Vsauce!
Kevin here -- and we can predict where a burglary will occur in Beverly Hills within a 1,000
foot zone with 90% confidence a week before it happens by using a complex algorithm that
tracks the frequency and location of similar transgressions to determine the acceleration
and trajectory of escalating antisocial behavior.
And over 1,500 mathematicians have publicly boycotted doing ANY work on it.
Predictive policing was named one of TIME Magazine’s 50 best inventions in 2011 -- which
sounds really impressive until you discover it’s just below the baguette vending machine.
Can algorithmic prediction systems that sound like they’re straight out of a cthonic hellscape
actually prevent future assaults and murders like their numbers claim?
Or are we just using math to bully people with the police and claiming victory?
Ensuring public safety would be pretty easy if you knew that offenses against society
were going to happen before they happened.
But it’s such a fantastic proposition that its consequences and abuses have fueled dystopian
stories from George Orwell, Philip K. Dick, and Isaac Asimov.
We seem to inherently KNOW there’s a problem with inescapable accountability based on potentially
flawed information.
But real world systems for predictive policing have actually been utilized by law enforcement
for over 100 years.
In the early 20th century, sociologist Ernest Burgess proposed the concentric zone model
for identifying and explaining sociological patterns in urban areas, including crime.
His model noted where tension and transitions were likely to occur, like deteriorated housing
in the areas between a city’s central business district and the workers’ neighborhoods
further out.
It culminated in social disorganization theory, which posits that people’s actions result
from their social relationships and their environments more than their own clear, rational
decisions.
Building upon generations of evolving sociological theories we’ve added algorithms crunching
complex data to stop crime before it occurs.
And the results have been…
The Electronic Frontier Foundation defines predictive policing as "the use of mathematical
analytics by law enforcement to identify and deter potential criminal activity."
We’ve got lots of data and increasingly sophisticated analytical tools, so why not
put ‘em to use?
Here’s how we put ‘em to use.
One group of researchers broke down the city of Chicago into small 2-block areas, then
gathered historical data on violent crime and property crime.
By tracking changes in those patterns -- essentially measuring the acceleration of crime types
and their frequencies -- they were able to predict where crime would occur, and they
replicated their results in 7 more cities.
They describe it as tracking “spatiotemporal point processes unfolding in social context.”
Okay.
Chicago does have a well-publicized crime problem, so this sounds like GREAT news…
… until you realize the Chicago PD’s “Strategic Suspect List” of 398,684 names of people
highly-likely to commit gang-related crime was composed of only 16% gang members.
How good are your gang-crime predictions gonna be when 84% of your suspects ARE NOT IN GANGS?
What good is the algorithm if the data make no sense?
What even ARE these tools?
Well.
One of them… kinda works.
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Okay.
Precobs is an abbreviation for “Pre-Crime Observation System,” and it was developed
by the Institute for pattern-based Prediction Technique in Germany.
It identifies crimes that occur as ‘repeats’ -- like burglary.
When a burglary is committed, the next crime that perpetrator commits is… also probably
going to be a burglary or robbery.
If there’s a car theft, there’s likely to be another car theft -- because someone
out there is good at stealing cars and they’re unlikely to switch to robbing banks.
Precobs feeds the data about crimes and minor events that may have been red flags to human
police officers who can then make better decisions about where and when to patrol.
Using Precobs, Zurich police claimed to reduce burglary by 30% in one year.
And then there’s PredPol.
PredPol got its inspiration from the algorithms used to predict earthquake aftershocks, and
they’ve made grand promises about what it can actually do: “PredPol was founded on
the audacious premise that we could help make the practice of policing better in America.
By “better” we mean providing less bias, more transparency, and more accountability.”
We have heard eerily similar messaging in the previous two videos and I’m glad I made
this black box for the last one, because… we’re gonna need it.
PredPol, which is now called Geolitica, brags that it’s used to “help protect roughly
one out of every 30 people in the United States.”
Cory Doctorow compiled a list of some of the cities using PredPol -- and dozens of other
cities have used it in secret.
