What tech companies know about your kids | Veronica Barassi

Transcriber: Leslie Gauthier Reviewer: Joanna Pietrulewicz

譯者: Lilian Chiu 審譯者: Carol Wang

Every day, every week,

每天，每週，

we agree to terms and conditions.

我們同意某些「條件及條款」。

And when we do this,

當我們這麼做時，

we provide companies with the lawful right

我們便讓公司擁有合法的權利

to do whatever they want with our data

可以任意使用我們的資料，

and with the data of our children.

以及我們孩子的資料。

Which makes us wonder:

這會讓我們不禁納悶：

how much data are we giving away of children,

我們給出了有關孩子的多少資料，

and what are its implications?

以及這背後的意涵是什麼？

I'm an anthropologist,

我是人類學家，

and I'm also the mother of two little girls.

同時也是兩個小女孩的母親。

And I started to become interested in this question in 2015

我從 2015 年開始 對這個問題感到好奇，

when I suddenly realized that there were vast --

那年，我突然發現，有很大量——

almost unimaginable amounts of data traces

和孩子有關的追蹤資料 被產生出來並收集起來，

that are being produced and collected about children.

且數量大到無法想像。

So I launched a research project,

於是，我展開了一項研究計畫， 名稱叫做「兒童資料公民」，

which is called Child Data Citizen,

and I aimed at filling in the blank.

我的目標是要填補這些空白。

Now you may think that I'm here to blame you

各位可能會認為我是來責怪大家

for posting photos of your children on social media,

在社群媒體上張貼 自己孩子的照片，

but that's not really the point.

但那其實不是重點。

The problem is way bigger than so-called "sharenting."

問題遠大於所謂的 「分享式教養」。

This is about systems, not individuals.

重點在於體制，而不是個人。

You and your habits are not to blame.

要怪的不是你們和你們的習慣。

For the very first time in history,

史無前例，

we are tracking the individual data of children

我們遠在孩子出生之前 就開始追蹤他們的個人資料——

from long before they're born --

sometimes from the moment of conception,

有時是從懷孕就開始，

and then throughout their lives.

接著便追蹤他們的一生。

You see, when parents decide to conceive,

要知道，當父母決定要懷孕時，

they go online to look for "ways to get pregnant,"

他們會上網搜尋「懷孕的方式」，

or they download ovulation-tracking apps.

或者他們會下載 排卵追蹤應用程式。

When they do get pregnant,

當他們確實懷孕之後，

they post ultrasounds of their babies on social media,

他們會把寶寶的超音波照片 張貼在社群媒體上，

they download pregnancy apps

他們會下載懷孕期應用程式，

or they consult Dr. Google for all sorts of things,

或者他們會向 Google 大神 諮詢各種相關事項。

like, you know --

比如——

for "miscarriage risk when flying"

搜尋「飛行造成的流產風險」，

or "abdominal cramps in early pregnancy."

或「懷孕初期的腹痛」。

I know because I've done it --

我知道是因為我做過許多次。

and many times.

And then, when the baby is born, they track every nap,

等寶寶出生了，他們會用各種技術

every feed,

追蹤每次小盹、每次進食、 生命中的每件事。

every life event on different technologies.

And all of these technologies

而他們用的這些技術，

transform the baby's most intimate behavioral and health data into profit

都會把寶寶最私密的行為 和健康資料分享出去，

by sharing it with others.

以轉換成利潤。

So to give you an idea of how this works,

讓我說明一下這是怎麼運作的：

in 2019, the British Medical Journal published research that showed

2019 年，英國醫學期刊 刊出了一篇研究，

that out of 24 mobile health apps,

指出在二十四個 行動健康應用程式中，

19 shared information with third parties.

有十九個會和第三方分享資訊。

And these third parties shared information with 216 other organizations.

而這些第三方會把資訊分享給

兩百一十六個其他組織。

Of these 216 other fourth parties,

在這兩百一十六個第四方當中，

only three belonged to the health sector.

只有三個屬於健康領域。

The other companies that had access to that data were big tech companies

其他能取得這些資料的公司 則是大型科技公司，

like Google, Facebook or Oracle,

比如 Google、臉書， 或甲骨文公司，

they were digital advertising companies

還有數位廣告公司，

and there was also a consumer credit reporting agency.

還有一家是消費者信用調查機構。

So you get it right:

所以，沒錯：

ad companies and credit agencies may already have data points on little babies.

廣告公司和信用機構可能 都已經有小寶寶的資料了。

But mobile apps, web searches and social media

但，行動應用程式、 網路搜尋和社群媒體

are really just the tip of the iceberg,

其實只是冰山的一角，

because children are being tracked by multiple technologies

因為有許多技術在日常生活中

in their everyday lives.

追蹤兒童的資料。

They're tracked by home technologies and virtual assistants in their homes.

