Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence

Article Source: Yale University Press, 2021
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Artificial intelligence (AI) relies on natural resources, low-cost labor, and data. The production of AI technology harms the environment. AI systems rely on low-wage workers.


Policy Relevance:

Assessment of the costs of AI should include costs to the environment and to workers.


Key Takeaways:
  • Nonhuman systems such as computers are not analogous to human minds; AI is not really intelligent, and “machine learning” would be a better term.
  • AI depends on extracting energy and mineral resources from the planet, cheap labor, and data.
    • Seventeen rare elements are needed to make AI, such as lanthanum, europium, and yttrium
    • Mining is associated with local and geopolitical violence, bringing war, famine, and death.
  • Clean technology is a myth, and portrayals of cloud-based computation should factor in costs to the environment.
    • Data centers are among the largest consumers of electricity.
    • China's data center industry draws 73 percent of its power from coal.
    • Shipping emissions significantly affect the Earth's atmosphere.
  • AI systems rely on cheap crowd-sourced labor to do tasks that cannot be done by machines, such as assessing violent videos or hate speech.
  • Algorithms that monitor worker productivity may assign workers very short or very long shifts, whichever is profitable; workers are unable to predict their schedules, and the algorithm does not factor in this human cost.
  • As data extraction becomes more invasive and researchers access data without interacting with their subjects, the risk that research using human data will inculcate bias or do harm rises.
  • The processes for classifying people by race and gender used in training AI are beset with stereotypes and error.
    • People assume that race and gender can be detected by machines, but this presents political and social problems.
    • Some AI systems try to detect sexuality or criminality based on headshots from drivers' licenses.
    • Companies that set up classification systems operate with little oversight or public input.
  • Large-scale AI surveillance originated with national security services, but now extend to classrooms, police stations, workplaces, and other everyday locales.
    • Surveillance supports central control at the cost of other types of social organization.
    • Surveillance systems blur the lines between law enforcement and tech firms.
    • Surveillance will radically reshape civic life.
  • AI ethics should include consideration of the labor conditions of miners, contractors, and crowdworkers; we should consider the experiences of those harmed by AI, and the systems' carbon footprint.



Kate Crawford

About Kate Crawford

Kate Crawford is a Research Professor of Communication and Science and Technology Studies at USC’s Annenberg School for Communication and Journalism and a Senior Principal Researcher at Microsoft Research in New York. Professor Crawford is a leading scholar of the social and political implications of artificial intelligence. Over her 20-year career, her work has focused on understanding large-scale data systems, machine learning and AI in the wider contexts of history, politics, labor, and the environment.