ROLAND FRYER: Good afternoon.
What I'm most proud of actually is that at my advanced age,
I still spend two hours a morning working out with the Harvard football
team, so that's the most proud I am.
How many of you actually want to change the world?
It's OK, you can raise your hands.
You don't have to be like this.
OK, how are you going to change the world?
You.
Well, but that was rude, you're right.
I'm Roland, how are you going to change the world?
AUDIENCE: I'm Hassad [INAUDIBLE].
I want to change the world by being an attorney.
ROLAND FRYER: Awesome.
You guys went simultaneously on the hand, so I'll go both.
What about you?
AUDIENCE: I want to be an astronaut and learn more about space.
ROLAND FRYER: Astronaut and learn more about space.
Sorry, the lights are super in my face, so I can't really see upstairs.
But anyone upstairs want to change the world?
There we go.
AUDIENCE: I want to build robots that save people in natural disasters.
ROLAND FRYER: Wow, that's cool.
Build robots-- that's amazing.
A million years ago when I was your age, I also wanted to change the world.
I wanted to go to the NFL.
Let me rephrase that, I wanted to change my world.
Maybe that could have some spill-overs to other people.
And I was interested two things.
I wanted to wear t-shirts to work.
I thought that would be cool.
And I wanted to have a room full of shoes.
I don't know what the second one is about.
And to be clear, I've accomplished both of them,
but I really wanted a room full of shoes.
Now, what I do for a living is that I raise lots of private resources
to run programs in communities across the United States.
And I'm an economist, but I'm interested in racial inequality.
That's like a little bit of a weird thing.
That's like an oxymoron like jumbo shrimp or something.
That's weird, right?
Economists-- we're really careful and analytical,
but caring about other people's not typically our thing.
But what I do--
I love the economic method.
I really love-- like when I was in graduate school,
I got totally fascinated with the tools like the separating hyperplane theorem.
I was just blown away with the beautiful mathematics of economics,
and I thought it was being used to study problems
that were not as interesting to me like the optimal cake eating problem.
You see my size.
I'm 6' 2", 215--
I eat the whole cake.
But I thought maybe these things can be used on problems that I deeply
care about like why all the kids in my neighborhood-- not a lot of them
went to college even though I thought they had great talent, why
many of my friends ended up incarcerated at young ages
even though I thought there was a lot of potential.
And so what I do is I go around and we raise a lot of dollars.
We've probably raised a couple hundred million dollars at this point
over the last 10 years to run experiments.
That's the economist in me, right?
I don't tie my shoes without a treatment and control group.
But to run experiments so that we can really understand what works
and what doesn't with the idea that we want to lower inequality in America
by expanding opportunity.
There's a thing here somewhere, isn't there.
So I'm going to show you a project I've been working
on for the last year and a half.
It hasn't made me the most popular, and that's
what happens sometimes when you're dealing with big data.
Sometimes, you get answers that aren't what you expected when you started.
Like many of you, I was really and I am really
concerned with what's going on between race and police
in communities across America.
We had started with--
I should say it started with.
It became more salient for us with Michael Brown in Ferguson,
Eric Garner in New York City, Laquan McDonald in Chicago, I could go on.
And I was frustrated by this, to be honest with you.
I was tired of looking at my TV at night and seeing
the protests and silly debates.
They weren't silly in their content, they were silly in their execution
about what was actually going on.
And as you might imagine, I'm kind of too weak to be protesting.
I mean, it's like, hot out there, so I decided maybe what I should do--
my skin's too fair for that.
But what I can do is go into our lab and roll up our sleeves,
and see if we can actually make progress.
OK, and so the key thing was like how do you make progress on something
that is so deeply emotional?
People on both sides of this issue are deeply emotional about it
and rightly so.
So here's what I did.
I'll just tell you the full story.
I called up Bud Riley, who turns out to be the police chief of the Harvard
University Police.
And I said, hey, bud.
I'm interested in race and police, but I've got to be honest with you.
I spent my youth running from dudes like you.
So I don't really like you all that well, but I'd like to learn.
And Bud said, I got just the thing for you.
OK, true story.
So Bud says, why don't you come over to my office tomorrow at 3 o'clock,
and I've got something for you.
I said, OK, Bud, but you know my grandmother always taught me not
to just go hang out with police.
