# Informing AI Policy Assessment using Large-Scale Simulation of Interventions

arXiv: https://arxiv.org/abs/2605.27395 · HTML: https://mlsanity.com/paper/2605.27395/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 27 of its 101 references that are in the map.
- First posted on arXiv 20 Apr 2026.

## Builds on (most cited first, 27 of 27)

- [Generative Agents: Interactive Simulacra of Human Behavior](https://mlsanity.com/paper/2304.03442/) (2023)
- [DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelines](https://mlsanity.com/paper/2310.03714/) (2023)
- [Whose Opinions Do Language Models Reflect?](https://mlsanity.com/paper/2303.17548/) (2023)
- [Large language models that replace human participants can harmfully misportray and flatten identity groups](https://mlsanity.com/paper/2402.01908/) (2024)
- [Can LLM be a Personalized Judge?](https://mlsanity.com/paper/2406.11657/) (2024)
- [LLM Social Simulations Are a Promising Research Method](https://mlsanity.com/paper/2504.02234/) (2025)
- [CoMPosT: Characterizing and Evaluating Caricature in LLM Simulations](https://mlsanity.com/paper/2310.11501/) (2023)
- [The Participatory Turn in AI Design: Theoretical Foundations and the Current State of Practice](https://mlsanity.com/paper/2310.00907/) (2023)
- [Questioning the Survey Responses of Large Language Models](https://mlsanity.com/paper/2306.07951/) (2023)
- [SimBench: Benchmarking the Ability of Large Language Models to Simulate Human Behaviors](https://mlsanity.com/paper/2510.17516/) (2025)
- [Centaur: a foundation model of human cognition](https://mlsanity.com/paper/2410.20268/) (2024)
- [Participatory Problem Formulation for Fairer Machine Learning Through Community Based System Dynamics](https://mlsanity.com/paper/2005.07572/) (2020)
- [AHA!: Facilitating AI Impact Assessment by Generating Examples of Harms](https://mlsanity.com/paper/2306.03280/) (2023)
- [Can A Society of Generative Agents Simulate Human Behavior and Inform Public Health Policy? A Case Study on Vaccine Hesitancy](https://mlsanity.com/paper/2503.09639/) (2025)
- [AI Automatons: AI Systems Intended to Imitate Humans](https://mlsanity.com/paper/2503.02250/) (2025)
- [AI Self-preferencing in Algorithmic Hiring: Empirical Evidence and Insights](https://mlsanity.com/paper/2509.00462/) (2025)
- [Particip-AI: A Democratic Surveying Framework for Anticipating Future AI Use Cases, Harms and Benefits](https://mlsanity.com/paper/2403.14791/) (2024)
- [Anticipatory Ethics and the Role of Uncertainty](https://mlsanity.com/paper/2011.13170/) (2020)
- [PolicyCraft: Supporting Collaborative and Participatory Policy Design through Case-Grounded Deliberation](https://mlsanity.com/paper/2409.15644/) (2024)
- [Addressing the regulatory gap: moving towards an EU AI audit ecosystem beyond the AI Act by including civil society](https://mlsanity.com/paper/2403.07904/) (2024)
- [Anticipating Impacts: Using Large-Scale Scenario Writing to Explore Diverse Implications of Generative AI in the News Environment](https://mlsanity.com/paper/2310.06361/) (2023)
- [Simulating Policy Impacts: Developing a Generative Scenario Writing Method to Evaluate the Perceived Effects of Regulation](https://mlsanity.com/paper/2405.09679/) (2024)
- [Who Evaluates AI's Social Impacts? Mapping Coverage and Gaps in First and Third Party Evaluations](https://mlsanity.com/paper/2511.05613/) (2025)
- [Normative Challenges of Risk Regulation of Artificial Intelligence and Automated Decision-Making](https://mlsanity.com/paper/2211.06203/) (2022)
- [Envisioning Stakeholder-Action Pairs to Mitigate Negative Impacts of AI: A Participatory Approach to Inform Policy Making](https://mlsanity.com/paper/2502.14869/) (2025)
- [Scenarios in Computing Research: A Systematic Review of the Use of Scenario Methods for Exploring the Future of Computing Technologies in Society](https://mlsanity.com/paper/2506.05605/) (2025)
- [Metaverse Perspectives from Japan: A Participatory Speculative Design Case Study](https://mlsanity.com/paper/2401.17428/) (2024)

## Abstract (arXiv)

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.