Course Design and Outline
STOR 323I (Fall 2026)
Machine Learning: Ethics and Society
Course overview
This course examines the ethical, social, and technical challenges arising from modern machine learning (ML) and artificial intelligence (AI) systems. It connects technical foundations—including supervised learning, online learning, multi-agent modeling, and game theory—with philosophical frameworks such as consequentialism, deontology, and virtue ethics.
The course emphasizes the tension between algorithmic decisions and societal impact, training students to critically evaluate and design responsible ML systems.
Course website
Visit the Fall 2026 course website for current materials, discussions, and course information. This page summarizes the course’s learning goals and design.
Learning goals
- Analyze ethical dilemmas in ML and AI systems.
- Understand technical tools used to address these issues.
- Evaluate real-world deployments and their societal consequences.
- Communicate technical and ethical arguments clearly.
Course structure
1. Moral pitfalls of machine learning
- Introduction to moral philosophy
- Foundations of machine learning
- Bias, fairness, privacy, and accountability
- Online learning and feedback loops
2. Social and economic implications
- Human factors in ML systems
- Game theory and strategic behavior
- Social choice, equilibrium, and mechanism design
- Platforms and recommender systems
3. Generative AI: challenges and opportunities
- Generative AI and large language models
- Aligning AI systems with human values
- Ethical and societal implications of generative AI
Course design
This is an interactive, discussion-driven course. Students should expect structured debates, case studies, technical exercises, and a team project addressing a contemporary issue in responsible machine learning.
Tools and materials
- Programming: Python
- Writing: LaTeX (Overleaf recommended)
- Materials: Open-source readings, lecture slides, and notes
Course materials and discussions are available through the Fall 2026 course website; some materials may require course access. Submissions and grades are provided through Canvas.
Policies and evaluation
For the current schedule, evaluation details, and course policies, consult the course website and Canvas.