One concerns the imbalance of power in the market for data. 其中之一关注的是数据市场中的实力的不平衡。

　　That stems partly from concentration among big internet firms. 这种不平衡部分地源于大型互联网公司间的集中。

　　But it is also because, though data may be extremely valuable in aggregate, an individual's personal data typically are not. 但是，它也部分是因为，尽管数据可能总体很有价值，但是，某一个人的个人数据一般没有价值。

　　For one Facebook user to threaten to deprive Facebook of his data is no threat at all. 因为，一位脸书用户要剥夺脸书其数据的威胁根本算不上威胁。

　　So effective negotiation with internet firms might require collective action: and the formation, perhaps, of a “data-labour union”. 因而，与互联网公司的有效谈判可能要求集体行为：可能还有“数据劳动力工会”的组建。

　　This might have drawbacks. 这或许有些障碍。

　　A union might demand too much in compensation for data, for example, impairing the development of useful AIs. 例如，工会可能在数据补偿方面要价太高，妨碍了有用AI的开发。

　　It might make all user data freely available and extract compensation by demanding a share of firms' profits;  它可能让全部的用户数据可免费获得并从要求一定比例的公司利润中抽取补偿。

　　that would rule out the pay-for-data labour model the authors see as vital to improving data quality. 这会把作者认为是对提高数据质量至关重要的付费数据模型排除在外。

　　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，在一个传统工会为留住其意义而奋斗的时代，作为团结工人力量的一种方式，数据工会大有潜力。

　　Most important, the authors' proposal puts front and centre the collective nature of value in an AI world. 最重要的是，作者的建议把价值在AI世界中的集体属性提将出来将其置于中心位置。

　　Each person becomes something like an oil well, pumping out the fuel that makes the digital economy run. 每个人都成为像油井一样的东西，挤出让数字经济运转的燃料。

　　Both fairness and efficiency demand that the distribution of income generated by that fuel should be shared more evenly, according to our contributions. 公平和效率双双要求由这种燃料所产生的收入的分配应当根据我们的贡献得以更均衡地分享。

　　The tricky part is working out how. 难办的是找到具体的办法。