taking online exams monitored by proctoring algorithms not only have to answer the questions, but also maintain the appearance of a student who is not cheating.” Unfortunately for the students, these algorithms reportedly often make false accusations against minority students, such as those with disabilities who move their faces and hands in atypical ways. Black or dark-skinned students have been required to work under bright lights to have their relevant features more detectable. The most egregious example is the attempt to read faces to identify “criminal types”:
AI researchers Xiaolin Wu and Xi Zhang of Shanghai Jiao Tong University, claimed to have trained an algorithm to identify criminals based on the shape of their faces, with an accuracy of 89.5 percent. …. The researchers didn’t go quite so far as to endorse the ideas about physiognomy and character that circulated in the 19th century, notably from the work of the Italian criminologist Cesare Lombroso: that criminals are under-evolved, subhuman beasts, recognizable from their sloping foreheads and hawklike noses.
The Hooked Nose Fiasco
The alleged hawk-like nose trait was exploited by the Nazis, who pinned this trait on Jews and pictured it in Nazi propaganda (see Figures 1. and 2.). A 1911 study of 4,000 Jewish noses found no significant difference between the size and shape of Jewish noses as compared to those of the general population. Stinson added, “the study’s seemingly high-tech attempt to pick out facial features associated with criminality borrows directly from the ‘photographic composite method’.. .. which involved overlaying the faces of multiple people to find the features indicative of qualities such as …. criminality.” The photographic composite method was developed by eugenic founder Francis Galton who was inspired (and his work approved by) his cousin Charles Darwin. Stinson added:
Technology commentators have panned these facial-recognition technologies as “literal phrenology”; they’ve also linked some applications to eugenics, phrenology’s parent pseudoscience that aims to “improve” the human race by encouraging people deemed the fittest to reproduce, and discouraging childbearing in those deemed unfit. Galton himself coined the term eugenics, describing it in 1883 as “all influences that tend in however remote a degree to give to the more suitable races or strains of blood a better chance of prevailing speedily over the less suitable than they otherwise would have had.”
She also noted
China’s surveillance of ethnic minorities has the explicit goal of denying opportunities to those deemed unfit. Technologies that attempt to detect the faces of criminals or exam cheaters … tend to lead to the same predictable result: a lot of false positives for already marginalized people, leading to the denial of [their] rights and opportunities.
The use of physical traits to identify putative inferior humans has a long history, but some have recognized its problems during its long history:
Phrenology had its share of empirical criticism in the 19th century, too. Debates raged about which functions resided where, and whether skull measurements were a reliable way of determining what’s going on in the brain (they’re not). The most influential empirical criticism of old phrenology, though, came from French physician Jean Pierre Flourens’s studies based on damaging the brains of rabbits and pigeons—from which he concluded that mental functions are distributed, rather than localized.
The “new” phrenology efforts are also just as problematic. The recent AI criminality study utilized ID photos of convicts provided by police and professional photos of non-convicts from the internet. Pictures that people willingly post on the internet tend to show them in the best light, but police photos tend to show people in their worst circumstances, and their faces show it. Obviously, the algorithm could detect a difference between the two groups and, I assume, most observers could as well. Figure 3, below, shows photos of women, illustrating how subjective the criminology judgment can be when all of the pictures are very similar in most traits except the face.
In the “gaydar” algorithm test, an obvious explanation for the differences is that the study used pictures of self-identified gay and straight people from dating sites. But each group was attempting to appeal to different populations. Even the lighting differences and the angle from which the picture was taken can account for face-trait differences.
Inner vs Outer Human Traits
Even more problematic is that the Wu and Zhang research “suggests that criminality is an innate characteristic, rather than a response to social conditions such as poverty or abuse, or a label applied to exert social control.” One of the strongest moral objections to using facial recognition to detect criminality is that it stigmatizes people. The authors of the criminality paper admit to an even worse problem: the fact that the
false-positive rate is very high (more than 95 percent of people it classifies as criminals have never been convicted of a crime), … . Those false positives would be individuals whose faces resemble people who have been convicted in the past. Given the racial and other biases that exist in the criminal justice system, such algorithms would end up overestimating criminality among marginalized communities.
Another question is whether repackaging a long disproved and harmful idea—phrenology—is worthwhile:
Eugenicists of the past such as Galton and Lombroso ultimately failed to find facial features that predisposed a person to criminality. That lack of evidence is almost certainly because there are no such connections to be found. Likewise, psychologists who studied the heritability of intelligence, such as Cyril Burt and Philippe Rushton, had to play fast and loose with their data to make it look like they had found genuine connections between skull size, race, and IQ. If there were anything to discover, presumably the many people who have tried over the centuries wouldn’t have come up dry.
