AI for Planetary and Social Impact

This unit explores how AI can be applied to address environmental, social, and economic challenges—covering cognitive cities, wildlife conservation, food security, equity, education, accessibility, and economic mobility. Students build on prior technical and data foundations to analyze case studies, evaluate risks and trade-offs, and prepare actionable, ethical proposals for real-world AI interventions.

Learning Objectives

• Analyze AI-driven case studies (e.g., cognitive cities, wildlife conservation, food security, education) to identify problem scope, data requirements, stakeholders, and measurable success metrics and produce a written summary of findings.
• Evaluate ethical, equity, accessibility, privacy, and ecological risks of proposed AI solutions using a structured risk-assessment framework and recommend at least three mitigation strategies for a selected case.
• Design a data-driven AI intervention concept for one societal challenge from the unit that includes a problem statement, dataset types and sources, model approach, evaluation metrics, implementation milestones, and deployment considerations, and present it as a project proposal.
• Demonstrate interdisciplinary and global collaboration skills by creating a stakeholder engagement plan and a concise policy/technical roadmap that addresses regulatory, privacy, and biological/ecosystem impacts for the proposed intervention.