One of the most famous, known as the Casa Lleó Morera, was built early in the 20th century with profits made from the sugar trade in Puerto Rico. While tourists from around the world today visit the mansion for its beauty, Puerto Rico still suffers from food insecurity because for so long its fertile land produced cash crops for Spanish merchants instead of sustenance for the local people.
As we stood in front of the intricately carved façade, which features flora, mythical creatures, and four women holding the four greatest inventions of the time (a lightbulb, a telephone, a gramophone, and a camera), I could see the parallels between this embodiment of colonial extraction and global AI development.
The AI industry does not seek to capture land as the conquistadors of the Caribbean and Latin America did, but the same desire for profit drives it to expand its reach. The more users a company can acquire for its products, the more subjects it can have for its algorithms, and the more resources—data—it can harvest from their activities, their movements, and even their bodies.
Neither does the industry still exploit labor through mass-scale slavery, which necessitated the propagation of racist beliefs that dehumanized entire populations. But it has developed new ways of exploiting cheap and precarious labor, often in the Global South, shaped by implicit ideas that such populations don’t need—or are less deserving of—livable wages and economic stability.
MIT Technology Review’s new AI Colonialism series digs into these and other parallels between AI development and the colonial past by examining communities that have been profoundly changed by the technology. In part one, we head to South Africa, where AI surveillance tools, built on the extraction of people’s behaviors and faces, are re-entrenching racial hierarchies and fueling a digital apartheid.
In part two, we head to Venezuela, where AI data-labeling firms found cheap and desperate workers amid a devastating economic crisis, creating a new model of labor exploitation. The series also looks at ways to move away from these dynamics. In part three, we visit ride-hailing drivers in Indonesia who, by building power through community, are learning to resist algorithmic control and fragmentation. In part four, we end in Aotearoa, the Māori name for New Zealand, where an Indigenous couple are wresting back control of their community’s data to revitalize its language.