Does predictive policing make us all safer?

Hi, I’m Ryan Jenkins, a philosophy professor at Cal Poly in San Luis Obispo. writing with the help of Tara Dixit. Law enforcement organizations are often among the first to adopt new technologies to make themselves more effective in the fight against crime. For example, police have been quick to adopt facial recognition technologies to more quickly identify suspects or those with outstanding warrants. But new technologies also raise important ethical questions. Let’s see how. Few people could argue against the goal of these new technologies: of course we want to keep our communities safe, and of course we want to catch dangerous criminals as quickly as we can. But what if we could take this a step further, and try to predict where crime is going to occur before it even happens? For example, police have known for a while that crimes tend to be concentrated in certain areas in a city. For example, you can see there are more murders or burglaries in Brooklyn than on the island of Manhattan. It’s reasonable to expect that more crimes would happen, say, when a crowd of revelers files out of a sports arena after a game. Or that crimes might happen on a street lined with bars right around 2 or 3 AM when the bars close. The technology of so-called “predictive policing” uses these kinds of insights, harnessing artificial intelligence, to forecast not just the probability of a crime being committed in the near future, but also the time, location, maybe even the perpetrator of this expected crime Some types of this AI are based on location data. They might, for example, divide a city into a grid of 500-by-500-foot squares and identify “hot spots” where crimes are expected to occur, based on data like historical crimes, weather, or other urban features, like abandoned buildings, liquor stores, and large parking lots, which can provide attractive opportunities for criminals. Police departments, including the LAPD and NYPD, have used these technologies to encourage police officers to patrol near predicted hot spots. The thought is, and data the show, that police presence in these areas can tamp down crime, at least for a few hours. According to LAPD police officers, having a law enforcement presence in hot spots is proactive and prevents crime from occurring. Other cities around the country are also noticing a difference. After using a version of the technology, burglaries in Santa Cruz declined significantly, and crimes in Atlantic City were reduced drastically. In addition, predictive policing allows police departments to save money and time and optimize patrol resources, with some departments saving tens of millions of dollars in the course of a few years. So: predictive policing makes sense as a strategy, there is evidence that it reduces crime, and it’s likely to save police departments, and ultimately taxpayers, a lot of money. What could be wrong with this technology? The major worry is that predictive policing could exacerbate the already unfair policing of minorities, it could help to justify wrongful arrests, and disproportionately target low-income communities. For one thing, think about the historical data that these systems are trained on. We have good reason to think that a lot of this data reflects the biases of past police behavior. While the law is supposed to treat us all equally, we know that it’s not always enforced equally, and that minorities can be scrutinized or punished more severely than whites. For example, we know that blacks are arrested more often than whites for crimes like drug possession, even though whites and blacks use drugs at about the same rates. If you feed skewed arrest data like this into an AI system, it’s going to “learn” that blacks tend to be more dangerous, which is not true. The predictions generated would recommend that officers spend disproportionate time policing minority neighborhoods, where they’re liable to encounter people committing crimes, generating more disproportionate arrest data, and so on. This creates a harmful “feedback loop.” The result is that police presence in these areas is “ratcheted up,” or increased more and more, until the effects are disproportionately concentrated in minority communities. Of course, these AI systems are never fed data about race directly. That would likely be illegal, and it would surely be unfair. But they are fed other data on the location of crimes or the income level of the surrounding neighborhood, and these data in turn correlate very closely with race. This could make the systems “biased by proxy.” While we think that data about crime might be objective, that’s not really clear either. In fact, a lot of debatable human decisions go into creating data about crime. For example, what should we count as a crime? What crimes should police pursue and investigate most intensely? If police discover someone in the act, do they end up arresting the suspect or letting them off with a warning? Whether a crime “took place” depends on many subjective human priorities, choices and interpretations of events. Imagine, for example, the difference between training an AI system on data about police arrests versus only on data about arrests that actually resulted in jury convictions. Given that the overwhelming majority of cases are settled by plea deals rather than by jury trials, you’d undoubtedly get two very different pictures of the “crime” in a community. So, here is the situation. On the one hand, we have a new technology which is effective at lowering crime and reducing costs, according to its proponents. On the other hand, this technology tends to burden some people more than others. In particular, it more significantly impacts minorities, people with low-income, and others who are already facing disproportionate disadvantages in society. When is it fair to disproportionately burden one part of society if this benefits the rest, say, by deterring crime? Does fairness require abandoning a technology, even if the consequences are that the public is less safe overall? Is that simply the cost of fairness? Or could there be some way to use this technology without allegedly causing disproportionate impacts on minority or low-income communities? Perhaps in the way that data is gathered and examined? Or the way police choose to use the technology? And what space should we reserve for human oversight when using AI could impact people’s rights and freedoms? What do <i>you</i> think?