 According to the author, what are people actually judging when they believe they're detecting a proclivity for delinquent behavior?
Some blamed the BBC, some blamed those in Savile's immediate circle, but others blamed a simple error of human judgment. A recent paper, published by Xiaolin Wu and Xi Zhang of Shanghai's Jiao Tong University, claims to be the first to use machine learning and neural networks to attempt a fully automated inference of criminality from facial images, removing prejudice from the equation and testing the validity of our gut feelings. "What facial features influence the average Joe's impulsive and yet consensual judgments on social attributes?" they ask. Through a study of 1,856 images ("controlled for race, gender, age and facial expression") they claim to have established the validity of "automated, face-induced inference on criminality, despite the historical controversy surrounding this line of enquiry." In other words, they believe that they've found a relationship between looking like a criminal and actually being one. Physiognomy, the 'science' of judging people by their appearance, was first theorised by the ancient Greeks in around the 5th century BC. Aristotle's pronouncement that "it is possible to infer character from features" led to a number of works relating to 'Physiognomica', a word derived from physis (nature), nomos (law) and (or) gnomon (judge or interpreter). Theories of physiognomy, however, would persist beyond the Renaissance.
 demographics
 media filtering  prejudice  intelligence  demographics