in my very first year of college --

大一時我開始了第一份工作： 程式設計師，

basically, as a teenager.

當時我還算是個青少女。

Soon after I started working,

開始為軟體公司寫程式後不久，

writing software in a company,

公司裡的一個經理走到我身邊，

a manager who worked at the company came down to where I was,

悄悄地問：

and he whispered to me,

「他能判斷我是否說謊嗎？」

"Can he tell if I'm lying?"

當時房裡沒別人。

There was nobody else in the room.

「『誰』能不能判斷你說謊與否？ 而且，我們為什麼耳語呢？」

"Can who tell if you're lying? And why are we whispering?"

經理指著房裡的電腦，問：

The manager pointed at the computer in the room.

「『他』能判斷我是否說謊嗎？」

"Can he tell if I'm lying?"

當時那經理與接待員有曖昧關係。

Well, that manager was having an affair with the receptionist.

（笑聲）

(Laughter)

那時我仍是個青少女。

And I was still a teenager.

所以，我用耳語大聲地回答他：

So I whisper-shouted back to him,

「能，電腦能判斷你撒謊與否。」

"Yes, the computer can tell if you're lying."

（笑聲）

(Laughter)

沒錯，我笑了，但可笑的人是我。

Well, I laughed, but actually, the laugh's on me.

如今，有些計算系統

Nowadays, there are computational systems

靠分析、判讀面部表情， 就能判斷出情緒狀態，

that can suss out emotional states and even lying

甚至判斷是否說謊。

from processing human faces.

廣告商，甚至政府也對此很感興趣。

Advertisers and even governments are very interested.

我之所以成為程式設計師，

I had become a computer programmer

是因為自幼便極為喜愛數學和科學。

because I was one of those kids crazy about math and science.

過程中我學到核子武器，

But somewhere along the line I'd learned about nuclear weapons,

因而變得非常關心科學倫理。

and I'd gotten really concerned with the ethics of science.

我很苦惱。

I was troubled.

但由於家庭狀況，

However, because of family circumstances,

我必須儘早就業。

I also needed to start working as soon as possible.

因此我告訴自己，

So I thought to myself, hey, let me pick a technical field

選擇一個在科技領域中 能簡單地找到頭路，

where I can get a job easily

又無需處理涉及倫理道德 這類麻煩問題的工作吧。

and where I don't have to deal with any troublesome questions of ethics.

所以我選擇了電腦。

So I picked computers.

（笑聲）

(Laughter)

是啊，哈哈哈！大家都笑我。

Well, ha, ha, ha! All the laughs are on me.

如今，電腦科學家

Nowadays, computer scientists are building platforms

正建構著可控制數十億人 每天接收訊息的平台。

that control what a billion people see every day.

他們設計的汽車 可以決定要輾過哪些人。

They're developing cars that could decide who to run over.

他們甚至建造能殺人的 戰爭機器和武器。

They're even building machines, weapons,

從頭到尾都是倫理的問題。

that might kill human beings in war.

機器智慧已經在此。

It's ethics all the way down.

我們利用計算來做各種決策，

Machine intelligence is here.

同時也是種新形態的決策。

We're now using computation to make all sort of decisions,

我們以計算來尋求解答， 但問題沒有單一的正解，

but also new kinds of decisions.

而是主觀、開放、具價值觀的答案。

We're asking questions to computation that have no single right answers,

問題像是，

that are subjective

「公司應該聘誰？」

and open-ended and value-laden.

「應該顯示哪個朋友的哪項更新？」

We're asking questions like,

「哪個罪犯更可能再犯？」

"Who should the company hire?"

「應該推薦哪項新聞或哪部電影？」

"Which update from which friend should you be shown?"

我們使用電腦雖有一段時間了，

"Which convict is more likely to reoffend?"

但這是不同的。

"Which news item or movie should be recommended to people?"

這是歷史性的轉折，

Look, yes, we've been using computers for a while,

因我們不能主導計算機 如何去做這樣的主觀決定，

but this is different.

無法像主導計算機去開飛機、造橋樑

This is a historical twist,

或登陸月球那樣。

because we cannot anchor computation for such subjective decisions

飛機會更安全嗎？ 橋樑會搖擺或倒塌嗎？

the way we can anchor computation for flying airplanes, building bridges,

那兒已有相當明確的基準共識，

going to the moon.

有自然的法則指引著我們。

Are airplanes safer? Did the bridge sway and fall?

但我們沒有

There, we have agreed-upon, fairly clear benchmarks,

判斷凌亂人事的錨點或基準。

and we have laws of nature to guide us.

