How racial bias works -- and how to disrupt it | Jennifer L. Eberhardt

譯者: Lilian Chiu 審譯者: Pui-Ching Siu

Some years ago,

幾年前，

I was on an airplane with my son who was just five years old at the time.

我和我兒子一起搭飛機， 他當時五歲。

My son was so excited about being on this airplane with Mommy.

對於能和媽咪一起搭飛機， 我兒子感到很興奮。

He's looking all around and he's checking things out

他環視周圍，到處看看，

and he's checking people out.

他也在打量其他人。

And he sees this man, and he says,

然後他看到一名男子，說：

"Hey! That guy looks like Daddy!"

「嘿！那個人看起來像爹地！」

And I look at the man,

我看向那名男子，

and he didn't look anything at all like my husband,

他看起來完全不像我丈夫，

nothing at all.

一點也不像。

And so then I start looking around on the plane,

所以我開始環視飛機內部，

and I notice this man was the only black guy on the plane.

我發現這名男子

是機上唯一的黑人男性。

And I thought,

我心想：「好。

"Alright.

I'm going to have to have a little talk with my son

我要和我的兒子談一下，

about how not all black people look alike."

讓他知道不是所有的黑人 看起來都一樣。」

My son, he lifts his head up, and he says to me,

我兒子抬起頭，對我說：

"I hope he doesn't rob the plane."

「我希望他不會在這架飛機上搶劫。」

And I said, "What? What did you say?"

我說：「什麼？你剛才說什麼？」

And he says, "Well, I hope that man doesn't rob the plane."

他說：「我希望那個人 不會在這架飛機上搶劫。」

And I said, "Well, why would you say that?

我說：「你為什麼會那樣說？

You know Daddy wouldn't rob a plane."

你知道爹地不可能會在飛機上搶劫。」

And he says, "Yeah, yeah, yeah, well, I know."

他說：「是啊，是啊，我知道。」

And I said, "Well, why would you say that?"

我說：「嗯，那你為什麼那樣說？」

And he looked at me with this really sad face,

他看著我，表情很悲傷，

and he says,

他回答：

"I don't know why I said that.

「我不知道我為什麼那樣說。

I don't know why I was thinking that."

我不知道我為什麼那樣想。」

We are living with such severe racial stratification

我們生活在如此 嚴重的種族分層當中，

that even a five-year-old can tell us what's supposed to happen next,

就連五歲小孩也會告訴我們 接下來應該會發生什麼事，

even with no evildoer,

即使當下沒有作惡者，

even with no explicit hatred.

也沒有明確的恨意。

This association between blackness and crime

黑人和犯罪之間的連結

made its way into the mind of my five-year-old.

進入了我五歲兒子的腦中。

It makes its way into all of our children,

它找到路進入了所有孩子的腦中，

into all of us.

我們所有人的腦中。

Our minds are shaped by the racial disparities

我們在世界上所看到的種族差異

we see out in the world

形塑了我們的大腦，

and the narratives that help us to make sense of the disparities we see:

這些說法協助我們理解 我們所看見的差異：

"Those people are criminal."

「那些人是罪犯。」

"Those people are violent."

「那些人很暴力。」

"Those people are to be feared."

「要害怕那些人。」

When my research team brought people into our lab

我的研究團隊把受試者 帶到我們的實驗室，

and exposed them to faces,

讓他們接觸不同的臉孔，

we found that exposure to black faces led them to see blurry images of guns

我們發現，若他們 接觸的是黑人的臉孔，

他們會更快、更清楚地

with greater clarity and speed.

看出槍枝的模糊影像。

Bias cannot only control what we see,

偏見不僅會控制我們看見什麼，

but where we look.

也會控制我們往哪裡看。

We found that prompting people to think of violent crime

我們發現，若促使 受試者去想像暴力罪行，

can lead them to direct their eyes onto a black face

他們會把視線轉向黑人臉孔，

and away from a white face.

而非白人臉孔。

Prompting police officers to think of capturing and shooting

若促使警察去想像 追捕、槍擊，以及逮捕，

and arresting

leads their eyes to settle on black faces, too.

也會導致他們把視線 落在黑人的臉孔上。

Bias can infect every aspect of our criminal justice system.

偏見會影響我們 刑事司法體系的每一方面。

In a large data set of death-eligible defendants,

在一個可判死刑之被告的 大型資料集中，

we found that looking more black more than doubled their chances

我們發現，膚色比較黑的人

被判死刑的機會高達兩倍以上——

of receiving a death sentence --

at least when their victims were white.

