UPDATE (6/30/2020): The article “Criminal tendency detection from facial images and the gender bias effect” was retracted because the authors “did not seek approval from their ethics committee before undertaking this study that uses human biometric data.” This was one of the many problems I pointed out about the original study.

更新(6/30/2020)： 文章“从面部图像检测犯罪趋势和性别偏见” 被撤回， 因为作者“在进行使用人类生物特征数据的研究之前未征求其伦理委员会的批准。” 这是我针对原始研究指出的众多问题之一。

A recent paper about to be published by Harrisburg University caused quite a stir earlier this month. Titled “A Deep Neural Network Model to Predict Criminality Using Image Processing,” the paper promised:

哈里斯堡大学即将发表的一篇最新论文引起了本月初的轰动。 该论文题为“使用图像处理来预测犯罪的深度神经网络模型”， 该论文承诺 ：

With 80 percent accuracy and with no racial bias, the software can predict if someone is a criminal based solely on a picture of their face. The software is intended to help law enforcement prevent crime.

该软件具有80％的准确性，并且没有种族偏见，可以仅基于其面部图片来预测某人是否为罪犯。 该软件旨在帮助执法部门预防犯罪。

This claim is absolutely ludicrous, as will be discussed below.

这项要求绝对是荒谬的，下面将对此进行讨论。

Wide-ranging criticism from various researchers caused the paper to be pulled. Another paper from 2016 made similar promises — it too was widely criticized, causing the authors to prepend a response to their original paper on arXiv.

各种研究人员的广泛批评 导致该论文被取消 。 2016年的另一篇论文也做出了类似的承诺-也遭到了广泛的批评，这导致作者对他们在arXiv上的原始论文做出了回应。

And yet, there was another paper published just months ago in January 2020 which has largely escaped similar notice and outrage. Hashemi and Hall’s “Criminal tendency detection from facial images and the gender bias effect” published by the Journal of Big Data claims the following:

然而，仅在几个月前的2020年1月，又发表了另一篇论文，在很大程度上避免了类似的注意和愤怒。 Hashemi和Hall的“从面部图像中发现犯罪倾向和性别偏见”，由大数据杂志出版 要求以下内容：

[T]his paper explores the deep learning’s capability in distinguishing between criminal and non-criminal facial images . . . CNN [convolutional neural network] achieves a tenfold cross-validation accuracy of 97%.

他的论文探讨了深度学习在区分犯罪和非犯罪面部图像方面的能力。 。 。 CNN(卷积神经网络)实现了十倍的交叉验证准确性，达到97％。

Claiming 97% accuracy in predicting criminality from a person’s photograph is a bold claim. As we will see, due to problems in both its conception and execution, this claim is utterly unfounded.

声称从一个人的照片预测犯罪的准确性达到97％是一个大胆的主张。 正如我们将看到的，由于其概念和执行方面的问题，这种主张是完全没有根据的。

This will be shown by looking at the pseudoscientific framework of the study as well as practical problems with the experiment, including an attempt to reproduce similar results by training a deep learning model. It turns out that we can get similarly excellent results that have nothing to do with “criminality” but with fundamental experimental errors.

通过查看研究的伪科学框架以及实验中的实际问题(包括尝试通过训练深度学习模型来重现类似结果)，可以证明这一点。 事实证明，我们可以获得类似的出色结果，与“犯罪”无关，但与基本的实验错误无关。

什么是犯罪？ (What is a crime?)

Crime is a social construct. This means that what we call a “crime” or a “criminal act” is actually an evolving definition in society depending on the place and time we are referring to. It has nothing to do with biology and has no higher, unchanging definition outside of how society defines it.

犯罪是一种社会建构。 这意味着我们所说的“犯罪”或“犯罪行为”实际上是社会中不断发展的定义，这取决于我们所指的地点和时间。 它与生物学无关，在社会定义之外没有更高的，不变的定义。

Gambling and prostitution are criminal acts in California but are legal industries in Nevada. Marijuana sale and consumption is legal in California but that was not the case until recently, and it remains a federal crime.

