AI for Social Good Applications

This unit explores how AI is applied across humanitarian, environmental, scientific, and societal domains — including disaster response, food and water security, medicine, education, biodiversity, proteomics, and astronomy — while examining ethical risks such as surveillance and the magnification of social harms. It builds on foundational AI concepts introduced earlier and prepares students to design responsible, domain-aware AI solutions in subsequent units.

Learning Objectives

• Analyze case studies of AI use in at least three domains (e.g., natural disaster response, food/water security, medicine, education, biodiversity, proteomics, astronomy) to identify problem statements, data requirements, model approaches, performance metrics, and limitations.
• Apply interdisciplinary reasoning to create a written project plan for an AI intervention that specifies stakeholders, data sources, model types, evaluation criteria, and deployment considerations for a chosen social good scenario.
• Demonstrate the ability to detect and describe potential biases, privacy risks, and unintended harms (including surveillance and social amplification effects) in proposed AI solutions and propose at least two concrete mitigation strategies for each identified risk.
• Evaluate trade-offs between technical performance, ethical constraints, and societal impact by comparing alternative AI approaches for a single domain and recommending a context-sensitive course of action supported by evidence and metrics.