YOU have multiple jobs, whether you know it or not. Most begin first thing in the morning, when you pick up your phone and begin generating the data that make up Silicon Valley's most important resource. That, at least, is how we ought to think about the role of data-creation in the economy, according to a fascinating new economics paper.We are all digital labourers, helping make possible the fortunes generated by firms like Google and Facebook, the authors argue. If the economy is to function properly in the future—and if a crisis of technological unemployment is to be avoided—we must take account of this, and change the relationship between big internet companies and their users.
不管知道与否，人们身兼多项工作。大多数人是在早晨拿起手机并开始生产构成硅谷最重要资源的数据时开始第一项工作的。据一篇引人入胜的新经济学论文，这至少是我们应当怎么去思考数据创造在经济中的角色的方式。作者指出，我们全是数据劳动力，致使由谷歌和脸书等公司所创造的财富成为可能。如果经济要想在未来正常地运转——如果一场技术性失业要想得以避免——我们必须对此有所重视，并改变大型互联网公司与其用户之间的这种关系。
Artificial intelligence (AI) is getting better all the time, and stands poised to transform a host of industries, say the authors (Imanol Arrieta Ibarra and Diego Jiménez Hernández, of Stanford University, Leonard Goff, of Columbia University, and Jaron Lanier and Glen Weyl, of Microsoft). But, in order to learn to drive a car or recognise a face, the algorithms that make clever machines tick must usually be trained on massive amounts of data. Internet firms gather these data from users every time they click on a Google search result, say, or issue a command to Alexa. They also hoover up valuable data from users through the use of tools like reCAPTCHA, which ask visitors to solve problems that are easy for humans but hard for AIs, such as deciphering text from books that machines are unable toparse. That does not just screen out malicious bots, but also helps digitise books. People “pay” for useful free services by providing firms with the data they crave.
这篇论文的作者们（斯坦福大学的
Imanol Arrieta Ibarra
和
Diego Jim
é
nez Hern
á
ndez
，哥伦比亚大学的
LeonardGoff
和
微软公司的
Jaron Lanier and Glen Weyl
）写道，人工智能（
AI
）一直都在完善之中并将彻底改变一些行业。但是，为了学习开车或是识别人脸，让聪明机器运行的算法必须通常接受海量数据的培训。互联网公司从用户对谷歌搜索结果的每一次点击或是向
Alexa
发出的每一个指令中那里收集这些数据。它们还通过
reCAPTCHA
之类的工具的使用从用户那里搜集有价值的数据。这类工具要求访问者解决对人类来说很容易但对
AI
却很难的问题，例如辨认来自机器无法辨析的书籍中的文本。这不仅将恶意机器人屏蔽在外，还有助于书籍的数字化。人们通过给公司提供它们梦寐以求的数据为各种免费服务“付费”。
These data become part of the firms' capital, and, as such, a fearsome source of competitive advantage. Would-be startups that might challenge internet giants cannot train their AIs without access to the data only those giants possess. Their best hope is often to be acquired by those very same titans, adding to the problem of uncompetitive markets.
这些数据成为公司资本的一部分，而且就其本身而言，也是一种可怕的竞争优势之源头。可能挑战互联网巨头的准初创公司不可能在不访问只有这些巨头们拥有的数据的情况下训练它们的
AI
。它们的最佳希望经常是被这些巨头收购，加重了非竞争市场问题。
That, for now, AI's contributions to productivity growth are small, the authors say, is partly because of the free-data model, which limits the quality of data gathered. Firms trying to develop useful applications for AI must hope that the data they have are sufficient, or come up with ways to coax users into providing them with better information at no cost. For example, they must pester random people—like those blur-deciphering visitors to websites—into labelling data, and hope that in their annoyance and haste they do not make mistakes.
作者称，
AI
对生产力增长的贡献目前微乎其微，部分是因为这种免费数据的模式，它限制了收集到的数据的质量。试图为
AI
开发有用应用的公司必须指望自己拥有的数据足够多。不然，就得想法不花一分钱地去诱导用户给他们提供更好的信息。例如，他们必须麻烦随机人群——如那些涂抹辨识码的网站访问者——为数据贴上标签，而且还要希望他们在不耐烦和匆忙之中不出错。
Even so, as AI improves, the amount of work made vulnerable to displacement by technology grows, and ever more of the value generated in the economy accrues to profitable firms rather than workers. As the authors point out, the share of GDP paid out to workers in wages and salaries—once thought to be relatively stable—has already been declining over the past few decades.
即便如此，随着
AI
的发展，被技术搞得对失去位置弱不禁风的工作的数量日渐增加，在经济体中被生产出来的价值越来越多地向盈利公司而不是工人累积。正如作者所指出的那样，以工资和薪水形式付给工人的
GDP
比例——曾经被认为是相对稳定的——在过去的几十年中，早已在不断下降之中。
To tackle these problems, they have a radical proposal. Rather than being regarded as capital, data should be treated as labour—and, more specifically, regarded as the property of those who generate such information, unless they agree to provide it to firms in exchangefor payment. In such a world, user data might be sold multiple times, to multiple firms, reducing the extent to which data sets serve as barriers toentry. Payments to users for their data would help spread the wealth generated by AI. Firms could also potentially generate better data by paying. Rather than guess what a person is up to as they wander around a shopping centre, for example, firms could ask individuals to share information on which shops were visited and which items were viewed, in exchange for payment. Perhaps most ambitiously, the authors muse that data labour could come to be seen as useful work, conferring the same sort of dignity as paid employment: a desirable side-effect in a possible future of mass automation.
为了解决这些问题，他们提出一项激进的建议。与其被视为资本，数据应当被作为劳动力对待——更具体地说，应当被看作是生产此类信息的人的财产，除非他们同意将其提供给公司，以换取报酬。在这样一个世界中，用户数据或许被多次出售，或是被出售给多家公司，降低了数据集充当准入门槛的标准。为数据而给用户付费会有助于传播由
AI
生产的财富。公司还可能通过付费潜在地生产更好的数据。例如，较之猜测人们在逛购物中心时在忙什么，公司可以要求人们分享哪些店铺得到光顾、哪些商品被浏览了的信息，以换取报酬。作者沉思道，或许，最野心勃勃的是，数据劳动可能最终被视为有用的工作，赋予同有偿劳动一样的尊严：在可能的大规模自动化未来中，这是一种值得期待的附带效应。
The authors' ideas need fleshing out; their paper, thought-provoking though it is, runs to only five pages. Parts of the envisioned scheme seem impractical. Would people really be interested in taking the time to describe their morning routine or office habits without a substantial monetary inducement (and would their data be valuable enough for firms to pay a substantial amount)? Might not such systems attract data mercenaries, spamming firms with useless junk data simply to make a quick buck?
作者的思想需要完善和充实；他们的论文，尽管具有启发性，只有
5
页纸之多。设想中的项目的某些部分似乎不切实际。在没有实质性的金钱诱惑下，人们真得会对花费时间
来描述他们早晨的惯例和办公室的习惯感兴趣（而且他们的数据会值得公司支付一大笔钱）吗？这类系统是否有可能招来单纯是想用毫无用处的数据挣一笔快钱的数据水军或者垃圾邮件公司呢？
