Inside the Disturbing World of Crime and Artificial Intelligence
By: Olivier Sorgho
Tags: Technology, World, Artificial Intelligence, Crime, Politics

where algorithms we can’t fully understand claim to foresee the future

Intro

A handful of sci-fi writers possess the remarkable ability to almost empirically predict the future, and more often than not, it’s their grim visions which come to haunt us in the present. In “Brave New World”, Huxley foresaw the dangers of genetic engineering before it even became a thing. The legendary writer understood the hypnotising effect of mass consumerism, as well as the perils of humanity’s urges to “program” a society towards perfection, relying on numbers, data, and seemingly rational, utilitarian calculations, on so-called objectivity. On the tyranny of science and numbers.

Orwell placed his lenses over politics and language as systems of controlling thought, but it was Huxley who envisioned the pivotal role technology would play in our futures. Other writers like Phillip K Dick also didn’t fail to appreciate the power of big tech, seemingly predicting where it would take us with surgical precision. Dick wrote Minority Report in the 1950s. The short story — adapted by Spielberg into a movie of the same name — envisioned a future society where a system known as PreCrime predicted criminal activity, thus preventing it from happening.

Fast forward to 2020… where predictive policing technologies geared towards stopping crime are alive and well; while artificial intelligence finds its foothold in facial recognition software aiming to spot a criminal by scanning his or her face. As more powerful jurisdictions implement these innovations without fully understanding them, a cautionary tale is in order.

Stopping Crime: Predictive Policing

Across the United States, police departments continue experimenting with various AI-led programs known by the umbrella term “predictive policing”. Some attempt to predict the scene and time of a crime. Others identify potential criminals or even victims. These algorithms sort through and analyse heaps of historical crime data such as the time, nature, location where past crimes occurred, offering indications to police on things like where vehicles should be patrolling, in what areas; if and when there might be a shooting; where the next car break-in might occur; who may might be the next victim etc.

The company PredPol has made a name for itself in the industry, attributing its algorithm with the ability to improve crime detection in cities by up to 15%. Originally designed to forecast the aftershocks of earthquakes, the PredPol algorithm attempts to predict the place and time of crimes within 12 hours, with data updated on a daily basis. According to its own data, in 2019, 60 police departments across the US used PredPol’s services. The LAPD, an old and loyal customer, was an early pioneer in implementing such technology back in 2008. The LASER program identified areas with high likelihood of gun violence, and pinpointed the individuals likely to perpetrate them.

“So the LASER program was designed on the metaphor that they were going to, like laser surgery, remove the tumours, the bad actors from the community” Andrew Ferguson, Washington University law professor and expert on predictive policing

LASER was shut down in 2019 after internal audits revealed serious problems and inconsistencies in the methodology of flagging individuals deemed by the system to be potential criminals. Around 50% of individuals deemed “chronic offenders” had either none or one arrest for violent crime, and around 10% had no meaningful interaction with police.

Teenager Conor Deleire learned about the system’s deficiencies the hard way. Sitting alone in his car in Manchester, New Hampshire, simply waiting for a friend, the teenager suddenly found himself surrounded by police, soon to be later zapped by a stun gun, blasted with pepper spray, and eventually arrested. The area where he had parked his car was flagged as a ‘predictive hot stop’ — the likely scene of a crime — and just being there was enough cause for suspicion. Deleire turned out to be innocent. Well, almost. Resisting arrest ended up being his only charge — raising questions about the extent to which the system was predicting criminals, or creating them.

The algorithms operate on skewed and biased data, to the disadvantage of minorities especially. Take routine stops by law enforcement. The US Justice department of Justice data points to the fact that black men are five times as likely to be routinely stopped without cause as whites. The consequence of such data being fed to the algorithms is the exacerbation of existing inequalities.

Does Predictive Policing Work?

Ask many law enforcement officials whether predictive policing works, and they’d likely answer with a resounding “yes”. Manchester police credit its own program (which led to the Connor Deleire situation) with reducing property crime by 25%. How they directly attribute this drop to the program itself remains unclear. Nonetheless, the US isn’t alone in implement AI in its battle with crime. Its police departments which have abandoned the program are also met with their counterparts overseas.

In Kent, the United Kingdom, PredPol’s software appeared more accurate than human analysts in predicting the locations of street crime. Over 8.5% of all street crime happened in “pink” areas pointed to by the software. Human police analysts lagged behind at 5%. Nonetheless, proving just how the software helped reduce crime proved more challenging, contributing to the Kent police force scrapping the program in 2018.

