How racial bias works -- and how to disrupt it
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0:01 - 0:02Some years ago,
-
0:02 - 0:07I was on an airplane with my son
who was just five years old at the time. -
0:08 - 0:13My son was so excited
about being on this airplane with Mommy. -
0:13 - 0:16He's looking all around
and he's checking things out -
0:16 - 0:18and he's checking people out.
-
0:18 - 0:20And he sees this man, and he says,
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0:20 - 0:23"Hey! That guy looks like Daddy!"
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0:24 - 0:26And I look at the man,
-
0:26 - 0:30and he didn't look anything
at all like my husband, -
0:30 - 0:31nothing at all.
-
0:31 - 0:34And so then I start
looking around on the plane, -
0:34 - 0:40and I notice this man was
the only black guy on the plane. -
0:41 - 0:42And I thought,
-
0:42 - 0:44"Alright.
-
0:44 - 0:47I'm going to have to have
a little talk with my son -
0:47 - 0:50about how not all
black people look alike." -
0:50 - 0:54My son, he lifts his head up,
and he says to me, -
0:56 - 0:59"I hope he doesn't rob the plane."
-
0:59 - 1:02And I said, "What? What did you say?"
-
1:02 - 1:05And he says, "Well, I hope that man
doesn't rob the plane." -
1:07 - 1:10And I said, "Well, why would you say that?
-
1:10 - 1:13You know Daddy wouldn't rob a plane."
-
1:13 - 1:15And he says, "Yeah, yeah,
yeah, well, I know." -
1:16 - 1:18And I said, "Well,
why would you say that?" -
1:20 - 1:23And he looked at me
with this really sad face, -
1:24 - 1:25and he says,
-
1:27 - 1:29"I don't know why I said that.
-
1:31 - 1:33I don't know why I was thinking that."
-
1:34 - 1:37We are living with such severe
racial stratification -
1:37 - 1:42that even a five-year-old can tell us
what's supposed to happen next, -
1:44 - 1:46even with no evildoer,
-
1:46 - 1:49even with no explicit hatred.
-
1:50 - 1:54This association between
blackness and crime -
1:54 - 1:58made its way into the mind
of my five-year-old. -
2:00 - 2:03It makes its way into all of our children,
-
2:04 - 2:06into all of us.
-
2:07 - 2:10Our minds are shaped by
the racial disparities -
2:10 - 2:12we see out in the world
-
2:13 - 2:18and the narratives that help us
to make sense of the disparities we see: -
2:20 - 2:22"Those people are criminal."
-
2:22 - 2:24"Those people are violent."
-
2:24 - 2:27"Those people are to be feared."
-
2:28 - 2:31When my research team
brought people into our lab -
2:31 - 2:33and exposed them to faces,
-
2:33 - 2:40we found that exposure to black faces
led them to see blurry images of guns -
2:40 - 2:44with greater clarity and speed.
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2:44 - 2:47Bias cannot only control what we see,
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2:47 - 2:49but where we look.
-
2:49 - 2:52We found that prompting people
to think of violent crime -
2:52 - 2:56can lead them to direct their eyes
onto a black face -
2:56 - 2:59and away from a white face.
-
2:59 - 3:02Prompting police officers
to think of capturing and shooting -
3:02 - 3:04and arresting
-
3:04 - 3:08leads their eyes to settle
on black faces, too. -
3:08 - 3:13Bias can infect every aspect
of our criminal justice system. -
3:13 - 3:16In a large data set
of death-eligible defendants, -
3:16 - 3:20we found that looking more black
more than double their chances -
3:20 - 3:22of receiving a death sentence --
-
3:23 - 3:26at least when their victims were white.
-
3:26 - 3:27This effect is significant,
-
3:27 - 3:31even though we controlled
for the severity of the crime -
3:31 - 3:33and the defendant's attractiveness.
-
3:33 - 3:36And no matter what we controlled for,
-
3:36 - 3:39we found that black
people were punished -
3:39 - 3:43in proportion to the blackness
of their physical features: -
3:43 - 3:45the more black,
-
3:45 - 3:47the more death-worthy.
