AI for Global Sustainability and Equity

This unit explores how AI can be applied at scale to address global sustainability challenges—urban systems, wildlife conservation, food security, education, and access to basic needs—while centering equity and international cooperation. Students synthesize case studies and design actionable, measurable AI-enabled strategies, preparing them for the pathway capstone and real-world implementation.

Learning Objectives

• Analyze at least three real-world AI case studies (e.g., cognitive cities, wildlife conservation, food systems) to identify measurable sustainability outcomes, risks, and trade-offs.
• Evaluate ethical, equity, and accessibility implications of AI interventions in agriscience and natural resources using established criteria (privacy, bias, environmental impact) and propose at least two mitigation strategies.
• Design a multi-stakeholder implementation plan that leverages AI to improve access to basic needs and economic/social mobility, including clear goals, success metrics, data sources, and collaboration roles.
• Demonstrate effective communication and collaboration by preparing and presenting a policy brief or stakeholder pitch that articulates recommended AI-enabled solutions, expected outcomes, and steps for global cooperation.