PredPol claims it can flag a 500ft x 500ft area and visualize its crime history to generate
information for ‘quick deployment’ of law enforcement resources -- and they even
publish the algorithm right on their website!
Uhhh?
They do mention the algorithm's 5 components, but no one knows how it works -- or more importantly,
IF it works.
The LAPD had nearly a DECADE to figure that out and couldn’t.
In 2019, the Los Angeles Police Department ended its use of PredPol for what they said
was budget reasons -- but they also were totally unable to evaluate its effectiveness over
the whole time they used it.
The LAPD combined PredPol with LASER, the Los Angeles Strategic Extraction and Restoration
program, to gather, analyze, and deploy as much data as possible to identify the places
and people involved in crime.
So… did it help?
I don’t know.
Look, here’s the big problem we know exists even without being able to access the inner
workings of these algorithms: You can have very clear data and STILL get the wrong conclusion.
WORLD WAR II AIRPLANES.
In World War II, Abraham Wald of the Statistical Research Group studied where to put armor
on planes to maximize survivability -- and if you’ve already heard this story, the
math is way way more complex than you realize, but Wald’s logical approach is important.
The bullethole density of damaged aircrafts were analyzed after their return home -- and
the areas with the most holes were the fuselage and the wings.
That data suggests those two areas got hit with bullets the most and therefore needed
the reinforced armor, right?
WRONG.
Wald knew that the absence of bullet holes near the returned planes’ engines meant
the engines had probably been shot in the planes that were shot down and never returned
at all -- he factored in survivorship bias, which is concentrating on things that make
it past a selection process and ignoring those that did not in a way that influences data-based
conclusions wrongly.
And that’s the main criticism of systems like PredPol: a certain kind of data makes
it through the process and is reflected in a certain kind of way.
Then it’s used to determine action.
In computer science, the phrase “Garbage In, Garbage Out” describes the problem of
bad or incomplete information yielding an equally bad output -- or worse.
It’s the same with policing.
Crime statistics and related data collection are historically…
not very good.
In 2021, 4 of the largest 15 cities in the United States reported only partial crime
data to the federal government -- and 5 reported NONE AT ALL.
ZERO.
In 3 of the 5 largest states, under 2% of agencies reported their data.
Was all of this just not ready?
Did it even exist?
Who knows?!
But bad data or the absence of critical data completely alters our understanding of a serious
issue.
The Director of the ​​ACLU Criminal Law Reform Project summarizes the issue: “When
you feed a predictive tool contaminated data, it will produce polluted predictions.”
It’s prone to creating destructive feedback loops due to systematic social biases.
Here’s the loop.
Wanna see the loop?
Here’s the loop.
If data goes into a predictive model that over-represents a threat, then identifies
relevant people and places to an outsized degree, which results in a stronger police
presence fueled by a preconceived notion of the presence of crime, which results in increased
police interactions…what do you think happens?
And this is why those 1,500-plus mathematicians and researchers just weren’t comfortable
working on predictive policing projects.
There’s a growing understanding that just like the initial read on the World War II
plane data, these tools might have it all backwards -- instead of predicting crimes,
they’re actually predicting policing.
Remember that amazing promise in the beginning about predicting urban crime with 90% accuracy
within 1,000 feet?
That same paper also revealed enforcement bias.
So even when the algorithm is right – we’re left with trusting human beings to make sound,
fair judgments based on the results.
Which is its own problem.
The Hippocratic Oath is an ancient Greek medical text that makes physicians promise to uphold
ethical standards and is commonly associated with the phrase: “Do no harm.”
The concept is to only prescribe beneficial treatments.
Should our predictive crime methods adhere similarly to the Hippocratic Oath?
Do the injustices caused by them outweigh the benefits?
Can we answer these questions when we can’t even measure the benefits to begin with?
And how much harm do we put up with as we figure it out?
Do we just have to break a few human eggs to make the perfect algorithm omelet?
Do you care if it’s one big experiment on society?
Does that change if it becomes one big experiment on… you?
And as always, thanks for watching.