-
3:47 - 3:51Bias can also influence
how teachers discipline students. -
3:52 - 3:56My colleagues and I have found
that teachers express a desire -
3:56 - 4:00to discipline a black
middle school student more harshly -
4:00 - 4:01than a white student
-
4:01 - 4:04for the same repeated infractions.
-
4:04 - 4:05In a recent study,
-
4:05 - 4:09we're finding that teachers
treat black students as a group -
4:09 - 4:12but white students as individuals.
-
4:12 - 4:16If, for example,
one black student misbehaves -
4:16 - 4:21and then a different black student
misbehaves a few days later, -
4:21 - 4:24the teacher responds
to that second black student -
4:24 - 4:26as if he had misbehaved twice.
-
4:27 - 4:30It's as though the sins of one child
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4:30 - 4:32get piled onto the other.
-
4:32 - 4:35We create categories
to make sense of the world, -
4:35 - 4:40to assert some control and coherence
-
4:40 - 4:44to the stimuli that we're constantly
being bombarded with. -
4:44 - 4:48Categorization and the bias that it seeds
-
4:48 - 4:53allow our brains to make judgments
more quickly and efficiently, -
4:53 - 4:56and we do this by instinctively
relying on patterns -
4:56 - 4:58that seem predictable.
-
4:58 - 5:04Yet, just as the categories we create
allow us to make quick decisions, -
5:04 - 5:07they also reinforce bias.
-
5:07 - 5:10So the very things that help us
to see the world -
5:11 - 5:13also can blind us to it.
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5:14 - 5:16They render our choices effortless,
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5:16 - 5:18friction-free.
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5:19 - 5:21Yet they exact a heavy toll.
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5:22 - 5:24So what can we do?
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5:25 - 5:27We are all vulnerable to bias,
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5:27 - 5:30but we don't act on bias all the time.
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5:30 - 5:33There are certain conditions
that can bring bias alive -
5:33 - 5:36and other conditions that can muffle it.
-
5:36 - 5:38Let me give you an example.
-
5:39 - 5:43Many people are familiar
with the tech company Nextdoor. -
5:44 - 5:51So, their whole purpose is to create
stronger, healthier, safer neighborhoods. -
5:51 - 5:54And so they offer this online space
-
5:54 - 5:58where neighbors can gather
and share information. -
5:58 - 6:02Yet, Nextdoor soon found
that they had a problem -
6:02 - 6:03with racial profiling.
-
6:04 - 6:06In the typical case,
-
6:06 - 6:08people would look outside their window
-
6:08 - 6:12and see a black man
in their otherwise white neighborhood -
6:12 - 6:17and make the snap judgment
that he was up to no good, -
6:17 - 6:21even when there was no evidence
of criminal wrongdoing. -
6:21 - 6:24In many ways, how we behave online
-
6:24 - 6:27is a reflection of how
we behave in the world. -
6:27 - 6:31But what we don't want to do
is create an easy-to-use system -
6:31 - 6:35that can amplify bias
and deepen racial disparities, -
6:36 - 6:38rather than dismantling them.
-
6:39 - 6:42So the cofounder of Nextdoor
reached out to me and to others -
6:42 - 6:44to try to figure out what to do.
-
6:44 - 6:48And they realized that
to curb racial profiling on the platform, -
6:48 - 6:50they were going to have to add friction;
-
6:50 - 6:53that is, they were going
to have to slow people down. -
6:53 - 6:55So Nextdoor had a choice to make,
-
6:55 - 6:58and against every impulse,
-
6:58 - 7:00they decided to add friction.
-
7:00 - 7:04And they did this by adding
a simple checklist. -
7:04 - 7:06There were three items on it.
-
7:06 - 7:09First, they asked users to pause
-
7:09 - 7:14and think, "What was this person doing
that made him suspicious?" -
7:15 - 7:19The category "black man"
is not grounds for suspicion. -
7:19 - 7:25Second, they asked users to describe
the person's physical features, -
7:25 - 7:27not simply their race and gender.
