The ecology of AI risk
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.
Authors
- Alvin Moon
- Aisha Nájera
- Anton Wu
- James Holland Jones
- Edward Geist
- Alexander Dolnick Meyer
Institutions
- University of Notre Dame (US)
- RAND Corporation (US)
- Stanford Medicine (US)
- Stanford University (US)
Publication Details
- Journal
- npj Complexity
- Published
- 2026-06-25
- DOI
- https://doi.org/10.1038/s44260-026-00090-2
- Primary Topic
- Ethics and Social Impacts of AI
- Type
- article
- Field-Weighted Citation Impact
- 0.00