Assistant Professor of Electrical Engineering and Computer Science
Participatory AI for Social Impact
About Participatory AI for Social Impact
AI is rapidly proliferating across society, but negative impacts can arise when communities and stakeholders are not equally involved in the AI development process. In this course, you’ll learn to critically evaluate AI's societal impact and apply participatory design methods, preparing you to develop ethical and inclusive AI solutions for the future.
"Participatory AI for Social Impact" introduces the core philosophy of designing with rather than for users and other stakeholders. Motivated by analyzing case studies where AI for social impact did not achieve the desired goals in the real world, you will learn to identify participatory AI practices and principles and create your own participatory AI plan. We will also discuss the logistical and ethical nuances of implementing participatory AI for social impact, including working with communities. This course will provide a practical toolkit for developing ethical, inclusive AI solutions that prioritize collaboration and positive social impact.
This is the third 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 Ethics
- 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 ethics in AI may be useful, but not required.
