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Another Scientific Incarnation of Selective Correlation
When the 19th century was young, a Viennese physician Franz Joseph Gall got the ball rolling for the "science" of phrenology. (Not to be confused with phenology.) Phrenology believed that the shape and contour of a person's skull revealed their character, and thus could be used by employers and the criminal justice system to identify the lazy and the miscreants with simply a few quick measurements.
It also came in handy to justify slavery in the U.S., as depicted in Tarantino's Django Unchained.
Phrenology never went away, but went on to lurk in spin-offs such as eugenics. And if there were to be an updated incarnation of using a few quick body measurements to find the evil among us, it would have to employ sci/tech terms as "researchers", "algorithms" and "AI".
And so it does: Convict-spotting algorithm criticised
Researchers trained an algorithm using more than 1,500 photos of Chinese citizens, hundreds of them convicts.
They said the program was then able to correctly identify criminals in further photos 89% of the time. But the research, which has not been peer reviewed, has been criticised by criminology experts who say the AI may reflect bias in the justice system. "This article is not looking at people's behaviour, it is looking at criminal conviction..."
So, will AI ever get this god-like?
[Continues...]
AI Can Predict the Future Criminals Based on Facial Features
The bankrupt attempt to infer moral qualities from physiology was a popular pursuit for millennia, particularly among those who wanted to justify the supremacy of one racial group over another. But phrenology, which involved studying the cranium to determine someone's character and intelligence, was debunked around the time of the Industrial Revolution, and few outside of the pseudo-scientific fringe would still claim that the shape of your mouth or size of your eyelids might predict whether you'll become a rapist or thief.
Not so in the modern age of Artificial Intelligence, apparently: In a paper titled "Automated Inference on Criminality using Face Images," two Shanghai Jiao Tong University researchers say they fed "facial images of 1,856 real persons" into computers and found "some discriminating structural features for predicting criminality, such as lip curvature, eye inner corner distance, and the so-called nose-mouth angle." They conclude that "all four classifiers perform consistently well and produce evidence for the validity of automated face-induced inference on criminality, despite the historical controversy surrounding the topic."
[...] The study contains virtually no discussion of why there is a "historical controversy" over this kind of analysis — namely, that it was debunked hundreds of years ago. Rather, the authors trot out another discredited argument to support their main claims:, that computers can't be racist, because they're computers:
[...] Absent, too, is any discussion of the incredible potential for abuse of this software by law enforcement. Kate Crawford, an AI researcher with Microsoft Research New York, MIT, and NYU, told The Intercept, "I'd call this paper literal phrenology, it's just using modern tools of supervised machine learning instead of calipers. It's dangerous pseudoscience."
(Score: 2) by AthanasiusKircher on Sunday November 27 2016, @10:36PM
There are centuries of socioeconomic history that confound the topic...
And there ya go rejecting the premise of the theoretical. Assume it works.
I don't read this as "rejecting the premise of the theoretical." That issue is actually at the heart of whether and how "it works." A predictive statistical model need not deal directly with causality. In fact, one major point of the Scientific Revolution was essentially a rejection of the premise that science must always have a causal explanation for a mathematical model. Looking for causality is Aristotelian thinking. Newton spearheaded modern science by effectively saying, "Yeah, there are these unseen forces in my model, but I don't know if they really exist or what the true cause is... but the math works." (He specifically added such apologetics to later editions of the Principia, since many criticized his "unseen forces" as potential claptrap, which belonged more to mystical traditions like alchemy and Hermeticism than "science.")
To me, it sounds like you're attempting to skirt the same issue here, by "assuming it works," without addressing the mechanism. In some areas of science, that's reasonable if you're just trying to create a mathematical model. But we're trying to do more here -- we're presumably trying to find the CAUSE of crime and perhaps PREVENT it. We're trying to change nature. That's different, and it's less like Newton's physics as abstract math and more like the way people research diseases or sociologists look for causes of poverty or whatever.
So, in the present case, it's not just the "socioeconomic history," but the ongoing effects of that history. "Confounding factors" are essential to determine causality and to aid in effective prevention. As I posted separately here, when you take poverty into account, a lot of the apparent differences in white vs. black crime rate disappear. Not all, but a lot. If we want to address crime for the future, should just target those with "African" facial features (as a naive math model might tell us)? Or should we look at broader societal factors which may be behind the apparent differences in our facial feature study?
This isn't rejecting the premise that a mathematical model might "work" -- it's asking WHY the model appears to work, and how we might act on it, depending on whether the physical features are causally related vs. just a symptom of other issues that seem more causal upon examination.
Or are you actually proposing that "assume it works" means that we've determined causality -- that somehow we can prove that facial ridges cause crime? Or that the same developmental process creates facial ridges as creates brain patterns that lead people toward crime?? IF we could actually prove that beyond a reasonable doubt using rigorous science, I have no doubt it would lead to a new eugenics movement, selective abortion, etc. But I sincerely doubt studies like this are ever going to get close to proving causality like that. (And that wasn't even really suggested in your initial post, where you referenced "big data" replication, not neurophysical investigations of causal links or whatever.)