The Ecology of AI Risk

Edward Geist, Alexander Dolnick Meyer, Alvin Moon, Aisha Najera, James Holland Jones, Anton Wu

ResearchPosted on rand.org Jun 29, 2026Published in: npj Complexity (2026). DOI: 10.1038/s44260-026-00090-2

Understanding the risk from applications of artificial intelligence (AI) is a critical part of creating AI governance strategies. Building on the idea of studying AI using ecological and evolutionary perspectives, we propose a novel approach for assessing risk from AI using indicators derived from theoretical ecology models. We illustrate our methods by deriving 3 indicators from population and ecosystem models originating from theoretical ecology. We conclude with a discussion of limitations of our analysis and considerations for improving AI governance policy.

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Document Details

  • Publisher: Springer Nature
  • Availability: Non-RAND
  • Year: 2026
  • Pages: 24
  • Document Number: EP-71375

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