Taught by Elizabeth Bondi-Kelly , Assistant Professor of Electrical Engineering and Computer Science
Courses in this series
1. Defining AI for Social Impact
An introductory course on how AI addresses global challenges. Examines real-world applications spanning wildlife conservation to healthcare access, and how to evaluate the algorithmic, practical, and ethical dimensions of AI deployment.
- Module 1: Explore AI Systems for Social Impact — Core AI concepts and terminology, and a case study on animal classification for conservation (MegaDetector).
- Module 2: Examine AI Systems for Social Impact — How to research and evaluate existing AI systems, with a case study on diabetic retinopathy detection.
Two graded quizzes (50% each); 80% required to pass.
Prerequisites: Interest in real-world AI applications addressing societal challenges.
2. Practical AI for Social Impact
An introductory course on AI algorithms and their application to social challenges. Covers machine learning and reinforcement learning techniques for real-world problems, and the practical realities of deploying AI in communities — data scarcity, bias, and ethical considerations.
- Module 1: Modeling the Real World — Framing social-impact problems as task environments.
- Module 2: Making Predictions and Decisions — Machine learning and reinforcement learning techniques, with case studies on healthcare and public health deployment.
Two graded quizzes (50% each); 80% required to pass.
Prerequisites: Interest in real-world AI applications for social challenges. Some knowledge of machine learning concepts may be useful, but is not required.
3. Participatory AI for Social Impact
Explores ethical and participatory AI development — creating AI with the people interested in and impacted by it, not just for them. Covers how communities are harmed when excluded from AI development, and how to apply participatory design methods to social-impact work.
- Module 1: What is Participatory AI? — The core philosophy of designing with, rather than for, users, plus foundational concepts and case examples.
- Module 2: How to Apply PAI for Social Impact — Applying participatory AI principles in real-world contexts, including challenges and ethical considerations for community engagement.
Two graded quizzes (50% each); 80% required to pass.
Prerequisites: Interest in real-world AI application, especially for social challenges. Some knowledge of ethics in AI may be useful, but not required.