使事情變得更為複雜的是， 因軟體越來越強大，

We have no such anchors and benchmarks

但也越來越不透明，越複雜難懂。

for decisions in messy human affairs.

過去十年

To make things more complicated, our software is getting more powerful,

複雜的演算法有長足的進步：

but it's also getting less transparent and more complex.

能辨識人臉，

Recently, in the past decade,

能解讀手寫的字，

complex algorithms have made great strides.

能檢測信用卡欺詐，

They can recognize human faces.

阻擋垃圾郵件，

They can decipher handwriting.

能翻譯不同的語言，

They can detect credit card fraud

能判讀醫學影像查出腫瘤，

and block spam

能在西洋棋和圍棋賽中 擊敗人類棋手。

and they can translate between languages.

這些進步主要來自所謂的 「機器學習」法。

They can detect tumors in medical imaging.

機器學習不同於傳統的程式編寫。

They can beat humans in chess and Go.

編寫程式是下詳細、精確、 齊全的計算機指令；

Much of this progress comes from a method called "machine learning."

機器學習更像是 餵大量的數據給系統，

Machine learning is different than traditional programming,

包括非結構化的數據，

where you give the computer detailed, exact, painstaking instructions.

像我們數位生活產生的數據；

It's more like you take the system and you feed it lots of data,

系統翻撈這些數據來學習。

including unstructured data,

至關重要的是，

like the kind we generate in our digital lives.

這些系統不在產生 單一答案的邏輯系統下運作；

And the system learns by churning through this data.

它們不會給出一個簡單的答案，

And also, crucially,

而是以更接近機率的形式呈現：

these systems don't operate under a single-answer logic.

「這可能更接近你所要找的。」

They don't produce a simple answer; it's more probabilistic:

好處是：這方法強而有力。

"This one is probably more like what you're looking for."

谷歌的人工智慧系統負責人稱之為：

Now, the upside is: this method is really powerful.

「不合理的數據有效性。」

The head of Google's AI systems called it,

缺點是，

"the unreasonable effectiveness of data."

我們未能真正明白 系統學到了什麼。

The downside is,

事實上，這就是它的力量。

we don't really understand what the system learned.

這不像下指令給計算機；

In fact, that's its power.

而更像是訓練

This is less like giving instructions to a computer;

我們未能真正了解 或無法控制的機器寵物狗。

it's more like training a puppy-machine-creature

這是我們的問題。

we don't really understand or control.

人工智慧系統出錯時會是個問題；

So this is our problem.

即使它弄對了還是個問題，

It's a problem when this artificial intelligence system gets things wrong.

因碰到主觀問題時， 我們不知哪個是哪個。

It's also a problem when it gets things right,

我們不知道系統在想什麼。

because we don't even know which is which when it's a subjective problem.

就拿招募人員的演算法來說，

We don't know what this thing is thinking.

亦即以機器學習來僱用人的系統，

So, consider a hiring algorithm --

這樣的系統用 已有的員工數據來訓練機器，

a system used to hire people, using machine-learning systems.

指示它尋找和僱用那些

Such a system would have been trained on previous employees' data

類似公司現有的高績效員工的人。

and instructed to find and hire

聽起來不錯。

people like the existing high performers in the company.

我曾參加某會議，

Sounds good.

聚集人資經理和高階主管，

I once attended a conference

高層人士，

that brought together human resources managers and executives,

使用這種系統招聘。

high-level people,

他們超級興奮，

using such systems in hiring.

認為這種系統會使招聘更為客觀，

They were super excited.

較少偏見，

They thought that this would make hiring more objective, less biased,

有利於婦女和少數民族

and give women and minorities a better shot

避開有偏見的管理人。

against biased human managers.

看哪！靠人類僱用是有偏見的。

And look -- human hiring is biased.

我知道。

I know.

我的意思是， 在早期某個編寫程式的工作，

I mean, in one of my early jobs as a programmer,

有時候我的直屬主管會在

my immediate manager would sometimes come down to where I was

大清早或下午很晚時來到我身旁，

really early in the morning or really late in the afternoon,

說：「日娜，走，吃午飯！」

and she'd say, "Zeynep, let's go to lunch!"

我被奇怪的時間點所困惑。

I'd be puzzled by the weird timing.

下午 4 點。吃午餐？

It's 4pm. Lunch?

我很窮，

I was broke, so free lunch. I always went.

因為是免費的午餐，所以總是會去。

I later realized what was happening.