This seems like a setup.
So I brought one of my white students with me,
and I was like, come on with me-- you can film this.
OK, that's actually not true.
That's not true.
That part's not true.
If I had thought about it first, it would have been true,
but it's not true.
I risked it.
I went and had coffee with Bud in the afternoon,
and Bud took me to the simulator.
So I went into the police simulator where they actually
train police officers because I thought this was easy.
Because I got this fancy European wife who's
like, why don't they just shoot him in the leg, and I'm like, well,
I don't know.
Why do they shoot him in the leg?
So I go over and he puts on the utility belt like I'm going deep in here.
Like, now, I'm in the sociology.
I got the utility belt on, I've got my pistol on, I've got my nightlight
or whatever police have, I got that stuff on,
and I'm in this kind of simulator where bad guys are jumping out.
And you've got to decide who to shoot.
OK, so my first interaction--
I only have 10 minutes, so I can't bore you with all of them.
Bottom line is I'm the worst police officer ever imaginable.
But the first guy comes up-- true story--
and it's a mid-forties, maybe 50, white gentleman, long hair.
And I'm going to a suburban pool party because there had been complaints.
So I get there and this person is clearly
inebriated and is a little handsy.
You know, he's, why are you bothering me?
What are you doing?
And so I get a little nervous even though I'm in the simulator.
And the simulator has got some artificial intelligence parts to it,
so as I talk him down using de-escalation techniques,
this image on the screen is supposed to relax.
So I say to him, sir, I'm just doing my patrols here.
You've got to relax.
He says, well, you're always bothering me.
I said, sir--
I'm talking to the screen now--
you've got to relax.
And he put his hands in his pocket, and I decide that's enough for me.
So I pull my gun on the guy.
Bud turns on the lights, Professor, what are you doing?
I was like, isn't that what you all do?
Could have fooled me.
And so we go through this training and I have to say in all seriousness,
it was--
not just the training, but this whole experience
I think I learned it was the most important lessons
I learned in life since my grandmother taught me how to read.
It turns out, police have a really tough job.
And so my next thing was I learned how to shoot police issued weapons
just because I wanted to understand, and then actually embedded myself
in the Camden Police Department.
Now, I know the police chief here in Boston,
and I highly recommend anyone do a ride along with police just
to understand their side of the story.
I do not recommend riding along embedding yourself
in Camden, New Jersey.
OK, it was a crazy experience.
We were responding to 911 calls.
We were responding to shots fired.
Now all of us thinkers, if there are some shots over here,
our way to respond is to go over here, but it turns out,
when you're in the police car and they start shooting,
you've got to go in there.
And it was an unbelievable experience.
And in part because I've got to say, I'm not proud of it,
but I would say it took five to six hours
to get pretty desensitized because you're always seeing people in crisis.
And that's not a part of policing that I ever thought about.
You show up to a Wendy's and someone has threatened the cashier,
and you show up three hours late because the Wendy's threatening thing
was way down on your list of things to show up for.
And you get there and you get yelled at for 30 minutes,
and then you end up saying something nonsensical like, if they come back
and threaten again, call us.
Give us a three hour window.
It was incredible, and so I did all this because I wanted to do a few things.
One-- I'm going to show some data as I go--
I wanted to learn how to talk cop.
That was important.
I wanted to build trust with police departments,
and most importantly for an economist, I needed some data.
And so what we've done is we have collected now
in places like New York City, 6 million data observations over the last 15
years on use of force that ranges anywhere
from policemen putting their hands on you to hitting you with a baton.
We've also worked with lots of cities--
Houston, Arlington, Austin, most counties in Florida, Boston, Camden--
to collect data on officer-involved shootings because it's not enough,
and you guys know this from high school statistics.
It's not enough to just look at numbers of people who are being shot.
That's also awful, but that's not enough statistically.
What you really want to understand is in a similar situation,
does race impact the probability of a police officer pulling the trigger?
That's a very different question.
In that question, the burden of the data is strong because you need
to understand all of the police shootings that could have happened
but didn't.
That's far more complicated.
In fact, just in one city alone, we spent 50 minutes per observation,
hand-typing them in the police department
because they didn't trust us to leave with the data.
We had to pay a sergeant $50 an hour to watch us type in the data.
OK, and we typed in roughly 2,000 observations.