“PredPol had a good record of predicting where crimes were likely to take place,..What is more challenging is to show that we have been able to reduce crime with that information.” John Phillips, Superintendent of Kent to the Financial Times

Transparency

Despite optimism in many police departments surrounding the program’s effectiveness, other studies have proved far less optimistic for predictive policing proponents, and their hopes of justifying its presence in the criminal justice system. This 2013 report by the RAND corporation found predictive policing to be ineffective in reducing the homicide rate in Chicago through its attempt to identify would-be shooters.

Overall, studies have generated ambiguous results. A study by the Max Planck Institute for Foreign and International Criminal Law concluded that we cannot definitively state whether predictive policing works.

Not only do we not know if predictive pbut also how predictive policing works: what exact data the police feed to the software, or how it’s obtained. The lack of transparency of information on the part of police departments raises questions on whether the technology is applied in legal and ethical manners.

In 2019, digital rights group Upturn sued the NYPD in an attempt to force them to reveal records over “mobile device forensic tools” which gave law enforcement officials access to mobile phone metadata, including location and allegedly some forms of encrypted communication. The NYPD in general has developed a sort of notoriety for lawsuits by civil rights groups and individuals demanding more accountability. The department, like other agencies, doesn’t make clear what data exactly is fed to its predictive policing software and often resists journalists’ demands for accountability, provoking courtroom battles.

This raises serious questions about how exactly the software is being used, to what ends, and what the police is hiding.

Spotting The Criminal

If predictive policing sounds like stuff from sci-fi stories, assertions about AI’s ability to link criminality with facial features should send shivers down our spines. The old practice of judgement on someone’s character based on their appearance is known as physiognomy. The advent of artificial intelligence has breathed new life into this dangerous practice, placing it under the dome of apparently ‘objective’ numbers. After all, who can argue with data?

In 2016, a study conducted by Chinese scientists Xiaolin Wu and Xi Zhang from Shanghai Jiao Tong University produced some pretty unsettling results. Their deep-learning AI could supposedly determine whether a person had a criminal record or not, simply by scanning ID images of their faces. Facial features appeared to be a prediction of criminality: The authors gave somewhat detailed examples of what the data showed: individuals with criminal records had on average a 23% larger curvature on the upper lip, as well as a smaller distance between inner corners of their eyes. The accuracy with which the software could determine whether someone was a criminal was 89%.

This research has reignited an old ,controversial and discredited claim in criminology. In the 18th century, Italian criminologist Cesare Lombroso argued that criminals had distinct physical features, in some cases similar to apes: their ears were of unusual size, they had asymmetrical faces, and long arms.

It doesn’t take a genius to link this sort of language with the pseudo science of Nazi propaganda in the 1930s as well as racist discourses today. Such language — much like human conclusions derived from technology and data — operates on the illusion of objectivity.

The Illusion of Objectivity

Research shows that humans are themselves pretty handy in spotting a criminal by looking at his/her face. Humans make that distinction pretty well according to a study by Cornell University in 2011. So if humans can do it, then of course machines can, too, just better. And that is actually the bottom line: Machines can be biased in the same way as humans, especially when they operate on human-derived data. If we feed AI with data which itself is “dirty”, it will reproduce our biases. The attraction of machine learning and the use of AI is often based on the (wrong) assumption that AI is impartial. Except it’s not. Let’s return to our example of facial features and criminality — are the inputs and outputs objective or prone to significant bias?

What is a ‘criminal’ anyway?

The output here is the outcome, or the verdict if you like — criminal, or not criminal. The Chinese system of justice makes that call. Can we trust the Chinese government’s (not exactly free from abuses of human rights, and questions about rule of law) judgement on who exactly is a criminal? Does an “objective” definition of a criminal actually exist? By my understanding, the word criminal is often ambiguous, and varies from country to country, changing through time. Even the authors of the study admit that many of the ‘criminals’ present in the database of empirical data (ID scans) committed minor, non-violent offences.

Not to mention methodological issues evident in the inputs, supposedly ‘controlled-for’ ID photos obtained by the researchers. Sample images of “non-criminal” types in the study reveal them all to be wearing white-collared shirts, while none of the “criminals” are.

Also, as y Arcas observes:

“the authors cannot reliably infer that their web-mined government ID images are all of “non-criminals”; on the contrary, if we presume that they are a good random sample of the general population, statistically some fraction of them will also have engaged in criminal activity.”

How is the AI picking up on these data flaws to reach its conclusion? We’re not sure.

What about structural causes of criminality?

So what do premature conclusions about face and criminality infer?

