Advocates of "data as labour" think users should be paid for using online services. An anonymous reader shares a report: Labour, like data, is a resource that is hard to pin down. Workers were not properly compensated for labour for most of human history. Even once people were free to sell their labour, it took decades for wages to reach liveable levels on average. History won't repeat itself, but chances are that it will rhyme, Glen Weyl, an economist at Yale University, predicts in "Radical Markets," a provocative new book he has co-written with Eric Posner of the University of Chicago. He argues that in the age of artificial intelligence, it makes sense to treat data as a form of labour. To understand why, it helps to keep in mind that "artificial intelligence" is something of a misnomer. Messrs Weyl and Posner call it "collective intelligence": most AI algorithms need to be trained using reams of human-generated examples, in a process called machine learning. Unless they know what the right answers (provided by humans) are meant to be, algorithms cannot translate languages, understand speech or recognise objects in images. Data provided by humans can thus be seen as a form of labour which powers AI. As the data economy grows up, such data work will take many forms. Much of it will be passive, as people engage in all kinds of activities -- liking social-media posts, listening to music, recommending restaurants -- that generate the data needed to power new services. But some people's data work will be more active, as they make decisions (such as labelling images or steering a car through a busy city) that can be used as the basis for training AI systems. Yet whether such data are generated actively or passively, few people will have the time or inclination to keep track of all the information they generate, or estimate its value. Even those who do will lack the bargaining power to get a good deal from AI firms. But the history of labour offers a hint about how things could evolve: because historically, if wages rose to acceptable levels, it was mostly due to unions. Similarly, Mr Weyl expects to see the rise of what he calls "data-labour unions," organisations that serve as gatekeepers of people's data. Like their predecessors, they will negotiate rates, monitor members' data work and ensure the quality of their digital output, for instance by keeping reputation scores. Unions could funnel specialist data work to their members and even organise strikes, for instance by blocking access to exert influence on a company employing its members' data. Similarly, data unions could be conduits channelling members' data contributions, all while tracking them and billing AI firms that benefit from them.
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