Invisible Digital Labor: How We Create Capital For Big Internet Companies

Tuned in the digital matrix, we are shaping the global economy and adding to the problem of uncompetitive markets. Rather than being regarded as capital, should data be treated as labor?

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Each and every one of us has multiple jobs, whether we know it or not. Right now, while reading this, you are generating data for Silicon Valley’s biggest giants; improving, upgrading, correcting, advancing, augmenting various artificial intelligence entities, tools of companies like Google and Facebook.

That is what the authors of a fascinating new report claim. According to them, we are all digital laborers. Leonard Goff of Columbia University, Imanol Arrieta Ibarra and Diego Jiménez Hernández of Stanford University, and Jaron Lanier and Glen Weyl of Microsoft have published a report titled “Should We Treat Data as Labor? Moving Beyond Free.”

In it, the authors pose a legitimate question: Rather than being regarded as capital, should data be treated as labor? Published on the Social Science Research Network (SSRN) and featured in the Economist, the report concludes: the relationship between big internet companies and their users has to transform.

As the authors point out, for an AI to improve, it has to be exposed to massive amounts of data, or rather, trained on massive amounts of data. That is where we come in. Internet companies gather data from users every time they read an article, issue a command to Alexa, Google a set of keywords or solve a CAPTCHA (an acronym for Completely Automated Public Turing test to tell Computers and Humans Apart).

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We pay for their services by providing them with the data they crave. By solving a CAPTCHA, we also help improve the AI. For example, a human solves a CAPTCHA by deciphering text from a book and this helps digitize books. A literature classic published on Kindle is, therefore, a product of an internet user’s free, digital labor. AI improves the value generated in the economy, bypasses workers, and reaches companies. Interestingly, authors also point out that the share of GDP paid out to workers in wages and salaries has been steadily declining.

Shaping the global economy

Google currently holds 75.54 percent of the search engine market share. Facebook has 2.07 billion monthly active users. Considered one of the most valuable brands worldwide, Amazon is the leading e-retailer in the United States with close to 136 billion U.S. dollars in 2016 net sales.

Since AI is getting better all the time, it threatens to transform a host of industries, authors claim. Tuned in the digital matrix, we are shaping the global economy and adding to the problem of uncompetitive markets. Would-be startups that might challenge companies like Google and Facebook cannot train their AIs without access to the data only these titans possess. They can, at best, hope to be acquired by those very same companies.

If the economy is to function properly in the future, the role of data-creation has to change, authors claim. And they have quite a radical proposal: data should be treated as labor. In a world where data is treated as labor, or rather property of those who generate it, it would have to be provided to internet companies in exchange for payment.

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In this scenario, data could be sold multiple times and to multiple companies, while at the same time reducing the extent to which data serves as barrier to enter the market. Quite ambitiously, authors of the paper also assert data labor could be seen as useful work in the future of mass automation, so internet companies could potentially generate better data by paying. Companies could, for example, ask individuals to voluntarily share information in exchange for payment.

At the moment, it seems like we are all complicit in creating internet monopolies, whether we like it or not. Although the paper contains essential insights, the hypothesis has some drawbacks, the Economist argues. “Effective negotiation with internet firms might require collective action: and the formation, perhaps, of a ‘data-labor union.’ A union might demand too much in compensation for data, for example, impairing the development of useful AIs. Most important, the authors’ proposal puts front and center the collective nature of value in an AI world. Each person becomes something like an oil well, pumping out the fuel that makes the digital economy run.”