Health Facility Location in Ethiopia: Leveraging LLMs to Embed Human-AI Alignment in Algorithmic Planning

This paper examines how large language models (LLMs) can support human-AI alignment in public-sector planning for health facility placement in Ethiopia. The authors propose methods for integrating human priorities, contextual knowledge, and algorithmic optimization into facility location planning systems. By combining AI-driven decision support with participatory and policy-aware planning frameworks, the study seeks to improve the transparency, responsiveness, and practical effectiveness of health infrastructure allocation in resource-constrained settings.