This article has been reviewed according to Science X's editorial process and policies. Editors have highlighted the following attributes while ensuring the content's credibility: Amid all the talk about artificial intelligence (AI) both creating and destroying jobs, a troubling reality flies under the radar. The tasks machines can't perform well are often offloaded onto marginalized global workers who are struggling in precarious labor markets.
They do ostensibly "automated" work under exploitative conditions. Data work is an essential part of building and refining AI systems. Before AI models can "learn" anything, human data workers must categorize, label, test and moderate vast volumes of text, images, audio and video to make the data usable for AI training.
This labor is performed by an expanding global digital workforce that prepares datasets not only for big tech but also for high-stakes industries such as banking, insurance, health care and government agencies, including defense. To understand the AI workforce, I have been interviewing workers in China and Australia who prepare datasets for AI models. The fieldwork is ongoing, but here's what they've revealed so far.
My interviews with 10 people to date show that precarious labor markets and marginalized social status have pushed digitally literate young workers into the data-labeling industry. As one interviewee said, "We do the manual work so that they get the credit for the intelligence." There's a lot of inequality across the data labor market, shaped by people's qualifications and geographic location. Those with Ph.D.-level or equivalent qualifications and STEM certifications can typically get more specialized tasks.
If based in the Global North, such workers tend to be higher-paid, earning A$400–A$800 per hour depending on the task. But such specialized and highly paid tasks are rare and difficult to get. Most workers I interviewed perform general tasks, such as repetitively drawing bounding boxes for images used in drones, self-driving cars and automated vending machines, or annotating audio.
These workers normally receive as little as A$6 per day or even less. The pay can't cover daily expenses, and the long hours leave workers with chronic eye strain and back pain. Data work is not unlike other poorly regulated jobs in the gig economy.
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