至少當被害人是白人時。

This effect is significant,

這個影響非常顯著，

even though we controlled for the severity of the crime

就算我們控制了犯罪的嚴重程度

and the defendant's attractiveness.

以及被告的吸引力也一樣。

And no matter what we controlled for,

不論我們控制哪些變數，

we found that black people were punished

我們都發現黑人受的懲罰

in proportion to the blackness of their physical features:

和他們身體膚色有多黑成比例：

the more black,

越黑的人，

the more death-worthy.

越有可能被判死刑。

Bias can also influence how teachers discipline students.

偏見也會影響老師如何懲戒學生。

My colleagues and I have found that teachers express a desire

我和我同事發現，

老師會想要對黑人中學生施以

to discipline a black middle school student more harshly

比白人學生更嚴厲的懲罰，

than a white student

for the same repeated infractions.

即使他們重犯相同的錯誤。

In a recent study,

在近期的一項研究中，我們發現

we're finding that teachers treat black students as a group

老師會把黑人學生 當作一個群體來對待，

but white students as individuals.

卻把白人學生當作 單獨的個體來對待。

If, for example, one black student misbehaves

比如說，一個黑人學生 做出了不當的行為，

and then a different black student misbehaves a few days later,

幾天後，有另一個黑人學生 也做了不當的行為，

the teacher responds to that second black student

老師對第二個黑人學生的反應

as if he had misbehaved twice.

會像是他做了兩次不當的行為。

It's as though the sins of one child

就彷彿一個孩子的罪過

get piled onto the other.

可以疊加在另一個孩子身上。

We create categories to make sense of the world,

為了理解世界，我們會創造分類，

to assert some control and coherence

為了稍微控制和整理

to the stimuli that we're constantly being bombarded with.

每天不斷轟炸我們的刺激物。

Categorization and the bias that it seeds

分類以及它所種下的偏見

allow our brains to make judgments more quickly and efficiently,

讓我們的大腦能快速 且有效地做出判斷，

and we do this by instinctively relying on patterns

我們會本能地依賴 看似可預測的模式。

that seem predictable.

Yet, just as the categories we create allow us to make quick decisions,

但，雖然我們創造的分類 能讓我們快速地做決策，

they also reinforce bias.

卻也會強化偏見。

So the very things that help us to see the world

所以，協助我們 理解世界的那些東西，

also can blind us to it.

也能矇蔽我們。

They render our choices effortless,

它們輕鬆地幫我們做出選擇，

friction-free.

完全沒有阻力。

Yet they exact a heavy toll.

但要付出的代價也很大。

So what can we do?

我們能怎麼做？

We are all vulnerable to bias,

我們大家都無法抵抗偏見，

but we don't act on bias all the time.

但我們不見得時時刻刻 都會依偏見行事。

There are certain conditions that can bring bias alive

有些條件會讓偏見活躍起來，

and other conditions that can muffle it.

有些則會抑制偏見。

Let me give you an example.

讓我舉個例子。

Many people are familiar with the tech company Nextdoor.

許多人都很熟悉 Nextdoor 這間科技公司。

So, their whole purpose is to create stronger, healthier, safer neighborhoods.

他們的目的是要創造出

更堅強、更健康、更安全的鄰里。

And so they offer this online space

他們便提供這個線上空間，

where neighbors can gather and share information.

讓鄰居可以聚集並分享資訊。

Yet, Nextdoor soon found that they had a problem

但，Nextdoor 很快就發現

他們有種族歸納方面的問題。

with racial profiling.

In the typical case,

在典型的情況中，

people would look outside their window

當居民看向窗外，

and see a black man in their otherwise white neighborhood

看到他們的白人鄰里中有個黑人，

and make the snap judgment that he was up to no good,

就會快速判斷這個黑人不懷好意，

even when there was no evidence of criminal wrongdoing.

即便沒有任何證據顯示 他有打算要犯罪。

In many ways, how we behave online

我們在線上的許多行為方式

is a reflection of how we behave in the world.

都反映出我們在真實世界的行為。

But what we don't want to do is create an easy-to-use system

但我們並不想要創造 一個操作簡易的系統

that can amplify bias and deepen racial disparities,

來放大偏見、加深種族差異，

rather than dismantling them.

而無法瓦解它們。

So the cofounder of Nextdoor reached out to me and to others

Nextdoor 的共同創辦人

便向我及他人求助， 希望能想出辦法來。

to try to figure out what to do.

And they realized that to curb racial profiling on the platform,

他們知道，若要在平台上 抑制種族歸納，

they were going to have to add friction;

他們就得增加阻力；

that is, they were going to have to slow people down.