後來我明白到底是怎麼回事。

My immediate managers had not confessed to their higher-ups

我的直屬主管沒讓她的主管知道，

that the programmer they hired for a serious job was a teen girl

他們僱來做重要職務的程式設計師，

who wore jeans and sneakers to work.

是個穿牛仔褲和運動鞋

I was doing a good job, I just looked wrong

來上班的十幾歲女孩。

and was the wrong age and gender.

我工作做得很好， 只是外表形象看起來不符，

So hiring in a gender- and race-blind way

年齡和性別不對。

certainly sounds good to me.

因此，性別和種族 不列入考慮的僱用系統

But with these systems, it is more complicated, and here's why:

對我而言當然不錯。

Currently, computational systems can infer all sorts of things about you

但使用這些系統會更複雜，原因是：

from your digital crumbs,

目前的計算系統

even if you have not disclosed those things.

可從你零散的數位足跡 推斷出關於你的各種事物，

They can infer your sexual orientation,

即使你未曾披露過。

your personality traits,

他們能推斷你的性取向，

your political leanings.

個性的特質，

They have predictive power with high levels of accuracy.

政治的傾向。

Remember -- for things you haven't even disclosed.

他們的預測能力相當精準。

This is inference.

請記住：知道你未曾公開的事情

I have a friend who developed such computational systems

是推理。

to predict the likelihood of clinical or postpartum depression

我有個朋友開發這樣的計算系統：

from social media data.

從社交媒體數據來預測 臨床或產後抑鬱症的可能性。

The results are impressive.

結果非常優異。

Her system can predict the likelihood of depression

她的系統

months before the onset of any symptoms --

能在出現任何症狀的幾個月前 預測出抑鬱的可能性，

months before.

是好幾個月前。

No symptoms, there's prediction.

雖沒有症狀，已預測出來。

She hopes it will be used for early intervention. Great!

她希望它被用來早期干預處理。

But now put this in the context of hiring.

很好！

So at this human resources managers conference,

但是，設想若把這系統 用在僱人的情況下。

I approached a high-level manager in a very large company,

在這人資經理會議中，

and I said to her, "Look, what if, unbeknownst to you,

我走向一間大公司的高階經理，

your system is weeding out people with high future likelihood of depression?

對她說：

They're not depressed now, just maybe in the future, more likely.

「假設在你不知道的情形下，

What if it's weeding out women more likely to be pregnant

那個系統被用來排除 未來極有可能抑鬱的人呢？

in the next year or two but aren't pregnant now?

他們現在不抑鬱， 只是未來『比較有可能』抑鬱。

What if it's hiring aggressive people because that's your workplace culture?"

如果它被用來排除 在未來一兩年比較有可能懷孕，

You can't tell this by looking at gender breakdowns.

但現在沒懷孕的婦女呢？

Those may be balanced.

如果它被用來招募激進性格者， 以符合你的職場文化呢？」

And since this is machine learning, not traditional coding,

透過性別比例無法看到這些問題，

there is no variable there labeled "higher risk of depression,"

因比例可能是均衡的。

"higher risk of pregnancy,"

而且由於這是機器學習， 不是傳統編碼，

"aggressive guy scale."

沒有標記為「更高抑鬱症風險」、

Not only do you not know what your system is selecting on,

「更高懷孕風險」、

you don't even know where to begin to look.

「侵略性格者」的變數；

It's a black box.

你不僅不知道系統在選什麼，

It has predictive power, but you don't understand it.

甚至不知道要從何找起。

"What safeguards," I asked, "do you have

它就是個黑盒子，

to make sure that your black box isn't doing something shady?"

具有預測能力，但你不了解它。

She looked at me as if I had just stepped on 10 puppy tails.

我問：「你有什麼能確保

(Laughter)

你的黑盒子沒在暗地裡 做了什麼不可告人之事？

She stared at me and she said,

她看著我，彷彿我剛踩了 十隻小狗的尾巴。

"I don't want to hear another word about this."

（笑聲）

And she turned around and walked away.

她盯著我，說：

Mind you -- she wasn't rude.

「關於這事，我不想 再聽妳多說一個字。」

It was clearly: what I don't know isn't my problem, go away, death stare.

然後她就轉身走開了。

(Laughter)

提醒你們，她不是粗魯。

Look, such a system may even be less biased

她的意思很明顯：

than human managers in some ways.

我不知道的事不是我的問題。

And it could make monetary sense.

走開。惡狠狠盯著。

But it could also lead

（笑聲）

to a steady but stealthy shutting out of the job market

這樣的系統可能比人類經理 在某些方面更沒有偏見，

of people with higher risk of depression.

