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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2509.03827 |
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| _version_ | 1866918312600928256 |
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| author | Coz, Pierre Le Liu, Jia An Bhattacharjya, Debarun Curto, Georgina Stinckwich, Serge |
| author_facet | Coz, Pierre Le Liu, Jia An Bhattacharjya, Debarun Curto, Georgina Stinckwich, Serge |
| contents | Large language models (LLMs) are increasingly being adopted in high-stakes domains. Their potential to encode evolving social contexts and to generate plausible scenarios position them as promising tools in social policymaking. This article evaluates whether LLMs are aligned with domain experts (and among themselves) on policy recommendations to alleviate homelessness - a challenge affecting over 150 million people worldwide. We develop a novel benchmark comprised of decision scenarios across four cities, with policy choices that are grounded in the conceptual framework of the Capability Approach for human development. We also present an automated pipeline that connects the policies to an agent-based model in one location, and compare the social impact of the policies recommended by LLMs to those recommended by experts. Our exploratory analysis reveals variation across LLMs in their policy recommendations compared to local experts, yet suggests potential benefits of the use of LLMs to provide insights for policymaking, if paired with responsible guardrails, contextual calibrations, and local domain expertise. Our work operationalizes the Capability Approach in a computational framework and provides new insights on homelessness alleviation policymaking with a focus on human dignity. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_03827 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | What Would an LLM Do? Evaluating Large Language Models for Policymaking to Alleviate Homelessness Coz, Pierre Le Liu, Jia An Bhattacharjya, Debarun Curto, Georgina Stinckwich, Serge Artificial Intelligence Large language models (LLMs) are increasingly being adopted in high-stakes domains. Their potential to encode evolving social contexts and to generate plausible scenarios position them as promising tools in social policymaking. This article evaluates whether LLMs are aligned with domain experts (and among themselves) on policy recommendations to alleviate homelessness - a challenge affecting over 150 million people worldwide. We develop a novel benchmark comprised of decision scenarios across four cities, with policy choices that are grounded in the conceptual framework of the Capability Approach for human development. We also present an automated pipeline that connects the policies to an agent-based model in one location, and compare the social impact of the policies recommended by LLMs to those recommended by experts. Our exploratory analysis reveals variation across LLMs in their policy recommendations compared to local experts, yet suggests potential benefits of the use of LLMs to provide insights for policymaking, if paired with responsible guardrails, contextual calibrations, and local domain expertise. Our work operationalizes the Capability Approach in a computational framework and provides new insights on homelessness alleviation policymaking with a focus on human dignity. |
| title | What Would an LLM Do? Evaluating Large Language Models for Policymaking to Alleviate Homelessness |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2509.03827 |