赌博和卖淫在加利福尼亚州是犯罪行为，但在内华达州是合法行业。 大麻的销售和消费在加利福尼亚是合法的，但直到最近才如此，这仍然是联邦犯罪。

But these are victimless crimes, one might argue. What about murder?

也许有人会说，这些都是无受害者的罪行。 那谋杀呢？

If you plant a bomb in your neighbors house and it explodes and kills them, you could be found guilty of arson and murder. But if the president declares war on Iraq and bombs an entire neighborhood in Baghdad, that might not be considered a criminal act (under US law) although some of us would find it morally objectionable.

如果您在邻居家中放置炸弹，并爆炸并杀死他们，您将被判纵火和谋杀罪。 但是，如果总统对伊拉克宣​​战并轰炸巴格达的整个社区，那么(根据美国法律)这可能不算是犯罪行为，尽管我们当中有些人会在道德上感到反感。

It used to be legal for a slaveowner to kill their slave, and entire economies and cultures saw this imbalance of power as a fundamental right of their society. Now in the US it is a crime to enslave somebody in the first place. A person who is kidnapped, held captive for a period of time and brutalized might be entirely justified, both morally and legally, in killing their captor in order to win back their freedom. A person who otherwise would never take another human life might be willing to do so in such circumstances.

奴隶主杀死他们的奴隶曾经是合法的，整个经济和文化都将这种权力失衡视为其社会的一项基本权利。 现在在美国，首先奴役某人是犯罪。 被绑架，被俘虏一段时间并遭受残酷对待的人在道义上和法律上完全有理由杀害俘虏以夺回自由。 在其他情况下，如果没有其他人的生命，他可能会愿意这样做。

In other words, there is nothing inherently biological about crime. It has nothing to do with your genes, much less your face.

换句话说，犯罪没有内在的生物学性。 它与你的基因无关，更不用说你的脸了。

No machine learning algorithm, not even a really good convolutional neural network, can predict crime based on a photo or other biological information about a person because “crime” has less to do with you as it does with how a society judges and responds to your actions.

没有机器学习算法，甚至不是一个真正好的卷积神经网络，都无法基于有关某人的照片或其他生物信息来预测犯罪，因为“犯罪”与您的关系较小，而与社会如何判断和应对您的行为无关动作。

Does this mean that if somebody kills another human being we just throw our hands up and say, “Well, it’s just a social construct!” No, of course not. But we have to put it into context and look at what sort of social harm was caused. In some cases, killing is inhumane and unconscionable, while in others it may be an act of self-defense and the only difference may not be the action but the society in which they occur.

这是否意味着如果有人杀死另一个人，我们就举手说：“嗯，这只是一种社会建构！” 不，当然不是。 但是，我们必须将其置于上下文中，并研究造成了什么样的社会伤害。 在某些情况下，杀戮是不人道和不合情理的，而在另一些情况下，杀戮可能是自卫行为，唯一的区别可能不是行为，而是发生这种行为的社会。

The point here is that “criminality” is an incredibly vague and broad concept and the acts that we might deem criminal (or not criminal) change over time for better and for worse.

这里的重点是“犯罪”是一个非常模糊和广泛的概念，我们认为犯罪(或非犯罪)的行为会随着时间的流逝而变坏。

How do Hashemi and Hall address these ambiguities? They do not address them at all. In fact, they have not even built a data set which consistently labels faces correctly as criminals or non-criminals, even using the most basic and widely accepted definition used in the US.

Hashemi和Hall如何解决这些歧义？ 他们根本没有针对他们。 实际上，即使使用美国使用的最基本且被广泛接受的定义，他们甚至都没有建立能够正确地将面Kong正确标记为罪犯或非罪犯的数据集。

直到被证明对数据科学有用的天真 (Innocent until proven useful to data science)

Hashemi and Hall use two sets of faces to train their models. First, their “criminal” faces are gathered from a set of mugshots made available by the National Institute of Standards and Technology (NIST). Their “non-criminal” faces are gathered from a number of generic non-mughsot sources managed by facial recognition researchers at various universities.