可能也省錢；

Is this the kind of society we want to build,

但也可能在不知不覺中逐步導致

without even knowing we've done this,

抑鬱症風險較高的人 在就業市場裡吃到閉門羹。

because we turned decision-making to machines we don't totally understand?

我們要在不自覺的情形下 建立這種社會嗎？

Another problem is this:

僅僅因我們讓給 我們不完全理解的機器做決策？

these systems are often trained on data generated by our actions,

另一個問題是：這些系統通常由

human imprints.

我們行動產生的數據， 即人類的印記所訓練。

Well, they could just be reflecting our biases,

它們可能只是反映我們的偏見，

and these systems could be picking up on our biases

學習了我們的偏見

and amplifying them

並且放大，

and showing them back to us,

然後回饋給我們；

while we're telling ourselves,

而我們卻告訴自己：

"We're just doing objective, neutral computation."

「這樣做是客觀、不偏頗的計算。」

Researchers found that on Google,

研究人員在谷歌上發現，

women are less likely than men to be shown job ads for high-paying jobs.

女性比男性更不易看到 高薪工作招聘的廣告。

And searching for African-American names

蒐索非裔美國人的名字

is more likely to bring up ads suggesting criminal history,

比較可能帶出暗示犯罪史的廣告，

even when there is none.

即使那人並無犯罪史。

Such hidden biases and black-box algorithms

這種隱藏偏見和黑箱的演算法，

that researchers uncover sometimes but sometimes we don't know,

有時被研究人員發現了， 但有時我們毫無所知，

can have life-altering consequences.

很可能產生改變生命的後果。

In Wisconsin, a defendant was sentenced to six years in prison

在威斯康辛州，某個被告 因逃避警察而被判處六年監禁。

for evading the police.

你可能不知道

You may not know this,

演算法越來越頻繁地被用在

but algorithms are increasingly used in parole and sentencing decisions.

假釋和量刑的決定上。

He wanted to know: How is this score calculated?

想知道分數如何計算出來的嗎？

It's a commercial black box.

這是個商業的黑盒子，

The company refused to have its algorithm be challenged in open court.

開發它的公司

But ProPublica, an investigative nonprofit, audited that very algorithm

拒絕讓演算法在公開法庭上受盤問。

with what public data they could find,

但是 ProPublica 這家 非營利機構評估該演算法，

and found that its outcomes were biased

使用找得到的公共數據，

and its predictive power was dismal, barely better than chance,

發現其結果偏頗，

and it was wrongly labeling black defendants as future criminals

預測能力相當差，僅比碰運氣稍強，

at twice the rate of white defendants.

並錯誤地標記黑人被告 成為未來罪犯的機率，

So, consider this case:

是白人被告的兩倍。

This woman was late picking up her godsister

考慮這個情況：

from a school in Broward County, Florida,

這女人因來不及去佛州布勞沃德郡的 學校接她的乾妹妹，

running down the street with a friend of hers.

而與朋友狂奔趕赴學校。

They spotted an unlocked kid's bike and a scooter on a porch

他們看到門廊上有一輛未上鎖的 兒童腳踏車和一台滑板車，

and foolishly jumped on it.

愚蠢地跳上去，

As they were speeding off, a woman came out and said,

當他們趕時間快速離去時，

"Hey! That's my kid's bike!"

一個女人出來說： 「嘿！那是我孩子的腳踏車！」

They dropped it, they walked away, but they were arrested.

雖然他們留下車子走開， 但被逮捕了。

She was wrong, she was foolish, but she was also just 18.

她錯了，她很蠢，但她只有十八歲。

She had a couple of juvenile misdemeanors.

曾觸犯兩次少年輕罪。

Meanwhile, that man had been arrested for shoplifting in Home Depot --

同時，

85 dollars' worth of stuff, a similar petty crime.

那個男人因在家得寶商店 偷竊八十五美元的東西而被捕，

But he had two prior armed robbery convictions.

類似的小罪，

But the algorithm scored her as high risk, and not him.

但他曾兩次因武裝搶劫而被定罪。

Two years later, ProPublica found that she had not reoffended.

演算法認定她有再犯的高風險，

It was just hard to get a job for her with her record.

而他卻不然。

He, on the other hand, did reoffend

兩年後，ProPublica 發現她未曾再犯；

and is now serving an eight-year prison term for a later crime.

但因有過犯罪紀錄而難以找到工作。

Clearly, we need to audit our black boxes