Generative Artificial Intelligence for Social Impact

Artificial intelligence for social impact 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. Large language model 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.