Legalizing marijuana makes sense for a lot of reasons, but there’s one valuable thing we’ll lose when police stop arresting people for smoking pot: A sense of just how misleading our crime data are.

Data on arrests and reported crime play a big role in public policy and law enforcement. Politicians employ them to gauge their success in making neighborhoods and the entire country safe. Police departments use them to determine where to deploy more officers to look for more crime. They are fed into recidivism-risk algorithms, which help judges and parole boards make decisions on sentencing and release.

This is troubling, because such data can be a terrible proxy for actual crime. Consider arrests: Only two-thirds of murders result in arrests, which means that homicide data are missing at least a third of actual incidents. And murders are unusual in that we typically have the body, so we know a crime actually occurred. That’s not the case with assaults, rapes, thefts or illegal gun possession. There’s no reason to think that the majority of these crimes lead to arrests, or that all arrests are related to actual crimes.

Reports aren’t much better. People decide whether to report in a cultural context. For example, they’re more likely to do so if they trust the police, and the level of trust can vary sharply over time. A year after Donald Trump was elected president, the number of reported rapes among the Latino population of Houston declined by 40 percent, a strong indication that people became afraid to report the crimes. Police often don’t take rape victims’ reports seriously, a problem that is probably even worse for male victims.

So how can we get a better understanding of the underlying rate of crime? Surveys typically don’t help: People who get away with committing serious offenses aren’t likely to admit it, even if they’re guaranteed anonymity. The one notable exception is marijuana use, which -- though still illegal in most places -- is mild and socially acceptable enough that people are willing to tell the truth. Hence, if we compare the reported rate of marijuana use to the arrest data, we can gain some insight into how useful the latter really is.

The picture isn’t pretty. The latest government surveys, for example, suggest that black and white Americans use marijuana at about the same rate. Yet blacks get arrested about four times more often than whites -- and 15 times more often in Manhattan, according to a recent New York Times analysis. This means that all the policies, policing strategies and algorithms that use the arrest data will unfairly target blacks for closer monitoring and harsher sentencing, perpetuating the ills they are supposed to address.

Given the ample evidence of extreme bias in marijuana arrests, there’s no reason to think that the situation is any better in other areas of crime. Indeed, from a statistical standpoint, we should assume there is a bias in all categories. We just can’t know how extreme it is, because we’re missing data, and we don’t know how much data we’re missing.

Worse, when marijuana is legalized, we’ll lose our only indicator of how off-base the available data really are. Overall, decriminalization is probably a good idea, considering how much devastation the policing has caused in black and brown communities. But it will eliminate our most reliable barometer of police racial bias. When the arrests stop, we’ll stop seeing the disparity, but that doesn’t mean bias in other police practices will suddenly end.

What to do? It won’t be easy, but there are some ways we can work toward improving crime data in the longer term. If police strive to increase public trust and improve follow through, people will be more likely to report crimes, making the data more reliable. This would also require more consistent policing across different neighborhoods, which means treating black youths in Harlem the same way one treats middle-aged white women on the Upper West Side. The Center for Policing Equity, for example, is helping police departments identify and challenge their own biased practices.

In the meantime, it’s crucial to recognize how bad our crime data are, lest we perpetuate the biases they reflect.


Cathy O’Neil
Cathy O’Neil is a mathematician who has worked as a professor, hedge-fund analyst and data scientist. -- Ed.

(Bloomberg)