AI for Good: Collaboration, Metrics, Data

This unit examines real-world AI for Good initiatives (including Microsoft’s AI for Good Lab) to identify key collaborators, stakeholder roles, common challenges, and lessons learned. Students learn how to choose and apply measures of social impact and to assess data needs, governance, and ethical considerations to prepare for designing responsible AI solutions.

Learning Objectives

• Analyze case studies (e.g., Microsoft’s AI for Good Lab) to identify collaborators, stakeholder roles, and partnership strategies in AI for Good projects
• Evaluate technical, ethical, and operational challenges and lessons learned from deploying AI for societal benefit, and propose mitigation strategies
• Apply commonly used quantitative and qualitative measures (e.g., accuracy, fairness metrics, impact indicators) to assess potential benefits and harms for a given AI for Good use case
• Demonstrate appropriate data considerations—data sources, quality, governance, privacy, and documentation—required to responsibly design and evaluate AI for Good solutions