Informing AI Policy Assessment using Large-Scale Simulation of Interventions

2026-04-20

Proceedings of the 2026 ACM Conference on Fairness, Accountability, and Transparency

AI Summary

This study addresses the urgent need for policymakers to effectively evaluate and prioritize AI governance interventions amid the rapid proliferation of AI systems and associated risks. The work proposes an innovative framework that integrates participatory AI principles into policy design by combining public engagement assessments, expert-informed cost modeling, and large language model–based predictions of harm reduction efficacy. Employing multi-objective optimization via genetic algorithms, the approach simulates diverse, actionable policy portfolios. Under varying weightings of cost, public participation, and harm reduction impact, the method identifies a spectrum of robust and negotiable policy pathways, offering a practical and inclusive starting point for AI governance decision-making.

Abstract

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 policy combinations, and examine how outcomes change under different weightings of cost, participatory input, and harm mitigation. We find that this method enables exploration of different balances between participatory and expert components, allowing policymakers and researchers to assess how much weight to assign to each. We argue that the diversity of viable policy combinations found by the genetic algorithm could be a useful starting point for deliberation. This method operationalizes existing work on participatory AI by integrating it directly into practical policy development pipelines.

Problem

• AI governance
• policy prioritization
• AI harms
• participatory evaluation
• policy assessment

Innovation

• large-scale simulation
• genetic algorithm
• participatory AI
• policy assessment
• LLM-based evaluation