The digital proletariat 經濟學人.Jan 12th 2018

The digital proletariatShould internet firms pay for the data users currently give away?

And, as a new paper proposes, should the data-providers unionise?

（標題）互聯網公司應該為現在用戶泄露的數據付錢嗎？

（引言）據一篇新論文的觀點，數據提供者也應該成立組織嗎？

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 musttake account ofthis, and change the relationship between big internet companies and their users.

不管你有沒有意識到，你有著多重工作。第一件事就是當你在早晨打開你的手機時就有大量的數據生成，這些數據組成了矽谷最重要的資源。在一篇新（發布）的優秀經濟學論文中提到：對於此，我們至少應該去思考數據創造在經濟（發展）中的作用。作者認為，我們都是信息的勞工，幫助Google和Facebook這樣的公司產生了有可能性的經濟效應。如果經濟在未來有合適的功能——如果科技失業的危機能被避免——我們必須考慮到這點，改變大型互聯網公司和用戶之間的關係。

Artificial intelligence (AI) is getting better all the time, andstands poisedto transforma host ofindustries, 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 alsohoover upvaluable data from users through the use of tools like reCAPTCHA,whichask visitors to solve problems that are easy for humans but hard for AIs,such asdeciphering text from books that machines are unable to parse.That does not justscreen outmalicious bots, but also helps digitise books. People 「pay」 for useful free services by providing firms with the data they crave.

作者（斯坦福大學的Imanol Arrieta Ibarra 和 Diego Jiménez Hernández ，哥倫比亞大學的Leonard Goff，微軟公司的 Jaron Lanier 和 Glen Weyl）提出，人工智慧每時每刻都在進步，穩定地立足並使一大批工業轉型。但是為了學習駕駛車輛或者識別面部，智能機器運轉時其中的演算法必須要求大量的數據不斷地訓練。用戶每次在谷歌查詢結果、對Alexa(亞馬遜的語音機器人)發布命令或問問題時，所產生的數據都被互聯網公司收集起來。不僅如此，他們還使用一些像reCAPTCHA(驗證碼)這樣的工具，在用戶使用時抓取有價值的數據，這個工具要求用戶解決一些簡單但對於AI很困難的題目，比如從書中破譯出機器無法解析的文字。這不僅屏蔽了惡意的機器，也幫助了書本的數字化。公司所渴望的數據，都由人們以「免費」且有用的服務來提供。

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 beacquired bythose very same titans, adding to the problem of uncompetitive markets.

這些數據成了公司資本的一部分，或者說是一個可怕的競爭優勢來源。潛在的創業公司要成為互聯網巨頭所面臨的挑戰就是不能訪問那些只有大公司才有的數據，這些數據能用來訓練他們的AI。他們最大的希望就是被同行巨頭收購，解決市場競爭力不足的問題。

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.

作者說，目前，AI的對生產力提升的貢獻還不大，部分原因是因為免費數據的現狀，其限制了數據統籌的質量。公司要發展實用性高的AI應用總是希望有足夠的數據，或者想出一些方法去哄用戶免費為他們提供更好的數據。比如，他們總是隨機性地糾纏用戶去為數據打標籤，像這些在網站上用戶需要模糊破譯，還希望他們在煩躁和匆忙的時候不會弄錯。

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 economyaccrues toprofitable 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.

儘管如此，隨著AI的進步，許多工作崗位在技術的提升之下變得岌岌可危，甚至在為公司帶來經濟增長這方面所產生的價值要大於工人。作者指出，曾一度被認為相對穩定的工資和薪水，其在GDP中的部分也已經於過去幾十年中有所減少。

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 exchange for payment.In such a world, user data might be sold multiple times, to multiple firms, reducing the extent to which data setsserve asbarriers to entry. 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.

為了解決這些問題，作者有全面的建議方案。數據更應該被視作勞動力，而不是被視為資本，更專業的說，被視作數據產生者的財產，只有在以錢作為交換時他們才會同意提供給公司。在這樣的世界裡，用戶數據可能多次被賣給多個公司，減少了數據集訪問的困難程度。為了用戶的數據支付給他們錢，將會幫助AI公司經濟流通，也可能生成更好的數據。比起猜測一個人最多只是鑒於

他們在購物中心的周邊漫遊，不如在付錢後直接要求他分享購物信息，比如逛了哪家店，看了哪些產品。可能最野心勃勃的是，作者們想把數據勞動作為一個有用的工作，賦予與有償工作相同的尊嚴：在大量自動化的未來這也是一個值得期望的「副作用」。

The authors』 ideas needfleshing 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 substantialmonetary inducement(and would their data be valuable enough for firms to pay a substantial amount)? Might not such systems attractdata mercenaries, spamming firms with useless junk data simply to make a quick buck?

作者的觀點需要充實；他們的論文儘管發人深省，但也只有5頁。其中一些想像計劃好像不太現實。在沒有可觀金錢的誘惑下，人們真的會對花時間描述他們的早晨日常和辦公習慣感興趣嗎（或者對公司來說，支付了可觀的金錢後，他們的數據真的值得嗎）？這樣的系統能不會吸引來為了簡單掙快錢而提供沒用的垃圾數據的數據水軍和垃圾郵件公司嗎？

Nothing to use but your brains

Still, the paper contains essential insights which should frame discussion of data』s role in the economy. One concerns the imbalance of power in the market for data.Thatstemspartly from concentrationamong big internet firms.But it is also because, though data may be extremely valuable in aggregate, an individual』s personal data typically are not. For one Facebook user to threaten to deprive Facebook of his data is no threat at all. So effective negotiation with internet firms might require collective action: and the formation, perhaps, of a 「data-labour union」.

運用你的大腦

但是，這論文包含的基本觀點應該有框架地討論經濟中數據的作用。一個是考慮到數據市場中權力的不平衡，有一大部分（數據）集中在大型互聯網公司。但這也是因為一個人的私人數據並不值錢，而積累大量的數據就會非常有價值。一個Fackbook用戶威脅Facebook要收回他的數據，都不足以構成威脅。所以與互聯網公司的高效協商需要集體行動：可能組成一個「數據勞動工會」。

This might have drawbacks. A union might demand too much in compensation for data, for example, impairing the development of useful AIs. It might make all user data freely available and extract compensation by demanding a share of firms』 profits; that wouldrule outthe pay-for-data labour model the authors see as vital to improving data quality.Still, a data union holds potential as a way ofsolidifyingworkerpowerat a time when conventional unions struggle to remain relevant.

但這樣也可能有缺點。一個工會可能會為數據索要過多的補償，比如損害一個有用的AI機構的發展。它可能要求所有用戶的數據免費提供，再從公司的利潤中抽取補償；這可能違背了為數據付款的勞動模型的初衷——作者最主要是想提升數據的質量。但是在傳統工會為保持相關性做鬥爭的時候，數據工會也有鞏固工人力量的潛力。

Most important, the authors』 proposal puts front and centre the collective nature of value in an AI world. Each person becomes something like an oil well,pumping outthe fuel that makes the digital economy run.Both fairness and efficiency demand that the distribution of income generated by that fuel should be shared moreevenly, according to our contributions.The tricky part is working out how.

至關重要的是，在AI時代，作者的建議將集體的自然價值放在了前面並為其正名。每個人都能像一口油井，流出使數字經濟發展的燃料。根據我們的貢獻，這些燃料產生的收入分配應該力求公平和效率，均勻地分攤。然而棘手的部分就是如何實施（這些工作）。

Edit by Lutra, Jan 25th 2018.

喜歡這篇文章嗎？立刻分享出去讓更多人知道吧！