General Intelligence

The A.I. Industry Is Exploiting Gig Workers Around the World — Sometimes for Just $8 a Day

A new paper sheds light on the industry’s troubling relationship with the global gig economy

Dave Gershgorn
Published in
3 min readFeb 12, 2021
Photo illustration; Image source: metamorworks/Getty Images

OneZero’s General Intelligence is a roundup of the most important artificial intelligence and facial recognition news of the week.

Modern artificial intelligence relies on algorithms processing millions of examples or images or text. A picture of a bird in an A.I. dataset would be manually tagged “bird” so that the algorithm associated aspects of that image with the category “bird.”

The process of tagging this data, by hand, scaled to the millions, is time-consuming and mind-numbingly monotonous.

Much of this work is done outside the United States and other Western countries and exploits workers from around the world, according to a new paper from Princeton, Cornell, University of Montreal, and the National Institute of Statistical Sciences.

Data-labeling companies like Sama (formerly Samasource), Mighty AI, and Scale AI use labor from sub-Saharan Africa and Southeast Asia, paying employees as little as $8 per day. Meanwhile, these companies earn tens of millions of dollars in revenue per year.

Take Amazon Mechanical Turk, an online gig working platform where anyone in the world can log on and perform simple tasks for a few cents each. Until 2019, Mechanical Turk required a U.S. bank account to get paid, meaning that anyone working for the platform without access to U.S. banking wouldn’t even be paid in legal currency. Instead they were compensated in Amazon gift cards.

One of the most impactful datasets in the history of artificial intelligence, ImageNet, relied on Mechanical Turk workers who were paid $2 per hour, according to the paper.

Furthermore, the data being tagged has been selected by developers and programmers in the United States or other Western countries, meaning they often exclude global culture context.

“Images of grooms are classified with lower accuracy when they come from Ethiopia and Pakistan, compared to images of grooms from the United…



Dave Gershgorn

Senior Writer at OneZero covering surveillance, facial recognition, DIY tech, and artificial intelligence. Previously: Qz, PopSci, and NYTimes.