Read the following passage and questions, then choose the right answer from options, the answer should be one of A, B, C, D.

<passage>:
We know that paedophiles, murderers and other violent criminals come in many shapes and sizes. If we knew nothing about their criminal history, some of their photos might even appear attractive. But the idea that someone's features betray their character is something rooted deep within us it's the reason why certain photos perform well on dating apps, or why trustworthy-looking politicians might rack up votes. But how wrong are our hunches of perceived criminality? 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. It's a claim that's been made many times over the years. 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 studies show that people with stereotypically 'untrustworthy' faces tend to receive harsher treatment than those who don't. There's evidently some consensus over people's attitudes toward certain faces, but it doesn't follow that the consensus is correct. The only attributes that we're reasonably good at detecting, according to research done at the University of Michigan in the 1960s and later tested at the University of Stirling in 2007, are extroversion and conscientiousness. For other traits there's insufficient evidence that our hunches are correct, with anomalies explained by our evolved aversion to 'ugliness', established links between broader faces and powerful physiques, or cultural associations with certain demographics which are reinforced with nagging regularity by newspapers, books, television and film. Data-driven studies, based upon huge quantities of facial data, would seem to offer the final word on this. Since 2005, computational models have used various techniques to test for links between social attributes and facial features, resulting in suggestions that our faces can betray, for example, political leanings, sexual orientation and criminality. One BBC Future article from 2015 even describes the 'discipline' of physiognomy as 'gaining credibility'. But Todorov details many problems with these studies, pointing out the challenging nature of doing such experiments with sufficient rigour – not least because different images of the same people can prompt wildly differing results. The aforementioned study at Shanghai's Jiao Tong University, with its enthusiastic, data-driven analyses of such questions as "What features of a human face betray its owner's propensity for crimes?" prompted a wave of press coverage. The vision outlined in these articles is of an unethical dystopia where neural networks can assess our faces and establish a likely score for criminality – but Todorov is scathing about this paper, too. "The main problem is the sampling of the images," he says. "There is not enough information about the [nature of] the images of the people who were convicted. Second, clearly, there are huge differences between the two samples [of convicts and non-convicts] [in terms of] education and socio-economic status." In other words, your appearance is affected by the kind of life you've led, so the classifiers within the computer program are simply distinguishing between different demographics rather than detecting a propensity for criminal behaviour. Todorov is also wary of these classifiers misidentifying more 'innocent' people than identifying actual criminals, and accuracy is a concern shared by Peter Hancock. "Networks don't assess faces in the same way that we do," he says. "One of our systems, which is a deep network, has a recognition engine which generates an ordered list of how similar various faces are. And sometimes you get good matches – but other times you look at them and say, well, it's the wrong race! To humans they look completely different. And that underlines the fact that the networks are working in a different sort of way, and actually you don't really know how they're working. They're the ultimate black box." This isn't to say that the use of big data, and particularly the use of composite imagery (digitally blending together certain types of faces) doesn't give us useful information and fascinating correlations. "You can, for example, take a given face and use computer software to make it look more or less trustworthy," says Hancock. "I remember a colleague playing with this and he made a less trustworthy version of George W Bush – and how shifty did he look! I'm surprised that they're not using these techniques in political advertising, because you couldn't tell that anything had been done [to the picture], but when you look at it you think 'I wouldn't trust him'." The revitalisation of the theory of physiognomy by the Shanghai students is, according to Todorov, deeply problematic on a theoretical level. "Are we back to Lombroso's theory," he asks, "that criminals were anomalous creatures, evolutionary degenerates? How does one become criminal, and what role do various life forces play into this? There are people making claims that you just need to look at the face to predict personality and behaviour, but many of these people have not given much thought to their underlying assumptions." While it's true that we judge books by their covers, covers are more than just faces We attribute social characteristics based on opinions we already hold about certain kinds of faces: whether they look unusual in some way, whether they resemble a partner, a family member or even ourselves, or perhaps have some other cultural association. Physiognomy ultimately stems from what Alexander Todorov, professor of psychology at Princeton University, calls an 'overgeneralisation hypothesis'. "People," he wrote, "use easily accessible facial information (eg an expression such as a smile, cues to gender and ethnic group) to make social attributions congruent with this information (eg a nice person)." In a social media age, the pictures we choose to represent ourselves online are a form of self-presentation driven by those social attributions and the knowledge that our pictures are being judged. Experiments at Princeton found that we take less than one tenth of a second to form an opinion of strangers from their pictures, and those opinions tend to stand firm even if we're exposed to those pictures for a longer period of time. That tendency to judge instantly gives rise to a number of selfie tropes that are deemed to elicit positive responses, particularly when it comes to photos on dating profiles: certain angles, particular expressions, minute adjustments of eyebrows and lips that might appear to be about narcissism and vanity, but are more about a fear of being incorrectly assessed. After all, false suppositions based on people's faces are hugely influential within society, and in extreme cases they can have a huge impact on people's lives.

<question>:
Which would the author think is most true?

<options>:
A We post pictures of ourselves online that we think are attractive to gain approval from specific people whose eyes we want to catch
B Regular people can use their social media accounts to help locate bad people before crimes are committed, because people are better at this than computers
C People know their photos are being judged by others when they post them so they critically judge them themselves first
D The application of machine learning in the study of social media photos could make it easier to find criminals before they commit cimes

<answer>:

C