Surfacing citizens’ policy perspectives at scale in the age of large language models
Ezequiel Lopez-Lopez, Stefan Michael Herzog · Behavioral Science & Policy · 2025
To address policy challenges such as climate change or pandemics effectively, policymakers require insights into the views of the general public. However, traditional large-scale quantitative methods like surveys and aggregated social media analytics lack nuance, while qualitative approaches such as interviews are labor intensive and thus limited to small samples. We discuss how artificial intelligence tools known as large language models (LLMs) could be leveraged to surface the detailed views of large numbers of citizens on policy issues. In particular, we showcase an LLM-supported method designed to provide both quantitative and qualitative insights from large samples of respondents who provide free-text responses to open-ended questions. We propose that such approaches could help policymakers efficiently integrate citizens’ input into their decision-making processes and give them timely, nuanced insights that complement those produced by established methods of obtaining large-scale public input.