意即，他們要讓大家慢下來。

So Nextdoor had a choice to make,

所以，Nextdoor 要做出選擇，

and against every impulse,

他們決定不衝動行事，

they decided to add friction.

選擇加上阻力。

And they did this by adding a simple checklist.

他們的做法是增添一個簡單的清單。

There were three items on it.

清單上有三個項目。

First, they asked users to pause

首先，他們請使用者暫停一下，

and think, "What was this person doing that made him suspicious?"

想想：「這個人做了什麼 讓他顯得可疑？」

The category "black man" is not grounds for suspicion.

「黑人」這個分類並不是 懷疑的基礎理由。

Second, they asked users to describe the person's physical features,

接著，他們請使用者描述 這個人的身體特徵，

not simply their race and gender.

不單單是種族和性別。

Third, they realized that a lot of people

第三，他們發現很多人

didn't seem to know what racial profiling was,

似乎不知道種族歸納是什麼，

nor that they were engaging in it.

也不知道他們自己參與其中。

So Nextdoor provided them with a definition

所以 Nextdoor 為他們提供定義，

and told them that it was strictly prohibited.

告訴他們種族歸納 是被嚴格禁止的。

Most of you have seen those signs in airports

大部分的人在機場和地鐵站

and in metro stations, "If you see something, say something."

都看過這類標牌：「如果 你看到了什麼事，說出來。」

Nextdoor tried modifying this.

Nextdoor 把這個標牌改成：

"If you see something suspicious,

「如果你看見了什麼可疑的事，

say something specific."

明確地說出來。」

And using this strategy, by simply slowing people down,

光是用這種讓大家慢下來的策略，

Nextdoor was able to curb racial profiling by 75 percent.

Nextdoor 就讓 種族歸納減少了 75%。

Now, people often will say to me,

現在，大家通常會對我說：

"You can't add friction in every situation, in every context,

「你不可能在所有情況、 所有情境中加上阻力，

and especially for people who make split-second decisions all the time."

更不可能改變那些總是 瞬間做決定的人。」

But it turns out we can add friction

但結果發現，我們能添加阻力的情況

to more situations than we think.

比我們想像中的還要多。

Working with the Oakland Police Department

和加州的奧克蘭警局合作之後，

in California,

I and a number of my colleagues were able to help the department

我和幾位同事能夠協助該警局

to reduce the number of stops they made

減少攔檢沒有犯下 任何嚴重罪行者的次數。

of people who were not committing any serious crimes.

And we did this by pushing officers

我們的做法是要求警員

to ask themselves a question before each and every stop they made:

在做每次攔檢時 都要先問自己一個問題：

"Is this stop intelligence-led,

「這次攔檢是有思考過 才決定要做的，

yes or no?"

是或否？」

In other words,

換言之，

do I have prior information to tie this particular person

我在攔檢之前是否有任何資訊，

能將這個人和特定的 犯罪連結在一起？

to a specific crime?

By adding that question

把那個問題加到警員 在攔檢時要填寫的表格上，

to the form officers complete during a stop,

they slow down, they pause,

就會讓警員慢下來，暫停一下，

they think, "Why am I considering pulling this person over?"

他們會想：「我為什麼 會想要把這個人攔下來？」

In 2017, before we added that intelligence-led question to the form,

在 2017 年，我們把 那個思考問題納入表格之前，

officers made about 32,000 stops across the city.

全市的警員進行了 大約 32,000 次攔檢。

In that next year, with the addition of this question,

隔年，加上了那個問題之後，

that fell to 19,000 stops.

數字減少至 19,000 次攔檢。

African-American stops alone fell by 43 percent.

光是對非裔美國人 所做的攔檢就減少了 43%。

And stopping fewer black people did not make the city any more dangerous.

且減少對黑人的攔檢次數 並沒有讓城市變得比較危險。

In fact, the crime rate continued to fall,

事實上，犯罪率持續下降，

and the city became safer for everybody.

該城市變得讓每個人都更安全。

So one solution can come from reducing the number of unnecessary stops.

一個解決方案是減少 不必要的攔檢數目。

Another can come from improving the quality of the stops

另一個解決方案則是必須攔檢時，

改善攔檢的品質。

officers do make.

And technology can help us here.

此時科技就能派上用場。

We all know about George Floyd's death,

我們都知道喬治．佛洛伊德的死亡，

because those who tried to come to his aid held cell phone cameras

因為當時試圖協助他的人

用手機攝影機拍下

to record that horrific, fatal encounter with the police.