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7:28 - 7:31Third, they realized that a lot of people
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7:31 - 7:34didn't seem to know
what racial profiling was, -
7:34 - 7:36nor that they were engaging in it.
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7:36 - 7:40So Nextdoor provided them
with a definition -
7:40 - 7:43and told them that it was
strictly prohibited. -
7:43 - 7:46Most of you have seen
those signs in airports -
7:46 - 7:49and in metro stations,
"If you see something, say something." -
7:50 - 7:53Nextdoor tried modifying this.
-
7:54 - 7:56"If you see something suspicious,
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7:56 - 7:58say something specific."
-
7:59 - 8:04And using this strategy,
by simply slowing people down, -
8:04 - 8:10Nextdoor was able to curb
racial profiling by 75 percent. -
8:10 - 8:13Now, people often will say to me,
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8:13 - 8:17"You can't add friction
in every situation, in every context, -
8:17 - 8:22and especially for people who make
split-second decisions all the time." -
8:23 - 8:25But it turns out we can add friction
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8:25 - 8:28to more situations than we think.
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8:28 - 8:30Working with the Oakland Police Department
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8:30 - 8:32in California,
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8:32 - 8:35I and a number of my colleagues
were able to help the department -
8:35 - 8:38to reduce the number of stops they made
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8:38 - 8:42of people who were not
committing any serious crimes. -
8:42 - 8:44And we did this by pushing officers
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8:44 - 8:49to ask themselves a question
before each and every stop they made: -
8:49 - 8:52"Is this stop intelligence-led,
-
8:52 - 8:54yes or no?"
-
8:55 - 8:57In other words,
-
8:58 - 9:02do I have prior information
to tie this particular person -
9:02 - 9:04to a specific crime?
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9:05 - 9:06By adding that question
-
9:06 - 9:09to the form officers complete
during a stop, -
9:09 - 9:11they slow down, they pause,
-
9:11 - 9:15they think, "Why am I considering
pulling this person over?" -
9:17 - 9:22In 2017, before we added that
intelligence-led question to the form, -
9:24 - 9:28officers made about 32,000 stops
across the city. -
9:28 - 9:32In that next year,
with the addition of this question, -
9:32 - 9:34that fell to 19,000 stops.
-
9:34 - 9:39African-American stops alone
fell by 43 percent. -
9:40 - 9:44And stopping fewer black people
did not make the city any more dangerous. -
9:44 - 9:47In fact, the crime rate continued to fall,
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9:47 - 9:50and the city became safer for everybody.
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9:50 - 9:56So one solution can come from reducing
the number of unnecessary stops. -
9:56 - 10:01Another can come from improving
the quality of the stops -
10:01 - 10:02officers do make.
-
10:03 - 10:05And technology can help us here.
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10:05 - 10:08We all know about George Floyd's death,
-
10:08 - 10:13because those who tried to come to his aid
held cell phone cameras -
10:13 - 10:19to record that horrific, fatal
encounter with the police. -
10:19 - 10:24But we have all sorts of technology
that we're not putting to good use. -
10:24 - 10:26Police departments across the country
-
10:26 - 10:30are now required to wear body-worn cameras
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10:30 - 10:36so we have recordings of not only
the most extreme and horrific encounters -
10:36 - 10:39but of everyday interactions.
-
10:39 - 10:41With an interdisciplinary
team at Stanford, -
10:41 - 10:44we've begun to use
machine learning techniques -
10:44 - 10:48to analyze large numbers of encounters.
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10:48 - 10:52This is to better understand
what happens in routine traffic stops. -
10:52 - 10:54What we found was that
-
10:54 - 10:58even when police officers
are behaving professionally, -
10:59 - 11:03they speak to black drivers
less respectfully than white drivers. -
11:04 - 11:08In fact, from the words
officers use alone, -
11:08 - 11:13we could predict whether they were talking
to a black driver or a white driver. -
11:13 - 11:19The problem is that the vast majority
of the footage from these cameras -
11:19 - 11:21is not used by police departments
-
11:21 - 11:24to understand what's
going on on the street -
11:24 - 11:26or to train officers.
