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arXiv · 2601.04238

Generative AI for Social Impact

Lingkai Kong·Cheol Woo Kim·Davin Choo·Milind Tambe
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Abstract

AI for Social Impact (AI4SI) has achieved compelling results in public health, conservation, and security, yet scaling these successes remains difficult due to a persistent deployment bottleneck. We characterize this bottleneck through three coupled gaps: observational scarcity resulting from limited or unreliable data; policy synthesis challenges involving combinatorial decisions and nonstationarity; and the friction of human-AI alignment when incorporating tacit expert knowledge and dynamic constraints. We argue that Generative AI offers a unified pathway to bridge these gaps. LLM agents assist in human-AI alignment by translating natural-language guidance into executable objectives and constraints for downstream planners, while diffusion models generate realistic synthetic data and support uncertainty-aware modeling to improve policy robustness and transfer across deployments. Together, these tools enable scalable, adaptable, and human-aligned AI systems for resource optimization in high-stakes settings.

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Lingkai Kong, Cheol Woo Kim, Davin Choo, Milind Tambe. 2026-01-05. Generative AI for Social Impact. https://arxiv.org/abs/2601.04238

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