評論
打印
Still, the paper contains essential insights which should frame discussion of data's role in the economy.
盡管如此，這篇論文仍然包含一些深刻的見解，它們應當成為數據在經濟中的角色之討論的框架。
One concerns the imbalance of power in the market for data.
其中之一關注的是數據市場中的實力的不平衡。
That stems partly from concentration among big internet firms.
這種不平衡部分地源于大型互聯網公司間的集中。
But it is also because, though data may be extremely valuable in aggregate, an individual's personal data typically are not.
但是，它也部分是因為，盡管數據可能總體很有價值，但是，某一個人的個人數據一般沒有價值。
For one Facebook user to threaten to deprive Facebook of his data is no threat at all.
因為，一位臉書用戶要剝奪臉書其數據的威脅根本算不上威脅。
So effective negotiation with internet firms might require collective action: and the formation, perhaps, of a “data-labour union”.
因而，與互聯網公司的有效談判可能要求集體行為：可能還有“數據勞動力工會”的組建。
This might have drawbacks.
這或許有些障礙。
A union might demand too much in compensation for data, for example, impairing the development of useful AIs.
例如，工會可能在數據補償方面要價太高，妨礙了有用AI的開發。
It might make all user data freely available and extract compensation by demanding a share of firms' profits; that would rule out the pay-for-data labour model the authors see as vital to improving data quality.
它可能讓全部的用戶數據可免費獲得并從要求一定比例的公司利潤中抽取補償。這會把作者認為是對提高數據質量至關重要的付費數據模型排除在外。
Still, a data union holds potential as a way of solidifying worker power at a time when conventional unions struggle to remain relevant.
然而，在一個傳統工會為留住其意義而奮斗的時代，作為團結工人力量的一種方式，數據工會大有潛力。
Most important, the authors' proposal puts front and centre the collective nature of value in an AI world.
最重要的是，作者的建議把價值在AI世界中的集體屬性提將出來將其置于中心位置。
Each person becomes something like an oil well, pumping out the fuel that makes the digital economy run.
每個人都成為像油井一樣的東西，擠出讓數字經濟運轉的燃料。
Both fairness and efficiency demand that the distribution of income generated by that fuel should be shared more evenly, according to our contributions.
公平和效率雙雙要求由這種燃料所產生的收入的分配應當根據我們的貢獻得以更均衡地分享。
The tricky part is working out how.
難辦的是找到具體的辦法。
譯文來源考研英語時事閱讀
