Assistant Professor of Electrical Engineering and Computer Science
Defining AI for Social Impact
What You'll Learn
- Learn critical areas where AI can serve as a powerful tool for social impact
- Identify algorithmic, practical, and ethical considerations when developing AI for social impact
- Recognize bias, fairness, and misinformation in AI
- Identify reputable source material, ensuring solutions are trustworthy
2 Modules
4 Hours
2 hrs per module (approx.)
About Defining AI for Social Impact
Artificial Intelligence (AI) is transforming how the world addresses some of its toughest challenges. In this course, "Defining AI for Social Impact", you’ll learn how AI is being applied in unique ways, from protecting endangered wildlife across the globe to expanding healthcare access in communities with limited resources. Through real-world case studies and faculty-led videos, you’ll gain a personal understanding of how AI can create positive social impact when applied thoughtfully and responsibly.
You’ll explore both the potential and limitations of AI, learning to recognize the algorithmic, practical, and ethical considerations that ideally guide its use. This course goes beyond theory by connecting abstract ideas to concrete examples of existing AI projects in society. By the end, you’ll understand how AI works in practice and consider how to leverage it to create a more just and moral future.
This is the first 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
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 Defining AI for Social Impact! This is the first course in a 3-part series in Realizing AI for Social Impact from the University of Michigan. In this course, we discuss AI’s potential for positive social impact with examples, practice identifying and searching for examples, and practical and ethical considerations. Bring your interests and experiences to the videos and activities to explore AI for social impact. We hope this course inspires you to safely try AI in your context!
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: Explore AI Systems for Social Impact
Lesson 1: Introduction to AI
- Video: Welcome to the Series
- App Item: AI to You
- Reading: Meet Your Instructor
- Reading: Course Syllabus
- Video: Overview of AI
- App Item: Is this AI?
- Reading: Textbook and Terminologies
- Reading: Pre-Course Survey
- Video: Case Study: Animal Classification in Conservation
- Reading: Deep Dive: How MegaDetector Powers Wildlife Monitoring
Lesson 2: AI for Social Impact
- Video: What is AI for Social Impact
- Reading: Introduction to AI for Social Impact
- Discussion Prompt: Meet Your Fellow Learners
- Reading: AI Projects in Society
- Reading: Dialogue with Coursera Coach PreRead
- Dialogue: Identify Your AI Use Case
- Graded: Module 1 Quiz
Module 2: Examine AI Systems for Social Impact
Lesson 1: Searching for Existing AI Systems
- Video: Researching Existing AI Systems
- Reading: Resources to Guide Future Searches
- Dialogue: Searching for Related Work
- Video: Paper Reading Walk-Through
- Practice Assignment: Identify Algorithms Used in A Writeup
Lesson 2: Practical and Ethical Considerations
- Video: Ethical Considerations
- Reading: Foundations of Ethics
- Video: Practical Considerations
- Video: Case Study: Detection of Diabetic Retinopathy
- Practice Assignment: Identifying Ethical Considerations
- Reading: Post-Course Survey
- Reading: Keep Learning with Michigan Online!
- Graded: Module 2 Quiz
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
Learners should be interested in AI applications in the real world, especially in the face of social challenges.