That facial features are an innate characteristic (ignoring the environmental and developmental impacts), and that no structural biases exist within and between them. That criminality is innate, and can be pinpointed to certain types of people. But take poverty as one such structural ‘bias’. Many studies intuitively indicate that economic upbringing and social class (in other words background) leave lasting marks on people’s faces. Would it not be logical to argue that the real predictors of criminality are largely structural? socioeconomic background, upbringing, income, education etc? These are in themselves obviously tied to demographics like race — whose people share certain features — and might be disproportionately represented in the criminal justice system. Are they naturally, innately inclined towards committing more crime as evidenced by coded, written features on their face? No. Do existing systems — like predictive policing — exacerbate their disadvantaged position by feeding into the cycle of discrimination? Yes.

Would it not make more sense to remove incentives for crime, rather than claim that some are naturally predisposed to commit them? Rather than using data to predict the area a crime might take place, why not invest in poor communities? Predictive policing and AI exemplifies a misguided societal approach which extends beyond crime, where treatments are designed to cure the symptoms, while ignoring the causes.

Can artificial intelligence replicate systemic biases? Of course it can. You may have heard of an algorithm called PULSE capable of turning a blurry, highly pixelated image into clear, high-resolution ones? It’s the same algorithm which systematically turned images of black politicians like Barack Obama into white males. How did this happen? We’re not exactly sure but the answer may lie in the predominantly white datasets which the machine worked on, or as claimed by the software’s creators.

“It does appear that PULSE is producing white faces much more frequently than faces of people of colour…This bias is likely inherited from the dataset StyleGAN was trained on […] though there could be other factors that we are unaware of.”

The unaware part is key but also problematic, because:

“Machine learning does not distinguish between correlations that are causally meaningful and ones that are incidental.”

The Inexplicability of deep-learning A.I processes

We often don’t understand how or why algorithms reach a certain outcome or conclusion, especially given the nature of cutting-edge AI technology known as deep learning. This is the process whereby an algorithm can “itself learn what to look for”, without human instruction setting parameters for what data to uncover. The human just gives the A.I a goal, and the A.I itself determines how to reach that goal: for instance: “match the facial features with criminality”. Large chunks of how it does so remains impossible to disentangle due to the complexity of the neural network on which the algorithm is based. The algorithmic mysteries become particularly problematic when the ‘hows’ and ‘whys’ of its outcomes pertain to direct judgement’s about a person’s character. Criminality is such a judgement.

In some cases, the inexplicability of AI is set aside for more important issues. Take medicine. An algorithm known as Deep Patient which analyses tons of medical records of individuals patients proved very strong at predicting the onset disease, often those which humans least understand, like schizophrenia or liver cancer. But how exactly does the Deep Patient make that prediction? What are the intricacies of its calculations and pattern associations? We’re not sure. Of course, in this relatively non controversial case we shrug off this lack of knowledge in honour of something we deem more important: saving lives.

But in an era where artificial intelligence already helps determine who makes parole and at what cost, who gets hired for a job while hugely powerful institutions like banks, the military, corporations and employers, increasingly rely on them , not fully understanding the processes and mechanics of how these decisions are reached — while ignoring the system’s potential to replicate human biases — becomes a problem in need of being seriously addressed.

Policy and conclusion

A start-up known as Faception claims its algorithm can analyse your picture and tell your personality. Meanwhile, news spreads that a “homeland security agency” is cooperating with the same company to pinpoint potential terrorists, which the start-up claims capable of identifying with 80% accuracy through a face scan. The same accuracy applies to the software’s alleged ability to identify a paedophile. How it does so is not clear.

Indeed, a rift is growing between the growing complexity of deep-learning AI neural networks, and our ability to understand them, thus keeping them under control. Which is why the European Union is pushing for making AI outcomes explicable as a key tenet of its guidelines for future technological engagement. In the body’s own words:

“Whenever an AI system has a significant impact on people’s lives, it should be possible to demand a suitable explanation of the AI system’s decision-making process. Such explanation should be timely and adapted to the expertise of the stakeholder concerned (e.g. layperson, regulator or researcher). High-level expert group on artificial intelligence, European Commission

The European Union’s strategy might be the sensible approach, but may also prove difficult to implement in practice, given the growing importance of deep-learning algorithms across various industries, and the implicit temptations to implement them and make life easier. Approaching AI with caution will require a strong political will to balance between its short and long term strategic benefits, and the threats it poses — to privacy, civil rights, jobs and economic stability to name a few. The key is making sure the technology doesn’t make uncontrolled leaps beyond our powers to keep it in check, so that it can do what technology should do: improve our lives, while striving to make our societies fairer and more sustainable.