Hashemi和Hall使用两组面Kong来训练他们的模型。 首先，他们的“犯罪”面Kong是从美国国家标准技术研究院(NIST)提供的一系列照片中收集的。 他们的“非犯罪”面Kong是从不同大学的面部识别研究人员管理的许多通用的非穆格索特人那里收集的。

The problem is, the mugshots do not necessarily depict criminals. These are people who have been arrested, not necessarily convicted of crimes. This is a database of people who the police think are guilty of crimes, or in some cases, people that the police just felt like arresting.

问题是，面部照片不一定描绘罪犯。 这些人已经被捕，不一定被定罪。 这是一个数据库，其中包含警察认为犯有罪行的人员，或者在某些情况下，警察只是觉得喜欢逮捕他们的人员。

On a side note, this also raises ethical issues as to whether the people in the mugshots have consented to have their images used for such purposes. Police regularly take mugshots and make them public in order to shame the people they have arrested and many journalists, to their great discredit, reprint them without permission. Again, mugshots are taken well before a person is convicted of a crime, if they are ever convicted. It is not at all clear that any of them have either been convicted of a crime or have consented to having their image widely distributed.

附带说明，这也引发了关于面部照片中的人是否同意将其图像用于此类目的的道德问题。 警察定期拍摄面部照片并将其公开，以使他们被捕的人感到羞耻，而且，许多记者在未经允许的情况下转载了这些照片，这使他们大失所望。 再次，如果有人被定罪，则在被定罪之前早已拍摄了照片。 根本不清楚他们中的任何人被定罪或同意广泛传播其形象。

On the other hand, Hashemi and Hall’s generic image databases of supposedly non-criminals do not necessarily depict people who have not been convicted of a crime. We have no idea one way or the other. In fact, there is no reason why a person from the mugshot group may not also have their face in one of the generic databases, other than the statistical unlikelihood of this.

另一方面，哈塞米(Hashemi)和霍尔(Hall)的非犯罪嫌疑人通用图像数据库并不一定描绘出没有被定罪的人。 我们不知道一种方法。 实际上，除了统计上的可能性之外，面部照片小组中的人没有理由没有在通用数据库之一中露面。

A Justice Department study estimated that one-third of working age adults in the US have an arrest record. It is statistically very likely that many of the “non-criminals” have been charged and some convicted of crimes at one time in their life, though their photos used in the experiment would show them in much happier times than when their mugshot was taken.

司法部的一项研究估计，美国有工作年龄的成年人中有三分之一有逮捕记录。 从统计学上讲，很可能很多“非罪犯”一生中都曾被指控，有些人被定罪，尽管实验中使用的照片显示出的照片比拍摄面部照片时快乐得多。

This is a stunning oversight. One does not need to be a sociologist to appreciate this problem with the data. One merely needs to have the most naive definition of what a crime is.

这是一个令人震惊的疏忽。 无需成为社会学家即可了解数据中的这一问题。 一个人只需要对犯罪是最幼稚的定义。

In spite of this, Hashemi and Hall claim to have trained a model that can accurately predict whether an image comes from the mugshot group or the non-mugshot group with 97% accuracy. How did they do this?

尽管如此，Hashemi和Hall声称已经训练了一个模型，该模型可以以97％的准确度准确地预测图像来自面部照片组还是非面部照片组。 他们是如何做到的？

One critique of their paper has noted a number of differences between the two datasets that a neural network would pick up on that has nothing to do with criminality or arrest:

对他们的论文的一种批评指出，神经网络将利用这两个数据集之间的许多差异，这与犯罪或逮捕无关：

All images for the Criminal category come from the NIST dataset, and all images for the Non-Criminal category come from a set of five datasets from other sources.

犯罪类别的所有图像均来自NIST数据集，非犯罪类别的所有图像均来自其他来源的五个数据集。

All of the images labeled Criminal are photographs of printed images and are taken in a controlled manner with the same camera model, and all of the images labeled Non-Criminal are photographs of live persons taken by various cameras.

所有标记为“犯罪”的图像都是打印图像的照片，并且是使用相同的相机模型以受控方式拍摄的，所有标记为“非犯罪”的图像都是由各种相机拍摄的活人的照片。

All of the images labeled Criminal were in (lossless) PNG format, and all of the images labeled Non-Criminal were in (lossy) JPG format.

