corporate AI landscape
Corporate AI is a term to describe the ways that powerful technology corporations control what AI products are built and how they are developed and governed. There are as many ways to approach AI development as there are innovative applications for AI technology, yet Corporate AI has set a precedent for developing AI in ways that are extractive, environmentally harmful, and colonialist.
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The Center for Land Use Interpretation over a decade ago took pictures of the internet. It was not photos of people on computer screens at the libraries or cafes. Most of the photos were actually of anonymous looking office buildings and squat structures behind manicured trees and plants. These photos were mostly of data centers,… <Read More>
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AI development today is primarily driven by financial incentives rather than social responsibility. Competing for funding, market dominance, and regulatory approval, companies prioritize getting high performance numbers, sometimes even gaming the system by tweaking evaluation criteria to maximize reported accuracy. This results in a culture where AI success is measured by marketability rather than functionality.… <Read More>
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Corporate AI development depends on invisible labor, much of which is performed by workers in the Global South. This extractive reality stands in stark contrast to the techno-utopian promises of AI companies. My personal interactions with Kauna Malgwi, the Nigerian chairperson of the content moderator’s union, and my close watching and reading of the whistleblowing… <Read More>
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To reclaim our socio-digital futures, we must challenge corporate AI’s capture of democratic politics and counter it by centering collective, solidary, and participatory forms of politics. <Read More>
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Corporate AI relies on extraction of the planet, people, and data contributing to the global climate crisis and environmental injustice. <Read More>
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Corporate AI, especially LLMs, reinforce colonial and capitalist structures by privileging Western, male, and Global North perspectives while excluding localized knowledges. <Read More>
