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Wed Jun 12, 2019, 08:06 AM

Big Data and its enclosure of the commons

Digital dystopias are overdone but inequality is rising. The answer lies in treating data as a commons and Big Data as a collective-action problem.


The fear of the machine is back. Dystopian views of a world without jobs abound as autonomous vehicles, humanoid robots and super computers are thought to replace human workers. Recent progress in artificial intelligence has been stunning: machines are taking phone calls, understanding questions and suggesting solutions—often much faster than a human call-centre worker.

Nevertheless, so far, there is no evidence job loss is mounting. Among the members of the Organisation for Economic Co-operation and Development, unemployment rates are at their lowest since 2006. What has increased is income inequality. The digital economy demands new policy approaches to address this.

Job polarisation

Inclusive growth is threatened in three ways. First, automation due to smart machines and computers is no longer confined to manufacturing but affects jobs—mostly middle-class—in services as well. Entry-level legal services, accounting, logistics and retail will see many tasks replaced by machines which require little oversight or maintenance by employees. Experience suggest that, rather than become unemployed, a growing number of those displaced will compete downwards, leading to further job polarisation

Secondly, the exponential increase in energy consumption induced by complex algorithms suggests that new applications of artificial intelligence will be geared more towards capital-saving and factor-enhancing innovations. Network management—for instance, electricity grids or traffic-control systems for ‘smart’ cities—as well as expert systems in research, agriculture or health care will likely dominate pure automation innovations. This will lead to a further rise in skill-biased technological change, a trend observed over past decades.

Last but not least, digital companies concentrate profits and wealth as they collect and exploit vast amounts of data for their algorithms to individualise prices and product offers. The underlying network externalities allow innovative first movers to gear up, leaving new entrants little chance to compete for market shares or profits. And algorithmic biases—the tendency of machine decisions to replicate the discrimination deeply engrained in the historical data on which these routines are run—compound the inequality challenge.


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