所有标记为“犯罪”的图像均采用(无损)PNG格式，所有标记为“非犯罪”的图像均采用(有损)JPG格式。

All of the images labeled Criminal started out as grayscale; all of the images labeled Non-Criminal were converted from color to grayscale by the investigators.

所有标记为“犯罪”的图像最初都是灰度图像。 研究人员将所有标记为“非犯罪”的图像从彩色转换为灰度。

Hashemi and Hall note, “Such disparities which are not related to facial structure, though negligible in majority of cases, might have slightly contributed in training the classifier and helping the classifier to distinguish between the two categories.”

Hashemi和Hall指出：“与面部结构无关的这种差异尽管在大多数情况下可以忽略不计，但可能在训练分类器和帮助分类器区分这两个类别方面有所贡献。”

But even here, they are only referring specifically to the first two points (the different sources) and not the last two.

但是即使在这里，他们也只是专门指的是前两点(不同的来源)，而不是后两点。

These last two points were almost certainly detected by the neural network and are very likely to have been the primary cause of the 97% hit rate. We can show this in practice

这最后两点几乎可以肯定地被神经网络检测到了，很可能是造成97％命中率的主要原因。 我们可以在实践中证明这一点

再现结果 (Reproducing the results)

It would be quite easy to build a neural network under similar conditions as Hashemi and Hall and train a model that simply discriminates between JPGs and PNGs.

在与Hashemi和Hall类似的条件下建立神经网络，并训练仅区分JPG和PNG的模型，将是非常容易的。

In an afternoon, I did just that and the resulting Jupyter notebook can be found here.

一个下午，我就这样做了， 可以在这里找到生成的Jupyter笔记本 。

Starting with a sample project from the book Deep Learning with Python by François Chollet, I was able to build a neural network to distinguish between “criminal” and “non-criminal” images.

从FrançoisChollet的《 用Python深度学习 》一书中的一个示例项目开始，我能够构建一个神经网络来区分“犯罪”图像和“非犯罪”图像。

Instead of using mugshots, I used the MNIST database of images of handwritten digits retrieved via the Keras Python library.

我没有使用面部照片 ，而是使用了通过Keras Python库检索的手写数字图像的MNIST数据库 。

First, I randomly labelled each image as being a criminal (0) or non-criminal (1), then I trained a model to predict this arbitrarily defined criminality. Note that every image was given a criminality designation regardless of the content of the image, that is, regardless of what number was displayed in the image. So a handwritten “7” in one image was labelled as “criminal” and a “7” in another case was labelled as “non-criminal.” This was completely arbitrary and random and without any knowledge of whether the handwriting was from people with criminal convictions.

首先，我将每个图像随机标记为犯罪(0)或非犯罪(1)，然后训练了一个模型来预测这种任意定义的犯罪。 注意， 不管图像的内容如何 ，即不管图像中显示了多少数字，每个图像都被赋予犯罪标记。 因此，一个图像中的手写“ 7”被标记为“犯罪”，而在另一情况下的“ 7”被标记为“非犯罪”。 这是完全任意和随机的，并且不知道笔迹是否来自被定罪的人。

The model succeeded in correctly determining which of the handwritten digits I had designated as “criminal” versus “non-criminal” only about 50% of the time, no better than a random guess or coin toss. This is what we would expect because these designations were arbitrary.

该模型仅在大约50％的时间内成功地正确确定了我指定为“刑事”还是“非刑事”的手写数字，没有比随机猜测或抛硬币更好的了。 这是我们所期望的，因为这些名称是任意的。

But after I transformed all of my “criminal” images into PNG and “non-criminal” images to JPG, the resulting model that I trained succeeded over 99% of the time!

但是，当我将所有“犯罪”图像转换为PNG，并将“非犯罪”图像转换为JPG之后，我训练的结果模型在99％的时间内都成功了！

Is this a weird and arbitrary experiment? Yes, but no more so than attempting to detect criminality from a person’s face. In fact, a version of my experiment could be performed to attempt to detect criminality from handwriting. Taking handwriting samples from people in prison and other samples from people with no criminal record, we could reproduce the results above with the same accuracy — so long as the “criminal” samples were PNGs and the “non-criminal” samples were JPGs. Instead of handwriting samples, of course, we could also use faces.