這段和警方可怕、致命的遭遇。

But we have all sorts of technology that we're not putting to good use.

我們有各種科技， 但我們沒有善用。

Police departments across the country

全國各地的警局

are now required to wear body-worn cameras

現在都被要求在身上戴攝影機，

so we have recordings of not only the most extreme and horrific encounters

這麼一來，我們會記錄到的 不僅是最可怕極端的遭遇，

but of everyday interactions.

還會記錄日常的互動。

With an interdisciplinary team at Stanford,

我們和史丹佛大學的 一個跨領域團隊合作，

we've begun to use machine learning techniques

開始使用機器學習技術

to analyze large numbers of encounters.

來分析大量相遇的情境。

This is to better understand what happens in routine traffic stops.

目的是要更了解在例行的 交通攔檢時會發生什麼事。

What we found was that

我們發現，

even when police officers are behaving professionally,

即使警員的行為表現非常專業，

they speak to black drivers less respectfully than white drivers.

比起白人司機，他們對黑人司機的 說話方式仍然比較不尊重。

In fact, from the words officers use alone,

事實上，單單從警員的用詞，

we could predict whether they were talking to a black driver or a white driver.

我們就能預測出他們是在 和黑人司機或白人司機說話。

The problem is that the vast majority of the footage from these cameras

問題是，這些攝影機 所拍攝的大部分影片

is not used by police departments

並沒有被警局用來 了解街上發生的狀況

to understand what's going on on the street

or to train officers.

或用來訓練警員。

And that's a shame.

那很可惜。

How does a routine stop turn into a deadly encounter?

例行的攔檢怎麼會 變成致命的遭遇？

How did this happen in George Floyd's case?

在喬治．佛洛伊德的案例中 這是怎麼發生的？

How did it happen in others?

在其他的案例中呢？

When my eldest son was 16 years old,

我的長子 16 歲時，

he discovered that when white people look at him,

他發現當白人看著他時，

they feel fear.

他們會感到恐懼。

Elevators are the worst, he said.

他說，最糟的時候是在電梯裡。

When those doors close,

當電梯門關上，

people are trapped in this tiny space

大家被困在這個小小的空間裡，

with someone they have been taught to associate with danger.

和他們認為危險的人困在一起。

My son senses their discomfort,

我兒子能感受到他們的不適，

and he smiles to put them at ease,

並用微笑讓他們感到自在些，

to calm their fears.

降低他們的恐懼。

When he speaks,

當他說話時，

their bodies relax.

他們的身體放鬆了。

They breathe easier.

他們比較能呼吸。

They take pleasure in his cadence,

他們喜歡他的抑揚頓挫、

his diction, his word choice.

他的發音、他的用字遣詞。

He sounds like one of them.

他聽起來就像他們的一份子。

I used to think that my son was a natural extrovert like his father.

我以前認為我兒子 和他爸爸一樣天生外向。

But I realized at that moment, in that conversation,

但，在那一刻， 我從那段談話中了解到，

that his smile was not a sign that he wanted to connect

他的微笑並不表示他想要

和那些本來會是陌生人的人連結。

with would-be strangers.

It was a talisman he used to protect himself,

他的微笑是個護身符， 用來保護他自己，

a survival skill he had honed over thousands of elevator rides.

是他搭電梯搭了數千次 而磨練出來的生存技能。

He was learning to accommodate the tension that his skin color generated

他的膚色會產生緊張感， 讓他自己有生命危險，

and that put his own life at risk.

他便學習去調解這樣的緊張感。

We know that the brain is wired for bias,

我們知道大腦天生就會有偏見，

and one way to interrupt that bias is to pause and to reflect

打破那偏見的一種方式 就是先暫停下來做反思，

on the evidence of our assumptions.

想想我們的假設有什麼依據。

So we need to ask ourselves:

我們得要問問自己：

What assumptions do we bring when we step onto an elevator?

我們帶著什麼假設踏進電梯？

Or an airplane?

或上飛機？

How do we make ourselves aware of our own unconscious bias?

我們要如何察覺到 自己無意識的偏見？

Who do those assumptions keep safe?

那些假設能保護誰的安全？

Who do they put at risk?

那些假設讓誰有危險？

Until we ask these questions

若我們不去問這些問題，

and insist that our schools and our courts and our police departments

不去堅持我們的學校、法庭、警局，

and every institution do the same,

及其他機構都要問這些問題，

we will continue to allow bias

那麼我們就會讓偏見繼續

to blind us.

矇蔽我們。

And if we do,

這麼一來，

none of us are truly safe.

沒有誰是真正安全的。

Thank you.

謝謝。