This unit examines real-world AI-for-good initiatives (including Microsoft’s AI for Good Lab), the roles of collaborators and stakeholders, and the common measures used to evaluate societal impact in health contexts. Students will analyze challenges, ethical and data stewardship considerations, and prepare to design responsible AI health solutions in subsequent units.
Learning Objectives
- Analyze the structure, goals, outcomes, and lessons learned from the Microsoft’s AI for Good Lab case study and similar initiatives to identify success factors and limitations for health-related projects
- Evaluate potential collaborators and stakeholder roles for AI-for-good health projects and justify partnership strategies that promote equity, scalability, and sustainability
- Apply commonly used quantitative and qualitative measures (e.g., accuracy, sensitivity/specificity, equity indicators, population health outcomes) to assess and compare the projected societal impact of proposed AI interventions
- Demonstrate responsible data stewardship by identifying appropriate data sources, articulating privacy and governance considerations, and proposing mitigation strategies for bias, data quality, and technical challenges
Curriculum
- 1 Section
- 9 Lessons
- 30 hours
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- Unit 2 — AI for Good: Collaboration and Metrics9
- 1.1Microsoft’s AI for Good Lab20 mins
- 1.2Collaborators20 mins
- 1.3What If Artificial Intelligence Were Used to Improve Societal Good?20 mins
- 1.4The Challenges and Lessons Learned from Using Artificial Intelligence20 mins
- 1.5Commonly Used Measures20 mins
- 1.6Why Is This Important?20 mins
- 1.7Why Is This Important?20 mins
- 1.8Why Is This Important?20 mins
- 1.9Data20 mins
Instructor
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