Assistant Professor of Electrical Engineering and Computer Science
Practical AI for Social Impact
About Practical AI for Social Impact
Gain a foundational understanding of key AI algorithms and build valuable skills highly sought after in tech and social impact fields. "Practical AI for Social Impact" introduces you to algorithms, including machine learning and reinforcement learning, with additional topics and resources referenced throughout the course.
Crucially, this course also addresses special challenges in implementing AI for social impact. Throughout the course, we’ll discuss how to navigate data scarcity, bias, and ethical deployment in real-world communities. Case studies will explore potential scenarios with sequential, adaptive decisions, helping you approach new situations with tact and knowledge.
By the end of the course, you’ll have developed skills highly sought after in tech and nonprofit sectors, empowering you to optimize resource distribution and drive systemic change at scale.
This is the second course in the three-course series, "Realizing AI for Social Impact", where you will explore use cases and frameworks for deploying AI to achieve social impact.
Skills You'll Gain
- Artificial Intelligence
- Community Advocacy
- Data Literacy
- Data Management
- Data Quality
- Social Justice
Course content developed by U-M faculty and managed by the university. Faculty titles and affiliations are updated periodically.
Interest in real-world AI application, especially for social challenges. Some knowledge of machine learning concepts may be useful, but not required.
