用模拟算法筛选可行的AI治理政策组合，帮决策者聚焦重点。
Informing AI Policy Assessment using Large-Scale Simulation of Interventions
- 结合专家评估与公众参与，用大模型判断政策减害效果。
- 通过遗传算法探索海量政策组合，发现多种有效方案。
- 适合参与式治理研究者和政策制定者参考使用。
随着AI系统及其风险的快速扩散，全球范围内加强AI治理的努力日益增多，但如何在众多政策选项中进行优先排序已成为决策者与研究者的挑战。本文提出一种方法，用于识别能缓解特定AI风险的可行政策组合，帮助决策者和研究者将资源集中于更值得投入的领域。该方法融合了政策的参与式评估、专家对实施成本的判断，以及基于大语言模型的感知风险缓解评估。通过基于遗传算法的规模化模拟研究，探索了大量潜在政策组合，并分析了在不同权重（成本、参与度、减害效果）配置下结果的变化。研究发现，该方法可灵活调整参与式与专家意见的平衡，使决策者能评估各自权重的合理性。遗传算法所发现的多样化可行政策组合，可作为讨论的起点。该方法将参与式AI理念直接融入实际政策开发流程，实现了现有理论的可操作化。
原文摘要 · Abstract (English)
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.
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