The hidden microwork behind automation

Refugees help power machine learning advances at Microsoft, Facebook, and AmazonRest of World
A woman living in Kenya’s Dadaab, which is among the world’s largest refugee camps, wanders across the vast, dusty site to a central hut lined with computers. Like many others who have been brutally displaced and then warehoused at the margins of our global system, her days are spent toiling away for a new capitalist vanguard thousands of miles away in Silicon Valley. A day’s work might include labelling videos, transcribing audio, or showing algorithms how to identify various photos of cats.

Amid a drought of real employment, “clickwork” represents one of few formal options for Dadaab’s residents, though the work is volatile, arduous, and, when waged, paid by the piece. Cramped and airless workspaces, festooned with a jumble of cables and loose wires, are the antithesis to the near-celestial campuses where the new masters of the universe reside. […]

Microwork comes with no rights, security, or routine and pays a pittance — just enough to keep a person alive yet socially paralyzed. Stuck in camps, slums, or under colonial occupation, workers are compelled to work simply to subsist under conditions of bare life. This unequivocally racialized aspect to the programs follows the logic of the prison-industrial complex, whereby surplus — primarily black — populations [in the United States] are incarcerated and legally compelled as part of their sentence to labor for little to no payment. Similarly exploiting those confined to the economic shadows, microwork programs represent the creep of something like a refugee-industrial complex.

And it’s not just happening in Kenya.

Brazilian workers paid equivalent of 70 cents an hour to transcribe TikToksThe Intercept
For Felipe, the plan to make a little quick money became a hellish experience. With TikTok’s short-form video format, much of the audio that needed transcription was only a few seconds long. The payment, made in U.S. dollars, was supposed to be $14 for every hour of audio transcribed. Amassing the secondslong clips into an hour of transcribed audio took Felipe about 20 hours. That worked out to only about 70 cents per hour — or 3.85 Brazilian reals, about three-quarters of Brazil’s minimum wage.

The minimum wage, however, did not apply to the TikTok transcribers — like many other workers, the transcription job used the gig economy model, a favorite of tech firms. Gig economy workers are not protected by some labor laws; they are considered independent contractors rather than employees or even wage earners. In the case of the TikTok transcribers, who did not even have formal contracts, pay was based on how much transcribing they did rather than the hours they worked.