AI for Social Good (AI4SG) is a research theme that uses and advances AI to im- prove the well-being of society. We introduce three lines of work that center around learning and planning to address real-world challenges in cybersecurity, food waste and security, and environmental conservation. For cybersecurity, we provide a learning and planning pipeline for generic cyber deception and an algorithm to counter watering-hole attacks. In food waste and food security, we develop a predictive model for the rescue claim status and an online learning and planning algorithm for volunteer engagement through push notifications. We also ran a randomized controlled trial for our algorithm to show significant improvement in the real world. For environmental conservation, we develop a natural language processing-based media content monitoring system to provide early warning of infrastructure projects that might pose harm to conservation efforts. The system leverages active learning and learning with noisy labels algorithms to address challenges in applied learning and planning applications. The tool has been deployed in multiple places around the world, monitoring over 60,000 conservation sites worldwide since February 2022. Distilling lessons learned from these projects, we propose bandit data-driven optimization, the first iterative learning and planning framework to rigorously address the pain points in practical prediction-prescription workflows in lots of social good projects across application domains.

176 pages

Thesis Committee:
Fei Fang (Chair)
Rayid Ghani
Jason Hong
Milind Tambe (Harvard University)

James D. Herbsleb, Head, Software and Societal Systems Department
Martial Hebert, Dean, School of Computer Science

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