在家中，家用科技 和虛擬助理會追蹤兒童。

They're tracked by educational platforms

在學校，教育平台 和教育相關技術都會追蹤兒童。

and educational technologies in their schools.

They're tracked by online records

在醫生的診間，線上記錄 和線上入口網站都會追蹤兒童。

and online portals at their doctor's office.

They're tracked by their internet-connected toys,

還有需連結網路的玩具、線上遊戲

their online games

及許多許多其他技術 都會追蹤兒童。

and many, many, many, many other technologies.

So during my research,

所以，在我研究期間，

a lot of parents came up to me and they were like, "So what?

很多父母來找我， 他們會說：「又怎樣？

Why does it matter if my children are being tracked?

我的孩子被追蹤有什麼關係？

We've got nothing to hide."

我們沒啥要隱瞞的。」

Well, it matters.

這是有關係的。

It matters because today individuals are not only being tracked,

有關係是因為，現今，

個人不僅受到追蹤，

they're also being profiled on the basis of their data traces.

這些追蹤資料還會 被拿來建構他們的側寫評比。

Artificial intelligence and predictive analytics are being used

人工智慧和預測分析

to harness as much data as possible of an individual life

正被用來盡可能多地利用

不同來源的個人生活資料：

from different sources:

family history, purchasing habits, social media comments.

家族史、購買習慣、社群媒體留言。

And then they bring this data together

接著，這些資料會被整合，

to make data-driven decisions about the individual.

以資料為根據， 做出針對個人的決策。

And these technologies are used everywhere.

到處都在使用這些技術。

Banks use them to decide loans.

銀行用它們來決定貸款，

Insurance uses them to decide premiums.

保險公司用它們來決定保費，

Recruiters and employers use them

招聘公司和僱主用它們

to decide whether one is a good fit for a job or not.

來判定應徵者是否適合某個職缺。

Also the police and courts use them

連警方和法庭也會用它們

to determine whether one is a potential criminal

來判斷一個人是否有可能是罪犯，

or is likely to recommit a crime.

或是否有可能再犯。

We have no knowledge or control

我們不知道也無法控制

over the ways in which those who buy, sell and process our data

購買、銷售、處理我們資料的公司

會用什麼方式來對我們 和我們的孩子做側寫評比，

are profiling us and our children.

But these profiles can come to impact our rights in significant ways.

但那些側寫評比有可能會 顯著影響我們的權利。

To give you an example,

舉個例子，

in 2018 the "New York Times" published the news

2018 年《紐約時報》 刊載的新聞提到，

that the data that had been gathered

透過大學規劃線上服務 所收集到的資料——

through online college-planning services --

that are actually completed by millions of high school kids across the US

這些資料來自全美各地數百萬名

想要尋找大學科系 或獎學金的高中生——

who are looking for a college program or a scholarship --

had been sold to educational data brokers.

被販售給教育資料中介商。

Now, researchers at Fordham who studied educational data brokers

福坦莫大學裡那些研究 教育資料中介商的研究者

revealed that these companies profiled kids as young as two

揭發出這些公司會根據不同的分類

來為小至兩歲的兒童做側寫評比：

on the basis of different categories:

ethnicity, religion, affluence,

人種、宗教、富裕程度、

social awkwardness

社交尷尬

and many other random categories.

及許多其他隨機的分類。

And then they sell these profiles together with the name of the kid,

接著，它們會賣掉這些側寫評比，

連帶附上兒童的姓名、 地址和聯絡細節資訊，

their home address and the contact details

to different companies,

賣給各種公司，

including trade and career institutions,

包括貿易和職涯機構、

student loans

學生貸款以及學生信用卡公司。

and student credit card companies.

To push the boundaries,

福坦莫大學的研究者還更進一步，

the researchers at Fordham asked an educational data broker

請一家教育資料中介商 提供他們一份名單，

to provide them with a list of 14-to-15-year-old girls

羅列十四到十五歲 對於避孕措施感興趣的女孩。

who were interested in family planning services.

The data broker agreed to provide them the list.

資料中介商同意 提供他們這份名單。

So imagine how intimate and how intrusive that is for our kids.

想像這多麼侵害我們孩子的私密。

But educational data brokers are really just an example.

但，教育資料中介商 也只不過是一個例子。

The truth is that our children are being profiled in ways that we cannot control

事實是，我們無法控制別人 如何對我們的孩子做側寫評比，

but that can significantly impact their chances in life.

但這些側寫評比卻會明顯影響 他們在人生中的機會。

So we need to ask ourselves:

所以，我們得要捫心自問：

can we trust these technologies when it comes to profiling our children?

我們能信任這些 側寫評比孩子的技術嗎？

Can we?

能嗎？

My answer is no.

