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  • In 2007, I became the attorney general

  • of the state of New Jersey.

  • Before that, I'd been a criminal prosecutor,

  • first in the Manhattan district attorney's office,

  • and then at the United States Department of Justice.

  • But when I became the attorney general,

  • two things happened that changed the way I see criminal justice.

  • The first is that I asked what I thought

  • were really basic questions.

  • I wanted to understand who we were arresting,

  • who we were charging,

  • and who we were putting in our nation's jails

  • and prisons.

  • I also wanted to understand

  • if we were making decisions

  • in a way that made us safer.

  • And I couldn't get this information out.

  • It turned out that most big criminal justice agencies

  • like my own

  • didn't track the things that matter.

  • So after about a month of being incredibly frustrated,

  • I walked down into a conference room

  • that was filled with detectives

  • and stacks and stacks of case files,

  • and the detectives were sitting there

  • with yellow legal pads taking notes.

  • They were trying to get the information

  • I was looking for

  • by going through case by case

  • for the past five years.

  • And as you can imagine,

  • when we finally got the results, they weren't good.

  • It turned out that we were doing

  • a lot of low-level drug cases

  • on the streets just around the corner

  • from our office in Trenton.

  • The second thing that happened

  • is that I spent the day in the Camden, New Jersey police department.

  • Now, at that time, Camden, New Jersey,

  • was the most dangerous city in America.

  • I ran the Camden Police Department because of that.

  • I spent the day in the police department,

  • and I was taken into a room with senior police officials,

  • all of whom were working hard

  • and trying very hard to reduce crime in Camden.

  • And what I saw in that room,

  • as we talked about how to reduce crime,

  • were a series of officers with a lot of little yellow sticky notes.

  • And they would take a yellow sticky and they would write something on it

  • and they would put it up on a board.

  • And one of them said, "We had a robbery two weeks ago.

  • We have no suspects."

  • And another said, "We had a shooting in this neighborhood last week. We have no suspects."

  • We weren't using data-driven policing.

  • We were essentially trying to fight crime

  • with yellow Post-it notes.

  • Now, both of these things made me realize

  • fundamentally that we were failing.

  • We didn't even know who was in our criminal justice system,

  • we didn't have any data about the things that mattered,

  • and we didn't share data or use analytics

  • or tools to help us make better decisions

  • and to reduce crime.

  • And for the first time, I started to think

  • about how we made decisions.

  • When I was an assistant D.A.,

  • and when I was a federal prosecutor,

  • I looked at the cases in front of me,

  • and I generally made decisions based on my instinct

  • and my experience.

  • When I became attorney general,

  • I could look at the system as a whole,

  • and what surprised me is that I found

  • that that was exactly how we were doing it

  • across the entire system --

  • in police departments, in prosecutors's offices,

  • in courts and in jails.

  • And what I learned very quickly

  • is that we weren't doing a good job.

  • So I wanted to do things differently.

  • I wanted to introduce data and analytics

  • and rigorous statistical analysis

  • into our work.

  • In short, I wanted to moneyball criminal justice.

  • Now, moneyball, as many of you know,

  • is what the Oakland A's did,

  • where they used smart data and statistics

  • to figure out how to pick players

  • that would help them win games,

  • and they went from a system that was based on baseball scouts

  • who used to go out and watch players

  • and use their instinct and experience,

  • the scouts' instincts and experience,

  • to pick players, from one to use

  • smart data and rigorous statistical analysis

  • to figure out how to pick players that would help them win games.

  • It worked for the Oakland A's,

  • and it worked in the state of New Jersey.

  • We took Camden off the top of the list

  • as the most dangerous city in America.

  • We reduced murders there by 41 percent,

  • which actually means 37 lives were saved.

  • And we reduced all crime in the city by 26 percent.

  • We also changed the way we did criminal prosecutions.

  • So we went from doing low-level drug crimes

  • that were outside our building

  • to doing cases of statewide importance,

  • on things like reducing violence with the most violent offenders,

  • prosecuting street gangs,

  • gun and drug trafficking, and political corruption.

  • And all of this matters greatly,

  • because public safety to me

  • is the most important function of government.

  • If we're not safe, we can't be educated,

  • we can't be healthy,

  • we can't do any of the other things we want to do in our lives.

  • And we live in a country today

  • where we face serious criminal justice problems.

  • We have 12 million arrests every single year.

  • The vast majority of those arrests

  • are for low-level crimes, like misdemeanors,

  • 70 to 80 percent.

  • Less than five percent of all arrests

  • are for violent crime.

  • Yet we spend 75 billion,

  • that's b for billion,

  • dollars a year on state and local corrections costs.

