Informing AI Policy Assessment using Large-Scale Simulation of Interventions

Researchers introduce a methodology combining participatory evaluation, expert cost assessment, and LLM-based harm evaluation to help policymakers identify effective AI governance policy combinations. Using genetic algorithm simulations, the approach explores vast policy solution spaces and demonstrates how different weightings of stakeholder input, implementation costs, and harm mitigation can inform practical policy development.

This research addresses a critical gap in AI governance: the lack of systematic methods for evaluating and prioritizing among competing policy interventions. As governments worldwide develop AI regulation frameworks, policymakers face overwhelming complexity when choosing between different regulatory approaches. The methodology presented operationalizes participatory AI governance by integrating community input directly into policy development pipelines, rather than treating it as an afterthought.

The approach combines three complementary evaluation layers: participatory assessments capturing diverse stakeholder perspectives, expert judgment on implementation feasibility and costs, and LLM-based evaluations of perceived harm mitigation. This tripartite framework acknowledges that effective policy requires balancing multiple competing objectives—feasibility, stakeholder legitimacy, and actual harm reduction. The genetic algorithm simulation is particularly valuable because it explores combinations of policies rather than evaluating them in isolation, revealing synergistic effects and trade-offs that sequential policy analysis would miss.

For the AI governance ecosystem, this work provides actionable infrastructure for evidence-based policymaking at a moment when AI regulation remains largely ad hoc and jurisdiction-dependent. The methodology's flexibility in weighting different components allows policymakers to adjust emphasis based on local contexts and values—some jurisdictions may prioritize expert cost assessment while others weight participatory input more heavily.

Looking ahead, the critical challenge involves implementing these recommendations at scale across different regulatory regimes. The research's impact depends on adoption by policymaking bodies and integration into official policy development processes. Future work should examine whether recommendations generated through this methodology actually produce the predicted harm mitigation when implemented in real-world contexts.

• →A new methodology combines participatory input, expert cost analysis, and LLM assessment to systematically evaluate AI policy options.
• →Genetic algorithm simulations explore vast combinations of policies to identify viable solutions with different cost-benefit trade-offs.
• →The approach allows policymakers to adjust weighting between stakeholder participation and expert assessment based on local priorities.
• →Research operationalizes participatory governance by integrating it directly into practical policy development rather than as a secondary consideration.
• →Method addresses growing complexity of AI governance by providing systematic evaluation framework for competing regulatory approaches.