Millions of Workers Are Training AI Models for Pennies


In 2016, Oskarina Fuentes got a tip from a friend that seemed too good to be true. Her life in Venezuela had become a struggle: Inflation had hit 800 percent under President Nicolás Maduro, and the 26-year-old Fuentes had no stable job and was balancing multiple side hustles to survive.

Her friend told her about Appen, an Australian data services company that was looking for crowdsourced workers to tag training data for artificial intelligence algorithms. Most internet users will have done some form of data labeling: identifying images of traffic lights and buses for online captchas. But the algorithms powering new bots that can pass legal exams, create fantastical imagery in seconds, or remove harmful content on social media are trained on datasets—images, video, and text—labeled by gig economy workers in some of the world’s cheapest labor markets.

Appen’s clients have included Amazon, Facebook, Google, and Microsoft, and the company’s 1 million contributors are just a part of a vast, hidden industry. The global data collection and labeling market was valued at $2.22 billion in 2022 and is expected to grow to $17.1 billion by 2030, according to consulting firm Grand View Research. As Venezuela slid into an economic catastrophe, many college-educated Venezuelans like Fuentes and her friends joined crowdsourcing platforms like Appen.

For a while, it was a lifeline: Appen meant Fuentes could work from home at any hour of the day. But then the blackouts started—power cutting out for days on end. Left in the dark, Fuentes was unable to pick up tasks. “I couldn’t take it anymore,” she says, speaking in Spanish. “In Venezuela, you don’t live, you survive.” Fuentes and her family migrated to Colombia. Today she shares an apartment with her mother, her grandmother, her uncles, and her dog in the Antioquia region.

Appen is still her sole source of income. Pay ranges from 2.2 cents to 50 cents per task, Fuentes says. Typically, an hour and a half of work will bring in $1. When there are enough tasks to work a full week, she earns approximately $280 per month, almost meeting Colombia’s minimum wage of $285. But filling out a week with tasks is rare, she says. Down days, which have become increasingly common, will bring in no more than $1 to $2. Fuentes works on a laptop from her bed, glued to her computer for over 18 hours a day to get the first pick of tasks that could arrive at any time. Given Appen’s international clients, days begin when the tasks come out, which can mean 2 am starts.

It’s a pattern that’s being repeated across the developing world. Labeling hot spots in east Africa, Venezuela, India, the Philippines, and even refugee camps in Kenya and Lebanon’s Shatila camps offer cheap labor. Workers pick up microtasks for a few cents each on platforms like Appen, Clickworker, and Scale AI, or sign onto short-term contracts in physical data centers like Sama’s 3,000-person office in Nairobi, Kenya, which was the subject of a Time investigation into the exploitation of content moderators. The AI boom in these places is no coincidence, says Florian Schmidt, author of Digital Labour Markets in the Platform Economy. “The industry can flexibly move to wherever the wages are lowest,” he says, and can do it far quicker than, for example, textile manufacturers.



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