Generative AI for Social Impact

TL;DR

This paper discusses how Generative AI can address key deployment challenges in AI for Social Impact by improving data generation, policy synthesis, and human-AI alignment, enabling scalable and adaptable solutions.

Contribution

It proposes a unified approach using Generative AI, including LLMs and diffusion models, to overcome data scarcity, policy complexity, and alignment issues in high-stakes social impact applications.

Findings

Generative AI improves data realism and diversity for social impact tasks.

LLM agents facilitate human-AI policy alignment through natural language guidance.

Diffusion models enhance policy robustness and transferability across deployments.

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.