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]