我的答案是「不能。」

As an anthropologist,

身為人類學家，

I believe that artificial intelligence and predictive analytics can be great

我相信人工智慧和預測分析

to predict the course of a disease

很擅長預測疾病的過程 或對抗氣候變遷。

or to fight climate change.

But we need to abandon the belief

但我們不能夠信任

that these technologies can objectively profile humans

這些技術能夠客觀地 對人類做側寫評比，

and that we can rely on them to make data-driven decisions

讓我們依據這些側寫評比資料 來對個人的人生做出判斷，

about individual lives.

Because they can't profile humans.

因為它們無法對人類做側寫評比。

Data traces are not the mirror of who we are.

追蹤資料並無法反映出 我們是什麼樣的人。

Humans think one thing and say the opposite,

人類說出來的話 可能和心中想的相反，

feel one way and act differently.

做出來的行為 可能和心中的感受不同。

Algorithmic predictions or our digital practices

用演算法做預測或其他數位做法

cannot account for the unpredictability and complexity of human experience.

無法考量到人類經歷中的 不可預測性和複雜性。

But on top of that,

除此之外，

these technologies are always --

這些技術向來——

always --

向來——會以某種方式偏頗。

in one way or another, biased.

You see, algorithms are by definition sets of rules or steps

在定義上，演算法就是 一組一組的規則或步驟，

that have been designed to achieve a specific result, OK?

設計的目的是要達成 一個特定的結果。

But these sets of rules or steps cannot be objective,

但這些規則或步驟並不客觀，

because they've been designed by human beings

因為它們是由某種 特定文化情境下的人所設計的，

within a specific cultural context

and are shaped by specific cultural values.

且由某些特定的 文化價值觀所形塑出來。

So when machines learn,

所以，機器學習時

they learn from biased algorithms,

會自偏頗的演算法學習，

and they often learn from biased databases as well.

通常也會從偏頗的資料庫中學習。

At the moment, we're seeing the first examples of algorithmic bias.

現在我們已經開始看見 一些偏頗演算法的初始例子，

And some of these examples are frankly terrifying.

當中有些還挺嚇人的。

This year, the AI Now Institute in New York published a report

紐約的 AI Now Institute 今年公佈的一份報告揭露出

that revealed that the AI technologies

用來做預測性維安的人工智慧技術

that are being used for predictive policing

have been trained on "dirty" data.

是用「髒數據」訓練出來的。

This is basically data that had been gathered

收集這些資料的時期，

during historical periods of known racial bias

是歷史上已知很有種族偏見

以及警方作業不透明的時期。

and nontransparent police practices.

Because these technologies are being trained with dirty data,

因為訓練這些技術 所用的資料是髒數據，

they're not objective,

不具備客觀性，

and their outcomes are only amplifying and perpetrating

它們產出的結果

只會放大和犯下警方的偏見和錯誤。

police bias and error.

So I think we are faced with a fundamental problem

所以，我認為我們面臨的 是社會中的根本問題。

in our society.

We are starting to trust technologies when it comes to profiling human beings.

我們開始交由科技技術 來側寫評比人。

We know that in profiling humans,

我們知道在側寫評比人時，

these technologies are always going to be biased

這些技術一定會偏頗，

and are never really going to be accurate.

永遠不會正確。

So what we need now is actually political solution.

所以，現在我們需要的 是政治上的解決方案。

We need governments to recognize that our data rights are our human rights.

我們需要政府認可 我們的資料權和人權。

(Applause and cheers)

（掌聲及歡呼）

Until this happens, we cannot hope for a more just future.

在那之前，我們不用冀望 會有更公正的未來。

I worry that my daughters are going to be exposed

我擔心我的女兒會接觸到

to all sorts of algorithmic discrimination and error.

各種演算法歧視和錯誤。

You see the difference between me and my daughters

我和我女兒的差別在於

is that there's no public record out there of my childhood.

我的童年並沒有 公開的記錄可被取得。

There's certainly no database of all the stupid things that I've done

肯定也沒有資料庫 記錄我在青少女時期

and thought when I was a teenager.

做過的所有蠢事和蠢念頭。

(Laughter)

（笑聲）

But for my daughters this may be different.

但我女兒要面臨的情況可能不同。

The data that is being collected from them today

今天收集到和她們有關的資料，

may be used to judge them in the future

未來可能就會被用來評斷她們，

and can come to prevent their hopes and dreams.

且有可能會漸漸阻擋到 她們的希望和夢想。

I think that's it's time.

我認為該是我們大家 站出來的時候了。

It's time that we all step up.

It's time that we start working together

該是我們開始同心協力，

as individuals,

以個人、組織、 機構的身份攜手合作，

as organizations and as institutions,

and that we demand greater data justice for us

我們要為自己及我們的孩子 爭取更高的資料公平性，

and for our children

before it's too late.

別等到太遲了。

Thank you.

謝謝。

(Applause)

（掌聲）