Assistant Professor of Electrical Engineering and Computer Science
Participatory AI for Social Impact
What You'll Learn
- Recognize the importance of including interested and impacted communities in AI design and development
- Describe principles and methods for participatory AI
- Develop strategies for overcoming challenges when working with communities
2 Modules
4 Hours
2 hrs per module (approx.)
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
What You'll Earn
- Certificate of Completion
- Certificates of completion acknowledge knowledge acquired upon completion of a non-credit course or program.
Welcome Message
Welcome to Participatory AI for Social Impact! This is the third course in a 3-part series in Realizing AI for Social Impact from the University of Michigan. In this course, we discuss ethical and participatory AI development—creating AI with people interested in and impacted by AI—for social impact. We hope this course inspires you to start trying to apply AI techniques in your context, with participation!
This abbreviated syllabus description was created with the help of AI tools and reviewed by staff. The full syllabus is available to those who enroll in the course.
Course Schedule
Module 1: What is Participatory AI?
Lesson 1: What is PAI?
- Video: What is Participatory AI?
- Reading: Meet Your Instructor
- Reading: Syllabus
- Discussion Prompt: Would You Let AI Change Your Space?
- App Item: Is this Participatory AI?
- Reading: Pre-Course Survey
Lesson 2: Why PAI?
- Video: Why Participatory AI?
- Reading: Optional: Reverse Machine Translation for African Languages
- Reading: Participatory AI in Practice: Case Examples
- Reading: Create Your PAI Plan Overview
- Reading: Create Your PAI Plan, Part 1
- Ungraded App Item: Submit Your PAI Plan, Part 1
- Graded: Module 1 Quiz
Module 2: How to Apply PAI for Social Impact
Lesson 1: PAI Principles in the Real World
- Video: Overview of Participatory AI Principles
- Reading: Foundational Research on PAI Principles
- Video: Applying Participatory AI in the Real World
- Reading: Create Your PAI Plan, Part 2 Instructions
- Ungraded App Item: Submit Your PAI Plan, Part 2
Lesson 2: Working with Communities
- Video: Challenges in Participatory AI for Social Impact
- Reading: Summary of Challenges in PAI
- Discussion Prompt: When Does Participation Become Difficult?
- Video: Considerations for Working with Communities
- Reading: Summary of Considerations for Working with Communities
- Reading: Examples of Working with Communities
- Reading: Create Your PAI Plan, Part 3 Instructions
- Ungraded App Item: Submit Your PAI Plan, Part 3
- Graded: Module 2 Quiz
- Reading: Acknowledgments
- Reading: Post-Course Survey
Grading Policy
There are two quizzes in this course, each worth 50% of your final grade. Learners must earn an overall grade of 80% or higher in order to pass the course.
Course content developed by U-M faculty and managed by the university. Faculty titles and affiliations are updated periodically.
Beginner Level
Interest in real-world AI application, especially for social challenges. Some knowledge of ethics in AI may be useful, but not required.
