Platform Extractivism: Venezuela’s Hidden Workforce Trains AI Algorithms for Pennies
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| Source: Mastodon | Original article
In Venezuela, a hidden workforce of low‑paid workers is training AI algorithms for pennies, often under surveillance, with many relying on the work during the COVID‑19 pandemic.
Venezuelan residents have become the unseen hands behind the AI models that dominate global tech, a reality laid bare in information scientist Julián Posada’s forthcoming book *Platform Extractorism: Data Work and the People Powering Artificial Intelligence*. Drawing on extensive fieldwork conducted during the height of the COVID‑19 pandemic, Posada documents how entire communities turned to “data work” – tasks such as image tagging, transcription and content moderation – to survive. Workers are paid as little as 50 cents per hour and operate under constant surveillance by platform‑owned algorithms, many of them unaware of the broader systems they feed.
The book reframes this dispersed labor force as a form of “platform extractivism,” arguing that digital platforms act as modern extraction engines that perpetuate long‑standing social inequalities and transnational power imbalances rooted in coloniality. By turning precarious, low‑paid labor in the Global South into the raw material for high‑value AI products, the model raises urgent ethical questions about who profits from the AI boom and under what conditions.
Posada’s findings arrive at a moment when regulators worldwide are grappling with AI governance, and they could sharpen calls for transparency in data‑sourcing practices. Advocacy groups may use the book as evidence to demand labor protections for data annotators, while tech firms could face pressure to disclose supply‑chain details and to redesign compensation structures.
Watch for the book’s release later this year, for reactions from platform companies, and for any policy initiatives that reference “platform extractivism” in upcoming AI regulatory frameworks. The story also signals a broader research agenda: mapping hidden data work across other low‑income regions and assessing its impact on the ethics and sustainability of AI development.
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