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Most people know by now that artificial intelligence powers much of the online world, sifting through social media posts, planning driving routes or helping to choose a movie to watch. But what goes largely unseen is the human labor—often performed by people working for low wages in the Global South—that makes this careful algorithmic lifestyle curation possible.
Cunyan Ma, an undergraduate at Brown University working with the university's Center for Technological Responsibility, Reimagination and Redesign, is trying to bring that hidden labor to light. As part of an ongoing project to explore the material underpinnings of the digital world, the Brown senior has created an interactive map linking technology companies and their intermediaries to data workers around the world. The map, which is freely available online, aggregates existing data worker databases, Ma's own data collection and news reports describing the conditions many data workers experience.
"My primary motivation is to visualize the idea of AI colonialism in an impactful and immediately accessible manner," Ma said. "The gap I'm trying to fill is that most of what we know about data workers comes from anecdotal reports from authors and journalists. Those are great because they give us a very detailed look at working conditions and pay, etc. But I'm hoping this might start to fill in a larger picture of what AI colonialism actually looks like."
Outsourced data workFor an AI model to do much of anything, it must be trained by humans. At a basic level, that could mean people simply labeling objects in photos or parts of speech in a sentence. It could also mean transcribing long passages of speech, rating good or bad responses from chatbots or identifying tumor boundaries in a medical scan.
Much of the work is tedious, but Ma notes there can be even darker aspects to it. To filter out disturbing images or posts, or to keep chatbots from creating such content, AI systems have to be able to recognize it. That means that data workers must view horrifying images or text passages to label that content.
Most corporations, from social media and tech firms to retailers and defense companies, outsource their data labeling to companies that specialize in it. And most of those data-labeling firms look for inexpensive labor.
"How that manifests in reality is that companies outsource that work to the Global South—Kenya, Indonesia, Colombia and elsewhere," Ma said. "So you have folks around the world who have to read paragraph after paragraph of AI-produced child pornography, bestiality, violent content or whatever else, just to try to train AI to be safe."
That, Ma says, is the essence of AI colonialism—the extraction of difficult, low-paid labor from the Global South for profit in the West.
Mapping the power structureMa's goal was to uncover that chain of AI labor, which she says is intentionally kept opaque to avoid accountability. She used existing databases of data-labeling firms and their corporate customers, as well as her own original database of data-labeling firms' delivery centers, a proxy for where data workers are located. The map draws the lines that connect those nodes.
In its current form, it reveals links between 13 data-labeling companies, 141 of their corporate customers and 31 countries that contain data worker sites. The map isn't a comprehensive look at chains of AI labor, Ma says, but she hopes it's an illustrative one.
"One purpose for this is as a public education tool," she said. "When people want to learn about AI and its social impact, the map puts it in the context of this larger economic and political structure."
Suresh Venkatasubramanian, a computer science professor at Brown who leads the Center for Technological Responsibility, Reimagination and Redesign and is Ma's adviser on the project, says work like this can help people better understand that underneath the algorithms, AI is really an aggregation of human knowledge, understanding and effort.
"It's all humans underneath," Venkatasubramanian said. "That's the real story behind the generation of AI. The problem with a lot of current discourse around AI is that it seeks to ignore that. The result is that we start constructing stories around AI as its own sentient being, as something that's alive. But it's not. It's something that's built—something that's constructed by us, by people, all across the world."
Ma says she hopes to continue expanding the map of AI labor and to map other aspects of the materiality that lies beneath the algorithms. Soon, for example, she hopes to map the labor implicated in the semiconductor industry. As a social analysis and research concentrator, Ma sees this technique, known as power mapping, as an effective means of understanding how global power structures influence the distribution of labor worldwide.
For now, she sees her map as a potential means for workers to connect to each other.
"One of the things about this is that it's distributed," Ma said. "People don't even know about their coworkers. That makes unionizing or anything like that just kind of impossible. Maybe they can use a future iteration of the map to identify each other."
Ultimately, Ma hopes that these illustrations of the power structures beneath AI might make for a more equitable distribution of AI labor and its spoils.
Who's behind this story?
Swati MestriSwati Mestri holds a bachelor's degree in Electronics Engineering and has worked as a content editor since 2019. She has experience editing research documents across technology, health care, and materials science, and has a particular interest in technology and space. Full profile →
Andrew ZininMaster's in physics with research experience. Long-time science news enthusiast. Plays key role in Science X's editorial success. Full profile →
Citation: Mapping the hidden global labor powering AI (2026, October 26) retrieved 26 October 2026 from https://phys.org/news/2026-10-hidden-global-labor-powering-ai.html
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