  • Right now, today, we have 2.3 million people

  • in our jails and prisons.

  • And we face unbelievable public safety challenges

  • because we have a situation

  • in which two thirds of the people in our jails

  • are there waiting for trial.

  • They haven't yet been convicted of a crime.

  • They're just waiting for their day in court.

  • And 67 percent of people come back.

  • Our recidivism rate is amongst the highest in the world.

  • Almost seven in 10 people who are released

  • from prison will be rearrested

  • in a constant cycle of crime and incarceration.

  • So when I started my job at the Arnold Foundation,

  • I came back to looking at a lot of these questions,

  • and I came back to thinking about how

  • we had used data and analytics to transform

  • the way we did criminal justice in New Jersey.

  • And when I look at the criminal justice system

  • in the United States today,

  • I feel the exact same way that I did

  • about the state of New Jersey when I started there,

  • which is that we absolutely have to do better,

  • and I know that we can do better.

  • So I decided to focus

  • on using data and analytics

  • to help make the most critical decision

  • in public safety,

  • and that decision is the determination

  • of whether, when someone has been arrested,

  • whether they pose a risk to public safety

  • and should be detained,

  • or whether they don't pose a risk to public safety

  • and should be released.

  • Everything that happens in criminal cases

  • comes out of this one decision.

  • It impacts everything.

  • It impacts sentencing.

  • It impacts whether someone gets drug treatment.

  • It impacts crime and violence.

  • And when I talk to judges around the United States,

  • which I do all the time now,

  • they all say the same thing,

  • which is that we put dangerous people in jail,

  • and we let non-dangerous, nonviolent people out.

  • They mean it and they believe it.

  • But when you start to look at the data,

  • which, by the way, the judges don't have,

  • when we start to look at the data,

  • what we find time and time again,

  • is that this isn't the case.

  • We find low-risk offenders,

  • which makes up 50 percent of our entire criminal justice population,

  • we find that they're in jail.

  • Take Leslie Chew, who was a Texas man

  • who stole four blankets on a cold winter night.

  • He was arrested, and he was kept in jail

  • on 3,500 dollars bail,

  • an amount that he could not afford to pay.

  • And he stayed in jail for eight months

  • until his case came up for trial,

  • at a cost to taxpayers of more than 9,000 dollars.

  • And at the other end of the spectrum,

  • we're doing an equally terrible job.

  • The people who we find

  • are the highest-risk offenders,

  • the people who we think have the highest likelihood

  • of committing a new crime if they're released,

  • we see nationally that 50 percent of those people

  • are being released.

  • The reason for this is the way we make decisions.

  • Judges have the best intentions

  • when they make these decisions about risk,

  • but they're making them subjectively.

  • They're like the baseball scouts 20 years ago

  • who were using their instinct and their experience

  • to try to decide what risk someone poses.

  • They're being subjective,

  • and we know what happens with subjective decision making,

  • which is that we are often wrong.

  • What we need in this space

  • are strong data and analytics.

  • What I decided to look for

  • was a strong data and analytic risk assessment tool,

  • something that would let judges actually understand

  • with a scientific and objective way

  • what the risk was that was posed

  • by someone in front of them.

  • I looked all over the country,

  • and I found that between five and 10 percent

  • of all U.S. jurisdictions

  • actually use any type of risk assessment tool,

  • and when I looked at these tools,

  • I quickly realized why.

  • They were unbelievably expensive to administer,

  • they were time-consuming,

  • they were limited to the local jurisdiction

  • in which they'd been created.

  • So basically, they couldn't be scaled

  • or transferred to other places.

  • So I went out and built a phenomenal team

  • of data scientists and researchers

  • and statisticians

  • to build a universal risk assessment tool,

  • so that every single judge in the United States of America

  • can have an objective, scientific measure of risk.

  • In the tool that we've built,

  • what we did was we collected 1.5 million cases

  • from all around the United States,

  • from cities, from counties,

  • from every single state in the country,

  • the federal districts.

  • And with those 1.5 million cases,

  • which is the largest data set on pretrial

  • in the United States today,

  • we were able to basically find that there were

  • 900-plus risk factors that we could look at

  • to try to figure out what mattered most.

  • And we found that there were nine specific things

  • that mattered all across the country

  • and that were the most highly predictive of risk.

  • And so we built a universal risk assessment tool.

  • And it looks like this.

  • As you'll see, we put some information in,

  • but most of it is incredibly simple,

  • it's easy to use,

  • it focuses on things like the defendant's prior convictions,

  • whether they've been sentenced to incarceration,