听力课堂TED音频栏目主要包括TED演讲的音频MP3及中英双语文稿，供各位英语爱好者学习使用。本文主要内容为演讲MP3+双语文稿:科技公司知道你的孩子的哪些事？，希望你会喜欢！

【演讲者及介绍】Veronica Barassi

人类学家Veronica Barassi表示，你和你的家人每天使用的数字平台--从在线游戏到教育应用和医疗门户网站--可能正在收集和出售你孩子的数据。巴拉西分享了她令人瞠目结舌的研究，她敦促家长们要三思而不是盲目接受数字条款和条件，并要求保护他们的数据，以确保他们孩子的数据不会歪曲他们的未来。

【演讲主题】科技公司知道你的孩子的哪些事？ What tech companies know about your kids？

【中英文字幕】

翻译者 Leslie Gauthier 校对者 Joanna Pietrulewicz

00:04

Every day, every week, we agree to terms and conditions. And when we do this, we provide companies with the lawful right to do whatever they want with our data and with the data of our children. Which makes us wonder: how much data are we giving away of children, and what are its implications?

每一天， 每一个星期， 我们都会同意各种服务条款。 每当我们这样做， 我们其实就赋予了公司法律上的权利， 用我们的数据去做任何事， 也包括我们孩子的数据。 这难免使我们感到困惑： 我们到底提供了多少 关于孩子的数据， 它们的用途又是什么？

00:30

I'm an anthropologist, and I'm also the mother of two little girls. And I started to become interested in this question in 2015 when I suddenly realized that there were vast -- almost unimaginable amounts of data traces that are being produced and collected about children. So I launched a research project, which is called Child Data Citizen, and I aimed at filling in the blank.

是个人类学家， 也是两个女孩的母亲。 2015 年，我开始关注这个问题， 当时我突然发现很多科技公司 从孩子那里搜集到了 庞大到无法想象的数据信息。 所以我启动了一个研究项目， 叫“儿童数据市民”， 希望能够填补空缺的信息。

00:56

Now you may think that I'm here to blame you for posting photos of your children on social media, but that's not really the point. The problem is way bigger than so-called "sharenting." This is about systems, not individuals. You and your habits are not to blame.

现在，你们有可能以为我在责怪你们 在社交网络上传了孩子的照片， 但是这不是重点。 实际问题比分享要严重得多。 这事关系统，而不是个人。 你的行为习惯并没有错。

01:16

For the very first time in history, we are tracking the individual data of children from long before they're born -- sometimes from the moment of conception, and then throughout their lives. You see, when parents decide to conceive, they go online to look for "ways to get pregnant," or they download ovulation-tracking apps. When they do get pregnant, they post ultrasounds of their babies on social media, they download pregnancy apps or they consult Dr. Google for all sorts of things, like, you know -- for "miscarriage risk when flying" or "abdominal cramps in early pregnancy." I know because I've done it -- and many times. And then, when the baby is born, they track every nap, every feed, every life event on different technologies. And all of these technologies transform the baby's most intimate behavioral and health data into profit by sharing it with others.

历史上首次， 我们开始追踪孩子的个人数据， 从他们出生之前—— 有时候是从受孕开始， 然后贯穿他们的一生。 通常，当家长决定要一个孩子， 他们会在网上搜索 “怎么怀孕”， 或者下载排卵期追踪软件。 等到真的怀孕了， 他们会在社交网络上 发布宝宝的超音波图像， 下载关于怀孕的软件， 或者在谷歌上搜索相关信息。 比如， “乘飞机时的流产风险” 或者“怀孕早期的腹痛”。 我知道这些， 因为我也有过类似的经历， 而且是很多次。 等到宝宝出生后， 他们会用不同的技术 记录每个午觉、 每次喂食和每个重要时刻。 所有这些技术 都会通过把宝宝的资料分享给别人 从而换取利润。

02:20

So to give you an idea of how this works, in 2019, the British Medical Journal published research that showed that out of 24 mobile health apps, 19 shared information with third parties. And these third parties shared information with 216 other organizations. Of these 216 other fourth parties, only three belonged to the health sector. The other companies that had access to that data were big tech companies like Google, Facebook or Oracle, they were digital advertising companies and there was also a consumer credit reporting agency. So you get it right: ad companies and credit agencies may already have data points on little babies. But mobile apps, web searches and social media are really just the tip of the iceberg, because children are being tracked by multiple technologies in their everyday lives. They're tracked by home technologies and virtual assistants in their homes. They're tracked by educational platforms and educational technologies in their schools. They're tracked by online records and online portals at their doctor's office. They're tracked by their internet-connected toys, their online games and many, many, many, many other technologies.