-
11:27 - 11:28And that's a shame.
-
11:29 - 11:34How does a routine stop
turn into a deadly encounter? -
11:34 - 11:36How did this happen
in George Floyd's case? -
11:38 - 11:40How did it happen in others?
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11:40 - 11:43When my eldest son was 16 years old,
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11:43 - 11:46he discovered that
when white people look at him, -
11:46 - 11:48they feel fear.
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11:49 - 11:52Elevators are the worst, he said.
-
11:52 - 11:55When those doors close,
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11:55 - 11:58people are trapped in this tiny space
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11:58 - 12:02with someone they have been taught
to associate with danger. -
12:03 - 12:06My son senses their discomfort,
-
12:06 - 12:09and he smiles to put them at ease,
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12:09 - 12:11to calm their fears.
-
12:11 - 12:13When he speaks,
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12:13 - 12:15their bodies relax.
-
12:15 - 12:17They breathe easier.
-
12:17 - 12:20They take pleasure in his cadence,
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12:20 - 12:22his diction, his word choice.
-
12:23 - 12:25He sounds like one of them.
-
12:25 - 12:30I used to think that my son
was a natural extrovert like his father. -
12:30 - 12:33But I realized at that moment,
in that conversation, -
12:34 - 12:39that his smile was not a sign
that he wanted to connect -
12:39 - 12:41with would-be strangers.
-
12:42 - 12:46It was a talisman he used
to protect himself, -
12:46 - 12:52a survival skill he had honed
over thousands of elevator rides. -
12:52 - 12:58He was learning to accommodate the tension
that his skin color generated -
12:59 - 13:02and that put his own life at risk.
-
13:03 - 13:06We know that the brain is wired for bias,
-
13:06 - 13:11and one way to interrupt that bias
is to pause and to reflect -
13:11 - 13:13on the evidence of our assumptions.
-
13:13 - 13:15So we need to ask ourselves:
-
13:15 - 13:20What assumptions do we bring
when we step onto an elevator? -
13:22 - 13:23Or an airplane?
-
13:24 - 13:28How do we make ourselves aware
of our own unconscious bias? -
13:28 - 13:31Who do those assumptions keep safe?
-
13:33 - 13:35Who do they put at risk?
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13:36 - 13:38Until we ask these questions
-
13:39 - 13:44and insist that our schools
and our courts and our police departments -
13:44 - 13:46and every institution do the same,
-
13:48 - 13:52we will continue to allow bias
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13:52 - 13:53to blind us.
-
13:53 - 13:55And if we do,
-
13:56 - 13:59none of us are truly safe.
-
14:02 - 14:03Thank you.
- Title:
- How racial bias works -- and how to disrupt it
- Speaker:
- Jennifer L. Eberhardt
- Description:
-
Our brains create categories to make sense of the world, recognize patterns and make quick decisions. But this ability to categorize also exacts a heavy toll in the form of unconscious bias. In this powerful talk, psychologist Jennifer L. Eberhardt explores how our biases unfairly target Black people at all levels of society -- from schools and social media to policing and criminal justice -- and discusses how creating points of friction can help us actively interrupt and address this troubling problem.
- Video Language:
- English
- Team:
- closed TED
- Project:
- TEDTalks
- Duration:
- 14:17
Erin Gregory edited English subtitles for How racial bias works -- and how to disrupt it | ||
Erin Gregory approved English subtitles for How racial bias works -- and how to disrupt it | ||
Erin Gregory edited English subtitles for How racial bias works -- and how to disrupt it | ||
Camille Martínez accepted English subtitles for How racial bias works -- and how to disrupt it | ||
Camille Martínez edited English subtitles for How racial bias works -- and how to disrupt it | ||
Camille Martínez edited English subtitles for How racial bias works -- and how to disrupt it | ||
Joseph Geni edited English subtitles for How racial bias works -- and how to disrupt it | ||
Joseph Geni edited English subtitles for How racial bias works -- and how to disrupt it |