Related papers: Generative AI for Social Impact

Generative AI for Social Impact

URL: http://arxiv.org/abs/2601.04238v1
Date: Mon, 05 Jan 2026 02:44:39 GMT
Title: Generative AI for Social Impact
Abstract summary: AI for Social Impact (AI4SI) has achieved compelling results in public health, conservation, and security.<n>We characterize this bottleneck through three gaps: observational scarcity resulting from limited or unreliable data; policy challenges involving decisions and nonstationarity; and the friction of human-AI alignment.<n>We argue that Generative AI offers a unified pathway to bridge these gaps.
Score: 37.37093679341245
License: http://creativecommons.org/licenses/by/4.0/
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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