先给各位举一个例子， 在 2019 年， 英国医学杂志发布了一项研究： 在 24 个健康类的手机软件里， 有 19 个把用户资料 分享给了第三方， 而这些第三方又分享给了 216 个其他的组织。 而这 216 个第四方机构， 只有三个属于健康类机构， 其他的则是大型科技公司， 比如谷歌，脸书或甲骨文， 都是数据广告类的公司， 而且还有消费信贷的报告机构。 所以你的猜测是对的： 广告公司和信贷机构 已经有了宝宝们的数据。 但是手机软件、网站搜索和社交媒体 只是冰山一角， 因为孩子们的日常生活 已经被很多科技追踪了。 他们被家里的设备和虚拟助手追踪， 他们被教育网站 和学校里的教育技术追踪。 他们被诊所的 网上记录和门户网站追踪。 他们也在被连网的玩具、 在线游戏 和很多很多其他的技术追踪。

03:43

So during my research, a lot of parents came up to me and they were like, "So what? Why does it matter if my children are being tracked? We've got nothing to hide." Well, it matters. It matters because today individuals are not only being tracked, they're also being profiled on the basis of their data traces. Artificial intelligence and predictive analytics are being used to harness as much data as possible of an individual life from different sources: family history, purchasing habits, social media comments. And then they bring this data together to make data-driven decisions about the individual. And these technologies are used everywhere. Banks use them to decide loans. Insurance uses them to decide premiums. Recruiters and employers use them to decide whether one is a good fit for a job or not. Also the police and courts use them to determine whether one is a potential criminal or is likely to recommit a crime.

在我的研究过程中， 很多家长问我，“那又怎么样？ 就算我的孩子被追踪，那又怎么样？ 我们又没什么见不得人的秘密。” 但是，这真的很重要。 因为现如今，个人信息不仅仅被追踪， 还会被用来创建网络个人档案。 那些公司会用人工智能和预测分析 从不同渠道搜集越来越多的 个人数据： 家庭历史、购物习惯和社交媒体评论， 然后将这些信息结合在一起 去做出关于你的决定。 这些技术几乎无处不在。 银行利用这些信息 决定批准谁的贷款， 保险公司用它们决定保费额度， 招聘人员和雇主用它们 来决定你们到底适不适合某个工作。 警察和法庭也利用它们 去决定这个人是不是罪犯， 或者有没有可能犯罪。

04:55

We have no knowledge or control over the ways in which those who buy, sell and process our data are profiling us and our children. But these profiles can come to impact our rights in significant ways.

这些购买、售卖 和处理我们信息的人 究竟如何调查我们和我们的孩子， 我们对此一无所知， 也没有任何控制权。 但这些信息会 严重影响我们的权益。

05:12

To give you an example, in 2018 the "New York Times" published the news that the data that had been gathered through online college-planning services -- that are actually completed by millions of high school kids across the US who are looking for a college program or a scholarship -- had been sold to educational data brokers. Now, researchers at Fordham who studied educational data brokers revealed that these companies profiled kids as young as two on the basis of different categories: ethnicity, religion, affluence, social awkwardness and many other random categories. And then they sell these profiles together with the name of the kid, their home address and the contact details to different companies, including trade and career institutions, student loans and student credit card companies. To push the boundaries, the researchers at Fordham asked an educational data broker to provide them with a list of 14-to-15-year-old girls who were interested in family planning services. The data broker agreed to provide them the list. So imagine how intimate and how intrusive that is for our kids. But educational data brokers are really just an example. The truth is that our children are being profiled in ways that we cannot control but that can significantly impact their chances in life.

举个例子， 2018 年《纽约时报》 发布的一则新闻称， 由线上大学规划服务 搜集的数据—— 这些数据都来自 全美数百万正在寻找 大学项目或奖学金的高中生—— 已经被售卖给了教育数据经纪人。 福特汉姆的研究人员在对一些 教育数据经纪人进行分析之后透露， 这些公司根据以下类别 对不小于两岁的孩子 进行了分组： 种族、宗教、家庭富裕程度、 社交恐惧症， 以及很多其他的随机分类。 然后他们会将这些资料， 以及孩子的名字、 地址和联系方式 出售给不同的公司， 包括贸易和职业发展机构， 学生贷款 和学生信用卡公司。 更夸张的是， 研究人员要求教育数据经纪人 提供一份对家庭生育服务感兴趣， 年龄在 14 至 15 岁的少女名单。 数据经纪人同意了。 所以不难想象，我们孩子的隐私 得到了何等程度的侵犯。 但是教育数据经纪人的例子只是冰山一角。 诚然，孩子们的信息 正以不可控的方式被人操纵着， 但这会极大地影响他们以后的人生。

06:57

So we need to ask ourselves: can we trust these technologies when it comes to profiling our children? Can we? My answer is no. As an anthropologist, I believe that artificial intelligence and predictive analytics can be great to predict the course of a disease or to fight climate change. But we need to abandon the belief that these technologies can objectively profile humans and that we can rely on them to make data-driven decisions about individual lives. Because they can't profile humans. Data traces are not the mirror of who we are. Humans think one thing and say the opposite, feel one way and act differently. Algorithmic predictions or our digital practices cannot account for the unpredictability and complexity of human experience.

