Informing AI Policy Assessment using Large-Scale Simulation of Interventions

A new methodology introduced on arXiv combines participatory evaluation, expert cost assessment, and large language model analysis with genetic algorithm simulations to help policymakers identify viable policy options for mitigating artificial intelligence harms [1]. The approach, detailed in a paper submitted 20 Apr 2026, addresses the growing difficulty of prioritizing among competing AI governance proposals as systems proliferate globally [1][2]. Researchers designed the method to operationalize existing work on participatory AI by integrating it directly into practical policy development pipelines [1][2]. The framework evaluates potential interventions through three lenses: participatory input from affected communities, expert assessment of implementation costs, and an LLM-based evaluation of perceived harm reduction under each option [1][2]. A genetic algorithm then explores a vast solution space of potential policy combinations, examining how outcomes shift under different weightings of cost, participatory input, and harm mitigation [1][2]. Genetic algorithms are a class of optimization techniques inspired by natural selection, distinct from the agent-based models used in ecology and social science to simulate emergent behavior from individual agent interactions [3]. The authors argue that the diversity of viable policy combinations surfaced by the algorithm could serve as a useful starting point for deliberation among stakeholders [1][2]. The work arrives as automated decision-making systems face increasing scrutiny across public administration, health, employment, and other sectors [4]. Such systems process data from databases, sensors, social media, and other sources using machine learning and natural language processing, presenting technical, legal, and ethical challenges that demand governance frameworks [4]. The methodology does not prescribe specific policies but instead provides a simulation environment where policymakers can adjust the relative weight given to expert judgment versus participatory feedback [1][2]. This simulation-based approach echoes broader trends in governance tooling, where computational methods are used to model regulatory impacts before implementation. In the environmental, social, and governance domain, for instance, standardization and data quality remain persistent criticisms, with some arguing that ESG frameworks can function as de facto regulation without democratic oversight [5]. The paper's authors position their methodology as a way to make trade-offs explicit, allowing policymakers and researchers to assess how much weight to assign to each component before committing resources [1][2].