这是一个奇怪而随意的实验吗？ 是的，但只不过是试图从一个人的脸上发现犯罪。 实际上，可以执行我的实验版本，以尝试从笔迹中检测犯罪行为。 从监狱中的笔迹样本和没有犯罪记录的其他样本中提取样本，只要“犯罪”样本是PNG，“非犯罪”样本是JPG，我们就可以以相同的精度重现上面的结果。 当然，除了手写样本，我们还可以使用面Kong。

In other words, what Hashemi and Hall built was probably nothing more than a sophisticated PNG vs JPG image format detector. They happened to train theirs on images of faces while I happened to train mine on images of handwritten digits. They achieved 97% accuracy with a convolutional neural network and 89% accuracy with a standard feedforward neural network — the latter being similar to how I trained my model with 99% accuracy.

换句话说，Hashemi和Hall建造的东西可能仅仅是复杂的PNG和JPG图像格式检测器。 他们碰巧在面部图像上训练自己的脸，而我碰巧在手写数字图像上训练我的脸。 他们通过卷积神经网络实现了97％的精度，并通过标准前馈神经网络实现了89％的精度-后者类似于我以99％的精度训练模型的方式。

The question then is not why they succeeded so well, but rather why their model fared so poorly.

然后的问题不是他们为什么成功这么好，而是他们的模型为什么表现这么差。

Putting all of these problems aside, there remain inherent racial issues with their experiment and its very conception.

撇开所有这些问题，他们的实验及其概念仍然存在固有的种族问题。

For example, FBI statistics show that Black people are four times more likely to be arrested than white people in the San Francisco Bay Area. This is true in many other large cities and metropolitan areas as well.

例如， 联邦调查局的统计数据显示 ，在旧金山湾区，黑人被捕的可能性是白人的四倍。 在其他许多大城市和大都市地区也是如此。

However, Hashemi and Hall note that “no control has been imposed on race, due to our small dataset and the difficulty and occasionally subjectivity of identifying the race from low-quality facial images.” A model trained as described could easily associate darker-skinned people with criminality due to their disproportionately high presence in the mugshots dataset and nothing else.

但是，Hashemi和Hall指出：“由于我们的数据集太小，以及从低质量的面部图像中识别种族的难度以及偶而的主观性，因此没有对种族施加任何控制。” 由于训练有素的皮肤模型数据集中在皮肤较黑的人群中比例很高，因此按上述方法训练的模型很容易将肤色较黑的人群与犯罪联系起来。

This would simply reflect the existing racism in the criminal justice system and further help to increase it rather than teaching us anything about inherent criminality. Such experiments also reinforce the often racist assumptions both police and juries make when believing that somebody “looks guilty.”

这只会反映出刑事司法系统中现有的种族主义，并进一步有助于增加种族主义，而不是教给我们关于固有犯罪的任何知识。 当相信某人“看起来有罪”时，这样的实验也加强了警察和陪审团经常做出的种族主义假设。

科学种族主义的回归 (The return of scientific racism)

This experiment harkens back to the scientific racism of the 19th and early 20th centuries. It was not so much scientific as it was a form of racism justified with psuedoscientific theories claiming to be based in Darwinian evolutionary theory but in fact having no scientific basis whatsoever.

这个实验可以追溯到19世纪和20世纪初的科学种族主义。 它不是那么科学，而是种族主义的一种形式，它以自称是达尔文进化论基础的人为主义的科学理论为依据，但实际上却没有任何科学依据。

These theories justified a number of racist policies including limits on immigration and forced sterilization of the poor and other “undesirables” who were disproportionately non-white. Much of this work originated in the US and became an inspiration for Hitler and the Third Reich.

