Informing AI Policy Assessment using Large-Scale Simulation of Interventions
Julia Barnett, Kimon Kieslich, Natali Helberger, Nicholas Diakopoulos
Why It Matters
What makes this one worth your time
This research provides a structured framework for policymakers to prioritize AI governance strategies effectively, addressing the urgent need for informed decision-making in AI policy.
A novel approach to AI policy assessment through large-scale simulation and participatory evaluation.
Summary
The paper presents a methodology that combines participatory evaluation, expert assessment, and LLM-based harm mitigation analysis to identify viable AI policy options, utilizing a genetic algorithm for simulation of policy combinations.
Key contributions
- Development of a new methodology for AI policy assessment that combines multiple evaluation approaches.
- Application of genetic algorithms to simulate and analyze a wide range of policy options.
- Operationalization of participatory AI principles in practical policy development.
Notable insights
- The integration of participatory evaluation with expert assessment and LLM analysis offers a comprehensive approach to policy formulation.
- Using genetic algorithms to explore policy combinations allows for a systematic examination of trade-offs between cost, participation, and harm mitigation.
Possible limitations
- Not stated in the abstract.
Abstract
arXiv:2605.27395v2 Announce Type: replace-cross 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.