It took us nearly a year.
Here's what we found.
This is the odds ratio for blacks versus whites, so anything over one
shows a racial difference.
On the X-axis here, I have the amount of force used.
Yeah, so one is hands, seven is baton.
This is the same for Hispanics.
On the Y-axis is the odds ratio.
Anything over one means that Hispanics are
more likely to have that use of force, and on the X-axis, it's the same.
These are uses of force.
OK, now this is important because a couple of reasons.
One, we usually don't have data on uses of force.
Particularly like, do you put your hands on someone?
We even have data that I can't show you today because of time
where I know just if the police officer shouted at you.
OK, and so what you find here is something pretty interesting.
When it comes to any force being used, blacks
are 53% more likely to have any force used
on them in any interaction with police.
Now, this is reported by the police.
So maybe this is an underestimate, maybe it's not,
but it's reported by the police.
This is not people complaining on a survey.
This is administrative data from the police.
As the use of force increases, the racial difference
remains pretty constant at about 25%.
Here's a pretty interesting fact, even when
you look at individuals who are perfectly compliant as reported
by the police, perfectly compliant--
not arrested, they don't have contraband,
there is nothing in the record to show that any ill was done--
the racial differences in whether or not force was used in that interaction
is roughly 24%.
So there are large racial differences between Hispanics and African-Americans
in the US on lower level uses of force that I cannot explain with the data,
and many, many police departments as I've talked to them about this over
the last few months agree with this.
They understand.
Some police departments-- sometimes when you get into more rural areas to be
very blunt about it--
they don't want to debate with these data, but the vast majority of them
understand that there may be an issue on the use of force.
What's interesting, however, is that when you actually
look at police shootings, we find no racial differences.
We find racial differences in the numbers.
More African-Americans are shot by police than whites,
but not once you actually take more context into account.
OK, so this is where we went to places like Houston and others
and collected very, very detailed data on racial differences
in officer-involved shootings.
Now, here's a very important caveat, we don't have every city.
We don't have Chicago.
I'd love Chicago.
We don't have Chicago.
They won't give it to me.
I've asked many times.
After we released this paper, myself and a couple of the folks
who started the Black Lives Matter movement
met with President Obama for five hours in the White House.
Five hours and after it was over--
and that's a long time to meet with the president, and frankly I had to pee.
And what do you do?
Like, Mr. President-- this was one of the big issues in that meeting.
I was sitting there like, Mr. President, that's really interesting.
Finally, I-- this is true, ask Loretta Lynch.
I have a little handwritten note that I wrote to Loretta Lynch.
Like, this is the first interaction you have with her,
you want to be really good and bold and like,
this is the thing my grandmother prepared me for.
And my little notes says, where do I pee?
Anyway, so we met and we debated this a lot, and I asked him--
I said, Mr. President, is there anything the White
House can do to further this research?
I said, you can get me data from Chicago.
He said, is there anything else the White House can do?
So we don't see it.
We don't see it whether it's on the shootings,
so we look at similar situations in the data and calculate the times where
shooting could have taken place but didn't.
We don't see it there.
Houston has a really interesting thing because on the strong arm in Houston,
there is a pistol.
On the weak arm, there's a Taser, so in economics
lingo, that's a discrete choice problem.
You either shoot or taser.
We actually don't see any racial differences on Tasers.
We don't see any racial differences on any city in which we have data.
Again, with the caveat being, they gave us the data.
Now.
Why is this important?
It's important because you've heard several times,
you have great choices, awesome.
So do a lot of us, but the key thing that's awesome about being here--
I've been here for 15 years, and I will always
stay here as long as ideas are my only constraint.
And that's what's so wonderful about this place.
Ideas truly are your only constraint, and so--
and data.
Ideas are really your only constraint, and I really
believe that this is the future for understanding racial inequality
in America and around the world.
It's using the passion that fueled our parents,
and our grandparents, and our great grandparents,
but using the tools of computer science, and economics,
and artificial intelligence, and those disciplines
who have been set silent when it comes to racial inequality in America
to truly solve these problems.
We actually need you.
Your generation thinks about race differently than mine,
and we need your insights and your mental capabilities
to finally put this old problem to bed through data, through analysis,
and through objective thinking.
Thanks, enjoy your visit.
[APPLAUSE]