Generative AI for Social Impact

2026-01-05

arXiv.org

AI Summary

This work addresses three critical challenges in scaling AI deployment for high-stakes domains such as public health, ecological conservation, and safety: scarcity of observational data, complexity of policy synthesis, and difficulties in human-AI alignment. To tackle these issues, we propose the first generative framework that synergistically integrates large language models (LLMs) and diffusion models within social impact scenarios. Our approach leverages LLM agents to automatically translate natural language instructions into executable constraints, while diffusion models generate high-fidelity synthetic data to support uncertainty-aware, robust decision-making. This unified methodology effectively resolves data scarcity, enables seamless policy transfer, and facilitates dynamic integration of human-derived constraints. The framework significantly enhances deployment efficiency, policy robustness, and human-AI collaboration in resource optimization tasks, offering a reusable paradigm for AI for Social Impact (AI4SI).

Technology Category

• Humans and AI: Learning Human Values and Preferences
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Application Domains: Humanities & Computational Social Science

Application Category

• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications

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.

Problem

• deployment bottleneck
• observational scarcity
• policy synthesis
• human-AI alignment
• nonstationarity

Innovation

• Generative AI
• LLM agents
• diffusion models
• human-AI alignment
• synthetic data generation