Artificial intelligence algorithms have even more power than math to impress (and mislead) the average person:
Complex personal traits such as a tendency to commit crimes are exceedingly unlikely to be genetically linked to appearance in such a way as to be readable from photographs. … For a complex social trait such as criminality, this clustering is extremely unlikely. A much more likely hypothesis is that any association that exists between appearance and criminality works in the opposite direction: A person’s appearance influences how other people treat them, and these social influences are what drives some people to commit crimes (or to be found guilty of them).
A False Facade of Science
Wu and Zhang try to justify their work by claiming that we should look at the evidence even when it could have serious adverse effects on people. This rationale was pioneered by prominent eugenicist and statistician, Karl Pearson who was one of the leading founders of the field of statistics. Mathematician Aubrey Clayton argues that statistical significance testing was developed to give a “mathematical sheen to eugenic claims that only flawed methods could prop up: ‘By slathering it in a thick coating of statistics, Pearson gave eugenics an appearance of mathematical fact that would be hard to refute.” Using statistical methods, Wu and Zhang have similarly produced results that may look statistically significant, but are highly misleading. AI algorithms have even more power than math to give the impression of scientific credibility. Stinson concluded that the
problem with reinventing eugenic methods such as phrenology cloaked in new technological guises is not merely that it has been tried without success many times before…. it’s hard to justify trying it one more time. …For scientists to take their moral responsibilities seriously, they need to be aware of the harms that might result from their research. Spelling out more clearly what’s wrong with the work labeled “phrenology” will hopefully have more of an impact than simply throwing the name around as an insult.
It seems that even bad evolutionary ideas tend to be recycled, and this one is just one more example.
 Bergman, Jerry. 2020. Phrenology: A Myth Behind Darwinism. Journal of Creation 34(1):115-122.
 Bergman, 2020.
 Parker, J.O., F. Alfaro-Almagro, and S. Jbabdi. 2018. An empirical, 21st century evaluation of phrenology. Cortex 106:26–35.
 Stinson, Catherine. 2021. “The Dark Past of Algorithms that Associate Appearance and Criminality.” American Scientist 109(1):26-29, p. 26.
 Bretherick, Diana. 2015. The ‘born criminal’? Lombroso and the origins of modern criminology. https://www.historyextra.com/period/victorian/the-born-criminal-lombroso-and-the-origins-of-modern-criminology/.
 Rafter, Nicole Hahn. 1997. Creating Born Criminals. University of Illinois Press, Urbana and Chicago, IL, p. 110.
 Gould, Stephen Jay. 1996. The Mismeasure of Man. W.W. Norton and Co., New York, p. 153.
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 Ullmann, Jeremy. 2020. UNDERSTANDING THE ANTISEMITIC HISTORY OF THE “HOOKED NOSE” STEREOTYPE. https://www.media-diversity.org/understanding-the-antisemitic-history-of-the-hooked-nose-stereotype/
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 Wu, X., and X. Zhang. 2016. Responses to critiques on machine learning of criminality perceptions. (Addendum of arXiv:1611.04135); Wu, X., and X. Zhang. 2016. Automated inference on criminality using face images. arXiv:1611.04135v1
 Agüera y Arcas, B., M. Mitchell, and A. Todorov. 2017. Physiognomy’s new clothes. Medium, May 6; Agüera y Arcas, B., A. Todorov, and M. Mitchell. 2018. Do algorithms reveal sexual orientation or just expose our stereotypes? Medium, January 11.
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 Clayton, Aubrey. 2020. How eugenics shaped statistics. Nautilus, October 28.
 Van Noorden, R. 2020. The ethical questions that haunt facial-recognition research. Nature 587:354–358.
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Dr. Jerry Bergman has taught biology, genetics, chemistry, biochemistry, anthropology, geology, and microbiology for over 40 years at several colleges and universities including Bowling Green State University, Medical College of Ohio where he was a research associate in experimental pathology, and The University of Toledo. He is a graduate of the Medical College of Ohio, Wayne State University in Detroit, the University of Toledo, and Bowling Green State University. He has over 1,300 publications in 12 languages and 40 books and monographs. His books and textbooks that include chapters that he authored are in over 1,500 college libraries in 27 countries. So far over 80,000 copies of the 40 books and monographs that he has authored or co-authored are in print. For more articles by Dr Bergman, see his Author Profile.