Informing AI Policy Assessment using Large-Scale Simulation of Interventions
As the rapid proliferation of AI systems and harms spurs efforts in AI governance around the world, prioritizing among competing policy options has become increasingly challenging for policymakers and researchers. We introduce a methodology for identifying viable policy options to mitigate specified AI harms, helping policymakers and researchers target areas that warrant greater time and resource investment. This method combines participatory evaluation of policies, expert assessment of implementation costs, and an LLM-based assessment of perceived harm mitigation under each policy option. We leverage a genetic algorithm-based simulation study to explore a vast solution space of potential...
| Authors | Julia Barnett, Kimon Kieslich, Natali Helberger et al. |
|---|---|
| Primary category | cs.CY |
| Published | 20 April 2026 |
| Last updated at source | 7 July 2026 |
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