这些理论证明了许多种族主义政策的合理性，包括限制移民和强迫绝大部分非白人的穷人和其他“不受欢迎的人”绝育。 这项工作大部分起源于美国，并成为希特勒和第三帝国的灵感来源。

After the horrors of World War II, these theories which had some hearing in academia became completely discredited. Hashemi and Hall do not seem to have gotten the message, instead placing their work in the tradition of Cesare Lombroso.

在经历了第二次世界大战的恐怖之后，这些在学术界颇有耳闻的理论被彻底抹黑了。 Hashemi和Hall似乎没有得到信息，而是将他们的工作放到了Cesare Lombroso的传统中。

“This study is triggered by Lombroso’s research,” they uncritically state in the introduction, “which showed that criminals could be identified by their facial structure and emotions.”

他们在引言中毫不客气地指出：“这项研究是由隆布罗索的研究触发的，这表明罪犯可以通过他们的面部结构和情感来识别。”

In fact, Lombroso’s work failed to show these things and has been completely discredited. Stephen Jay Gould, the late evolutionary biologist, dedicated most of a chapter in his classic work The Mismeasure of Man to debunking Lombroso’s theory, and described it with words based on Lombroso’s own twisted thinking:

实际上，隆布罗索(Lombroso)的作品未能展示这些东西，并且已被完全抹黑。 已故的进化生物学家斯蒂芬·杰伊·古尔德(Stephen Jay Gould)在他的经典著作《人的失误》中将大部分章节专门用于揭穿隆布罗索的理论，并用基于隆布罗索自己扭曲思想的词语来描述它：

Criminals are evolutionary throwbacks in our midst. Germs of an ancestral past lie dormant in our heredity. In some unfortunate individuals, the past comes to life again. These people are innately driven to act as a normal ape or savage would, but such behavior is deemed criminal in our civilized society. Fortunately, we may identify born criminals because they bear anatomical signs of their apishness.

罪犯是我们中间的进化退缩。 祖先的细菌潜伏在我们的遗传中。 在某些不幸的人中，过去又重新焕发了生命。 这些人天生就被当作普通的猿人或野蛮人，但在我们的文明社会中，这种行为被认为是犯罪。 幸运的是，我们可以确定天生的罪犯，因为他们带有解剖学上的标志性迹象。

According to a Wired article from 2014:

根据《 连线》杂志2014年的文章 ：

Lombroso took Darwin’s recently published theory of evolution and added a horrifying twist that would reverberate for decades. You’d be hard-pressed to find an upside to his argument that criminals in fact express the physical qualities of our ancestors, bringing them closer to the dispositions of an ape than a human. Or see what good came from the towering whirlwind of racism that accompanied his hypothesis. Or in profiling people with big earlobes, “as in the ancient Egyptians,” as born criminals.

隆布罗索(Lombroso)采纳了达尔文(Darwin)最近发表的进化论，并加入了令人震惊的转折，这种回旋会持续数十年。 您很难找到他的论点的优势，即罪犯实际上表达了我们祖先的身体素质，使他们更接近于猿猴而不是人类。 还是看看伴随着他的假设而来的种族主义高涨旋风带来的好处。 或者，像天生的罪犯一样，对大耳垂的人进行剖析，例如“在古埃及人中”。

These signs of criminality included a number of traits he identified in Africans and Asians, but even identifying missing earlobes and hooked noses in some Europeans as a sign of criminality.

这些犯罪迹象包括他在非洲人和亚洲人中发现的许多特征，但甚至在某些欧洲人中也发现了耳垂和钩鼻的缺失，这是犯罪的迹象。

The very foundation of Hashemi and Hall’s article is rooted in pseudoscience and scientific racism and should be rejected as such.

Hashemi和Hall的文章的根基植根于伪科学和科学种族主义，因此应被拒绝。

One wonders what sort of peer review process would allow so many of these problems to make it into an article in the Journal of Big Data.

一个人想知道什么样的同行评审过程将使如此多的问题成为《大数据杂志》上的一篇文章。

Much more concerning, though, is the future this sort of research has in academia and the harm it can cause to living, breathing human beings dealing with an already biased criminal justice system every day.

然而，更令人担忧的是，这类研究在学术界的未来及其可能对每天处理已经偏颇的刑事司法系统的呼吸人类造成的危害。