AI for Social and Scientific Challenges

This unit examines how AI and data science are applied to real-world social and scientific problems—natural disasters, food and water security, medicine, education, biodiversity, proteomics, and astronomy—while foregrounding ethical and societal impacts such as surveillance and amplification of inequities. Students build on foundational concepts from Unit 1 by analyzing cross-domain case studies and designing high-level solution concepts that prepare them for implementation, evaluation, and policy work in subsequent units.

Learning Objectives

• Analyze domain-specific case studies (natural disasters, food and water security, medicine, education, biodiversity, proteomics, astronomy) to identify relevant data types, modeling approaches, success metrics, and limitations
• Evaluate ethical, legal, and societal impacts of AI deployments—including surveillance risks and the potential to magnify societal ills—and propose mitigation and governance strategies
• Apply AI and data science principles to design a high-level solution concept for a selected social-good problem, specifying data sources, model choices, evaluation criteria, and deployment considerations
• Demonstrate clear interdisciplinary communication by preparing a concise written report and oral presentation that convey technical findings, ethical assessments, and stakeholder-aligned recommendations