所以我们要扪心自问： 这些搜集孩子们信息的技术 还值得信任吗？ 值得吗？ 我的答案是否定的。 作为一个人类学家， 我相信人工智能和 预测分析可以很好的 预测疾病的发展过程 或者对抗气候变化。 但是我们需要摒弃 这些技术可以客观的分析人类数据， 我们能够以数据为依据做出 关于个人生活的决定 这一想法。 因为它们做不到。 数据无法反映我们的真实情况。 人类往往心口不一， 言行不一。 算法预测或者数据实践 无法应对人类经验的 不可预测性和复杂性。

07:51

But on top of that, these technologies are always -- always -- in one way or another, biased. You see, algorithms are by definition sets of rules or steps that have been designed to achieve a specific result, OK? But these sets of rules or steps cannot be objective, because they've been designed by human beings within a specific cultural context and are shaped by specific cultural values. So when machines learn, they learn from biased algorithms, and they often learn from biased databases as well.

但是在此之上， 这些科技总是—— 总是—— 以这样或那样的方式存在偏见。 要知道，算法的定义是 被设计成实现一个具体结果的 很多套规则或步骤，对吧？ 但是这些都不是客观的， 因为它们都是 由带有特殊文化背景， 被特殊文化价值所塑造的人类 设计出来的。 所以当机器在学习的时候， 它们利用的是带有偏见的算法， 以及往往同样带有偏见的数据。

08:29

At the moment, we're seeing the first examples of algorithmic bias. And some of these examples are frankly terrifying. This year, the AI Now Institute in New York published a report that revealed that the AI technologies that are being used for predictive policing have been trained on "dirty" data. This is basically data that had been gathered during historical periods of known racial bias and nontransparent police practices. Because these technologies are being trained with dirty data, they're not objective, and their outcomes are only amplifying and perpetrating police bias and error.

如今，我们已经看到了 第一批算法偏见的例子， 其中有一些真的很可怕。 今年，位于纽约的 人工智能现在研究所（AI Now Institute） 发表的一份报告揭示了 预测警务领域的人工智能技术 是使用非常糟糕的数据进行训练的。 这些数据基本上都是 在历史上存在已知的种族偏见 和不透明的警察行为时期 收集的数据。 因为这些技术都是 用这类数据训练的， 它们无法做到客观， 结果只是放大和进一步深化 警察的偏见和错误。

09:16

So I think we are faced with a fundamental problem in our society. We are starting to trust technologies when it comes to profiling human beings. We know that in profiling humans, these technologies are always going to be biased and are never really going to be accurate. So what we need now is actually political solution. We need governments to recognize that our data rights are our human rights.

所以我觉得我们是在面对社会中的 一个基本问题。 我们正在放心大胆的 用各种技术对人类信息进行分析。 我们知道在这方面， 这些技术总是有偏见的， 结果也永远不可能准确。 所以我们现在需要 一个政治层面的解决方案。 我们需要让政府认识到， 我们的数据权利也是人权。

09:43

(Applause and cheers)

（鼓掌和欢声）

09:51

Until this happens, we cannot hope for a more just future. I worry that my daughters are going to be exposed to all sorts of algorithmic discrimination and error. You see the difference between me and my daughters is that there's no public record out there of my childhood. There's certainly no database of all the stupid things that I've done and thought when I was a teenager.

在这样的转变发生之前， 我们无法期待一个更加公平的未来。 我担心我的女儿们会暴露在 各种算法的歧视与错误判断中。 我和我女儿的区别就在于， 我的童年并没有公开的记录， 当然，我十几岁时做过的傻事 和那些荒唐的想法也没有被记录。

10:14

(Laughter)

（笑声）

10:17

But for my daughters this may be different. The data that is being collected from them today may be used to judge them in the future and can come to prevent their hopes and dreams.

但是我的女儿们就不同了。 今天从她们那里搜集的数据 在将来有可能被用来 评判她们的未来， 并可能阻止她们的希望和梦想。

10:32

I think that's it's time. It's time that we all step up. It's time that we start working together as individuals, as organizations and as institutions, and that we demand greater data justice for us and for our children before it's too late.

我觉得是时候了， 是时候 采取行动—— 无论是个人， 还是组织和机构—— 在一切还来得及之前就开展合作， 为我们和我们的孩子 争取更大程度的 数据公正。

10:47

Thank you.

谢谢大家！

10:48

(Applause)

（掌声）