What Would an LLM Do? Evaluating Policymaking Capabilities of Large Language Models
2025-09-05
Summary
The article explores the potential of large language models (LLMs) in informing social policymaking, specifically in addressing homelessness. It introduces a benchmark with decision scenarios across four locations and examines LLMs' policy recommendations compared to those of human experts, using the Capability Approach framework. The study also integrates an agent-based model (ABM) to simulate the social impact of policies, revealing that LLMs can offer valuable policy insights when used responsibly.
Why This Matters
This research is relevant as it highlights the potential of LLMs to contribute to complex social issues like homelessness, which affects millions worldwide. By evaluating LLMs' alignment with human experts and their capability to simulate policy impacts, the study underscores the importance of integrating AI with human expertise to improve policymaking. It also emphasizes the need for ethical frameworks and contextual understanding in deploying AI in socially sensitive areas.
How You Can Use This Info
Professionals in policymaking and social services can consider leveraging LLMs to generate alternative policy options and simulate their impacts, thereby enhancing decision-making processes. By collaborating with AI experts, they can ensure that the models are ethically aligned and contextually sensitive. Additionally, adopting an agent-based modeling approach can help in assessing the potential outcomes of proposed policies, leading to more informed and effective solutions.