Some people ask ML to do patently implausible things. Can you determine whether someone is a criminal from a photo of their face? It should be INCREDIBLY obvious that the answer to that question must be "no." Even if you do manage to guess correctly, there is has to be some confound, either a technical one (criminal training data is lit differently from the non-criminal ones) or a statistical one (e.g., correlation with socioeconomic status).
This is certainly a problem, made worse by AI hype in the press. People expect superhuman performance where it doesn't exist. Substantial part of the job when consulting in this field is bringing down the expectations to something achievable, preferably without ruining the client's willingness to pay.
Controversial but clearly not "patently implausible".
There is /no/ evidence that physiognomy or its cousin phrenology, the idea that scalp shape carries information, "work." I normally appreciate wikipedia's NPOV stance, but it's absurd that it takes two paragraphs to mention that it is universally (or almost, apparently) regarded as psuedo-science. I'm a neuroscience researcher, and I can't think of a single colleague who puts any credence in these ideas; in fact, I know several who use them as insults. As for the data, the hair-whorl things have been pretty aggressively debunked. The "gaydar" results were driven by individual choices in fashion, grooming, etc. I don't know if anyone has followed up on the hockey data, but...it doesn't matter, because of the second problem.
There's a giant leap between detecting actual criminals and people who look like members of groups that are, statistically more/less likely to be involved in crime. You just can't jump between group-level priors and individual predictions. This is especially true when some of the factors shouldn't legally or morally be used to make predictions.
Finally, think about how weird the biology would need to be for this to work. You'd need to have an underlying factor (genetic, presumably) that affects both facial structure and behavior. It would need to have a strong enough effect to reliably overcome all of the other factors that also determine someone's appearance and behavior. It's not totally impossible, but it's an extraordinary claim that would require extraordinary evidence and to date, no one has found much of anything.
Why are you so sure? For one thing, I bet criminals are more likely than the law-abiding to have facial tattoos. Of course, ML analysis of faces will not have 100% accuracy, but that is usually the case in ML and statistics.
It is possible to do this better than random guessing!
However, I can easily imagine this "working" by keying off things like age/race/gender, which will get you a value that's "better than chance" but isn't, really. Ditto for differences in the photos; nobody smiles in a mugshot, after all.
(And yes, I know about the paper purporting to show this [https://arxiv.org/pdf/1611.04135.pdf] I just think it's ridiculous)
Age, race, gender, income, attractiveness, testosterone levels, gang membership, attention to grooming, addiction, etc. are all predictive of crime, and part of the face picture you use.
Sure, there is some bias in how you label a criminal (convicted of a crime, self-report, etc.), but prediction is possible (without exploiting leakage like smiles or lighting).
Just from a hot-or-not rating I can make an educated guess of your conviction rate and sentencing length.
I have absolutely no doubt that features like age, race, and gender can be associated, at a group level, with crime. I'm also sure all of these can be extracted from face images. At the same time, these data are obviously not enough to make subject-level predictions. At best, this is a prior and even that is contaminated with all kinds of systematic biases.
Suppose you're hiring. You are certainly allowed—and sometimes required—to not hire criminals. If you systematically avoid hiring people with features "predictive of crime", an employment lawyer is going to slap you into tomorrow with a totally justified, slam-dunk of an employment discrimination case.