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First of all, I don't think this is satire. I'll admit that the use of a gmail account by a researcher at a Chinese uni is facially suspicious, but it's not that odd given that cursory googling shows that both authors appear to be faculty members at Shanghai Jiao Tong University as claimed on the paper- though neither appears to have much, if any, background or expertise in machine learning. I'm not much of a fan of a lot of the arguments made in Weapons of Math Destruction, but I do appreciate that in summarizing you draw the distinction between the biases of the engineer or (illogically, but oft-claimed nonetheless) the algorithm itself and the data which is used to train said model, and I think it's quite a valuable concept in regards to this particular paper. For instance, the data set they're using here is fairly small, and while, they did use 10-fold cross-validation, that's still a bit on the less than ideal side generally speaking neural nets, especially CNN architectures, which are usually pretty deep. Furthermore, the dataset itself seems fairly questionable to me. I'm not sure how much I trust the Chinese criminal justice system to adequately adjudicate culpability in the first place, but even setting aside such admittedly conspiratorial notions, it seems rather odd indeed that nearly half of their positive samples are not in fact convicted criminals but merely suspects. I do not find their attempts at devil's advocate persuasive as it's not readily obvious exactly how they used or obtained any of their testing with the three different data sets. As for the appropriateness of the broader topic, I'm more or less of the persuasion that all questions deserve to be examined, and that provided the work does not cause direct harm, it's hard for me to support a prohibition on examination of a given topic. That said, I do think that the more controversial the question, the higher quality of research required, and, good lord, does this mess fall well short of the mark. Perhaps if there existed a hypothetical criminal justice system free of systemic biases or, more realistically, a method by which to exactly define those prejudices and account for them in the composition of a data set, this could be a potentially useful question to investigate, but even then it seems to me quite unlikely that there's any particularly significant relationship between one's upper lip curvature and criminal disposition. |
But to do it you need experts in criminology, physiology and machine learning, not just a couple of people who can follow the Keras instructions for how to use a neural net for classification.
For example, I think I remember reading a papers in the physiology field that show a link between increased testosterone and different facial features - but from memory (and I don't have the paper) there was no link between that and criminal offending.
In this case, the features they are finding don't seem to make any sense. A slight smile in the criminals seems more likely to be due to the way that set of photos are taken, and a number of the other features could possibly be explained by the fact the criminal set came from a single police department (in a single geographical area), while the other dataset was collected online. Given the small size of the dataset, if it included a single "family"-gang of criminals it is likely that would have been enough to taint the features.